Guest: Adrian Estala, VP, Field Chief Data & AI Officer, Starburst

Host: Shubhangi Dua, Podcast Producer, Host and B2B Tech Journalist, EM360Tech

"AI is replacing BI,” stated Adrian Estala, VP and Field Chief Data & AI Officer at Starburst.

When Shubhangi Dua, host of Don’t Panic, It’s Just Data, put the statement back to Estala, the tension was intentional. In enterprise tech, few systems are as ingrained as business intelligence (BI) dashboards. For two decades, they have been the common language of decision-making – static reports, polished charts, and visuals that meet compliance standards.

However, Estala insists that the change isn't about removing dashboards. It's about staying relevant. “BI isn’t going away,” he explains. “It’s evolving.”

How AI is replacing BI?

A transformation to AI begins with something deceptively simple – a business semantic layer. Instead of forcing executives to understand data through IT-designed schemas, enterprises are creating context-rich data products using business language. A CFO sees finance terms, not table joins. A loans team sees portfolios, not pipelines.

Once this foundation is established, teams can plug the same governed, reusable data product into their business intelligene (BI) tools. This leads to improved performance and consistency rises too.

However, the growth doesn’t stop here; businesses typically ask for more. When a conversational agent is added next to a legacy dashboard, using the same trusted data product, the behaviour changes quickly. Leaders start asking questions in natural language, exploring trends they have never charted before. They make forecasts in seconds and adjust their thinking while on the go.

What was once a static reporting experience transforms into an interactive analytical dialogue. In one major bank, Estala recalls, a CEO challenged himself to avoid opening a dashboard for two weeks. He didn’t need to; the agent managed everything for him.

Also Watch: Are You Scaling Intelligence — or Just Scaling Errors?

Takeaways

• AI is replacing BI, but it's more about evolution than replacement.
• Organisations are moving towards data products for better analytics.
• Engaging business teams early is crucial for successful AI implementation.
• Conversational agents are transforming how teams interact with data.
• Data quality and governance are essential in the transition to AI.
• Business semantic layers help bridge the gap between IT and business needs.
• Organisations can achieve significant impact with AI in a short time.
• Don't wait for perfect architecture; start with a Pathfinder approach.
• Business teams can drive innovation when they understand their data.
• The future of data engagement lies in combining AI with traditional BI tools.

Chapters

• 00:00 The Evolution of BI to AI
• 03:11 Understanding AI's Role in Business Intelligence
• 14:44 Navigating the Transition to AI
• 20:03 Ensuring Data Quality and Governance
• 24:44 The Future of Data Engagement

To learn more about how data products and AI agents are changing enterprise analytics, follow:

• Starburst LinkedIn: @Starburst
• Starburst X: @starburstdata
• Starburst YouTube: @StarburstData
• EM360Tech YouTube: @enterprisemanagement360
• EM360Tech LinkedIn: @EM360Tech
• EM360Tech X: @EM360Tech
• Follow: @EM360Tech on YouTube, LinkedIn and X

Stay connected for more expert insights, podcast episodes, and enterprise data strategy discussions.

#AI #BI #AIvsBI #AIAgents #BusinessIntelligence #DataProducts #EnterpriseAnalytics #DataStrategy #Starburst #DontPanicItsJustData #AdrianEstala #ShubhangiDua #SemanticLayer #CIO #CDO #TechPodcast #DataGovernance #Dashboards

In this episode, they look at the growing excitement around AI and the reality many organisations face when trying to use it. While ambition is high, readiness often falls short. Focusing on procurement, the conversation explores why many AI initiatives struggle to move beyond early stages and what’s needed to turn that ambition into real, measurable value.

Data: The Backbone of AI

Successful AI depends on high-quality, unified data. Fragmented sources, unclean data, and siloed systems make it difficult to build reliable AI applications. As Bensoussan explains: “Fix your data foundation. Without that, you can’t get started with AI. Don’t jump into an AI frenzy hoping it will sort itself out. First, you need a unified transactional and master data model that captures relationships, ensures semantic coherence, and creates a system of truth you can trust.”

A unified data model enables AI to work effectively, increasing both its success rate and depth. Organisations should start with use cases that provide tangible value rather than trying to do everything at once. Governance frameworks, monitoring, and maintenance are critical to ensure reliability, security, and meaningful outcomes. 

Employee trust is another key factor. Users need confidence in AI outputs, and organisations must address scepticism about how AI might impact roles. Building that trust often requires broader cultural change, which can be one of the hardest barriers. Many teams are used to traditional methods and resist adopting new technologies. By combining solid data foundations with practical, focused use cases and a clear strategy, companies can guide teams through this change, ensuring AI initiatives don’t stall and deliver measurable results.

Understanding AI Ambition vs. AI Readiness

Ambition and readiness are not the same. AI ambition refers to the enthusiasm organisations have for integrating AI into operations, driven by the promise of efficiency and insight. AI readiness, on the other hand, measures whether an organisation can actually deploy AI effectively at scale.

According to MIT research, 95 per cent of enterprise AI projects fail to move from proof of concept to production. Bensoussan calls this the “GenAI divide”: “The ambition is there because the promise is incredible, but the readiness is often missing because often the foundation is cracked.”

Without a clear strategy or roadmap, even organisations with abundant resources can struggle to implement AI successfully. Starting with targeted, achievable use cases helps teams gain confidence, build trust, and generate measurable results before scaling more widely.

AI in Procurement

Procurement provides a unique lens for understanding AI adoption. Positioned at the intersection of data, compliance, risk, and finance, it offers significant opportunities but also considerable complexity. One major challenge is that unstructured data like contracts, risk assessments, and supplier communications must be integrated with transactional records, a process that is often time-consuming and difficult. Fragmented systems only add to the challenge, limiting AI’s ability to deliver meaningful, actionable insights.

Bensoussan emphasises that seeing the entire process from supplier discovery to payment is essential. A comprehensive view ensures that AI-driven insights are reliable, actionable, and fully traceable, allowing organisations to understand why specific decisions are made and to make more strategic choices.

AI in procurement is not about replacing humans; it is about augmenting them. By automating mundane tasks like data retrieval and report generation, professionals can focus on higher-value work, strategic thinking, and deeper evaluation. AI also enables richer insights, helping teams develop more effective strategies and make informed decisions. By addressing data challenges, building trust, and starting with targeted use cases, organisations can turn AI ambition into measurable value. With the right preparation and focus, AI can strengthen procurement operations, enhance decision-making, and unlock new levels of efficiency.

For more information, visit www.ivalua.com

Takeaways

• AI ambition vs. readiness in organisations
• Barriers to AI adoption: culture, strategy, data, trust, governance
• Importance of unified data models for AI effectiveness
• Practical AI applications in procurement: sourcing, contracts, invoicing
• Human-AI collaboration and the future of work in procurement

Chapters

00:00 AI Ambition vs. Readiness

05:02 The Procurement Landscape and AI Adoption

09:10 Data Foundations for AI Success

13:03 Unified Data Models in Procurement

16:43 The Human Element in AI Integration

25:57 Real-World Applications of AI Agents

32:22 Key Takeaways for Leaders in AI Adoption

In the latest episode of the Don’t Panic It’s Just Data podcast, EM360Tech’s Trisha Pillay explores this challenge with Chief Technology Officer Paul Brownell and Sergio Morales, Data and AI Engineering Leader from Growth Acceleration Partners. Their discussion unpacks why so many AI initiatives fail to translate into revenue and why the real starting point isn’t the model itself, but the data, governance, and engineering practices that make meaningful outcomes possible.

But here’s the uncomfortable truth and that is many AI strategies look powerful on paper, but the real financial impact is often unclear. This disconnect, called the revenue data gap, highlights an issue many organisations overlook. AI doesn’t create value on its own especially without strong data foundations, governance, and engineering discipline, even the most ambitious AI strategy will struggle to deliver measurable results.

The Revenue Data Gap in Enterprise AI

For many organisations, the excitement surrounding AI can create a tendency to jump straight into experimentation. Teams begin exploring tools, deploying models, or building prototypes without first defining how those initiatives will produce tangible business outcomes.

According to Brownell, this is where the first major disconnect appears. Many enterprises approach AI with what he describes as a “shiny object” mentality. They recognise that AI is powerful, but they have not yet defined where the value will actually come from. As a result, organisations may launch projects that generate interesting insights or technical demonstrations but fail to translate into revenue growth or cost reduction.

Brownell emphasises the importance of establishing a data hypothesis before pursuing any AI initiative. A data hypothesis outlines the relationship between the data an organisation holds and the business value it expects to extract from it. In practical terms, it asks a simple but critical question: If we analyse this data, what decision or action will it enable, and how will that affect revenue?

Without this hypothesis, organisations often find themselves exploring large volumes of data without a clear objective. Some companies may not even know where their most valuable data resides or whether it is reliable enough to support analytical models. Data quality, therefore becomes another major component of the revenue data gap. 

Engineering the Foundations for AI That Delivers Business Impact

While AI is often portrayed as a revolutionary technology, Morales points out that the engineering challenges behind it are not entirely new. Many of the same principles that guided earlier technology transformations such as cloud adoption or microservices architecture still apply to modern AI deployments.

In fact, Morales argues that organisations struggling with AI today are often experiencing the consequences of earlier architectural decisions. Systems built years ago were rarely designed with advanced analytics or AI in mind. As a result, critical data may be trapped inside legacy applications, scattered across departments, or stored in formats that make integration difficult. These limitations become highly visible once organisations attempt to deploy AI at scale.

Another major challenge lies in what Morales describes as the velocity mandate. Businesses increasingly expect technology teams to deliver results quickly, particularly when AI initiatives are positioned as strategic priorities. However, building the infrastructure required for reliable AI systems can take significant time and effort.

Morales explains that organisations do not necessarily need to choose between speed and stability. Instead, they can adopt a pragmatic approach that focuses on incremental progress. This strategy allows organisations to create early successes that build confidence across the business. Once stakeholders see tangible results from initial projects, it becomes easier to secure the support and investment needed for broader data transformation efforts.

Why Data Contracts and Governance Are Critical to AI Success

One of the most practical tools discussed is the concept of data contracts. Though less flashy than AI models, they ensure data flows reliably between systems. At their core, data contracts define a dataset’s structure and expectations; schemas, formats, and validation rules. Morales describes them as a way to embed governance directly into data pipelines, automatically catching violations before they disrupt downstream processes. This prevents silent errors that can skew analytics and decisions. 

Data contracts aren’t a cure-all, though. Their effectiveness relies on clear organisational ownership and communication around each dataset. In large companies, data often comes from multiple systems managed by different teams, each with distinct priorities. Brownell explains that data contracts create a shared framework for collaboration, letting teams integrate and analyse information confidently. Implementation can be gradual: start with critical datasets for a specific use case and expand governance as needed. This iterative approach improves data reliability without requiring a full infrastructure overhaul.

What’s next for AI?

While AI tools continue to evolve, the fundamentals of data management remain unchanged. Organisations must understand their data, govern it effectively, and design infrastructure that allows information to move reliably between systems. Closing the revenue data gap, therefore, requires more than deploying new AI models. It demands a strategic approach that begins with clear business objectives, continues through data engineering practices, and is reinforced by governance frameworks such as data contracts.

If you would like to learn more visit: https://www.growthaccelerationpartners.com/

Chapters

00:00 Introduction to AI Ambitions and Revenue Gaps

02:31 Understanding the Revenue Data Gap

05:47 Challenges of Legacy Architecture in AI

09:11 Closing the Revenue Data Gap

12:29 The Velocity Mandate in AI Implementation

16:42 Strategic and Technical Alignment for AI

18:31 Engineering Considerations for AI Initiatives

22:03 The Role of Data Contracts in AI Success

28:55 Practical Takeaways for AI Implementation

Takeaways

• The revenue data gap is a common challenge that organisations face when implementing AI.
• It’s crucial to define a clear data hypothesis and ensure data quality to drive measurable business impact.
• Data contracts work only if teams know who owns datasets, how to maintain them, and how changes are communicated.
• Balancing the velocity mandate with governance is key. 
• Engaging stakeholders and mapping the value chain ensures that AI initiatives are aligned with business needs, ultimately leading to revenue growth.

The Trust Paradox

The report exposes a striking disconnect. Turajski points out that while around 65 per cent of data leaders believe employees trust the data powering AI, 75 per cent say upskilling in data and AI literacy is essential. In other words, confidence is high, but readiness is lagging.

This is the trust paradox where employees increasingly rely on AI outputs, while data leaders remain cautious about the quality, governance, and lineage behind those results. The risk is not scepticism but rather overconfidence. When AI-generated answers are accepted without scrutiny, flawed data can quietly scale poor decisions. For CDOs, the challenge is cultural as much as technical.

AI Adoption Soars While Data Readiness Lags

The harsh reality is that AI experimentation is no longer confined to innovation teams. It’s spreading across marketing, operations, finance, and customer experience. As a result, scaling from pilot to production requires more than a model and a use case. To make AI work at scale, organisations need a data strategy that ensures consistency across domains, clear and transparent governance, measurable business impact, and sustainable management of their data assets.

Data Quality and Governance

Turajski explains that organisations are increasingly investing in data management and governance, with 86 per cent expanding data initiatives and 39 per cent prioritising upskilling. Metadata integration also helps unify distributed environments, providing the context AI needs to deliver reliable, trustworthy outputs. 

 Organisations need to remember that AI systems amplify whatever they are given, so if inputs are inconsistent, incomplete, or poorly defined, outputs will reflect those weaknesses which are often at scale. Data quality challenges frequently arise from duplicated or conflicting records, inconsistent definitions across business units, poor lineage visibility, and limited ownership accountability. 

For example, a retailer might describe the same product in multiple ways across systems. Without standardisation, AI tools trained on that data produce fragmented insights, and when this occurs across thousands of products and regions, the distortions multiply. The takeaway from data leaders is clear: AI performance cannot be separated from disciplined, high-quality data management.

Upskilling and Scaling AI Adoption

Both Petrie and Turajski stress that technology alone won’t close the gap. Upskilling employees in data literacy, AI fluency, and governance awareness ensures AI experimentation evolves into measurable, real-world results from improved customer experience to faster, more accurate analytics. The 2026 CDO Insights findings position data leaders at the centre of AI transformation. Their mandate extends beyond infrastructure to trust architecture. The trust paradox isn’t a reason to slow down innovation. It’s a reminder that lasting results require as much discipline as ambition. In 2026, the organisations that succeed won’t be the fastest to adopt new technologies, but those that build the most reliable data foundations to support them.

To learn more about this, visit informatica.com

Takeaways

• The trust paradox highlights a disconnect between employee confidence in AI and leadership's caution.
• Data leaders recognise the need for upskilling in data and AI literacy.
• Building a trusted context is essential for effective AI adoption.
• The vendor landscape for data management is complex and requires careful navigation.
• AI is being used to enhance customer experience and loyalty.
• Measurable results from AI adoption are becoming a priority for organisations.
• Data governance must keep pace with AI use to mitigate risks.
• Successful organisations are leveraging unified data management platforms to drive AI value.

Chapters

00:00 Introduction to the CDO Insights Report

03:13 Understanding the Trust Paradox in AI Adoption

08:34 Building Trusted Context for AI

14:11 The Importance of Data Quality and Completeness

20:28 Navigating the Vendor Landscape for Data Management

23:09 From Experimentation to Measurable Results

27:38 Recommendations for CDOs and CISOs

In this episode of the Don’t Panic, It’s Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sits down with Herb Blecher, Research Director of Data and Analytics at Enterprise Management Associates (EMA).

This conversation challenges a common belief in enterprise tech – that gathering everything ensures insight. Blecher, alluding to the modern-day AI craze, cautions the enterprise audience that just because you can access vast amounts of unstructured data doesn’t mean you should.

What is the AI Gold Rush & Why It’s Risky?

Unstructured data now fills the enterprise tech space — voice calls, financial documents, customer chats, images, logs, and emails. “With AI and machine learning, we’ve finally figured out how to access and organise it.”

However, Blecher offers a stark reality check. AI doesn’t just increase insight; it increases error. When machines transition from calculating numbers to interpreting tone, images, and incomplete context, the chances for mistakes rise significantly. A blurry comma in a financial document, a misread abbreviation, a misplaced decimal. In low-stakes situations, this is inconvenient. In finance or healthcare, it can be disastrous.

The danger lies not just in faulty outputs, but in confidently flawed outputs. AI doesn’t hesitate as humans do. It doesn’t say, “This seems off.” It fills in gaps, often convincingly. That confidence, Blecher argues, makes governance essential.

The real issue companies face isn’t a lack of data; it’s a lack of careful thought.

Also Read: AI is Making “As-Code” Inevitable

Why Human-in-the-Loop is Imperative?

Governance over hype is the key takeaway from the conversation. AI generating and using data at the same time creates a new situation. In the past, including financial troubles that Blecher experienced directly, human judgment acted as the final protection. Now, companies risk losing that safeguard in their rush to automate.

Dua puts it simply – humans are leaders; AI is the helper.

The enterprises that succeed with unstructured data aren’t the fastest; they are the most thoughtful. They clearly define their questions first, build feedback loops, monitor continuously, and foster a culture of scepticism.

What are the failures? They often look like ambitious automation without safeguards—from flawed document scanning to high-profile AI rollouts like McDonald's testing automated drive-through ordering, where conversational nuance proved more challenging than anticipated.

Tone, ambiguity, and context remain distinguishing human areas.

What Happens Five Years From Now?

Will AI solve data quality issues? No, it will not. However, Blecher believes that data quality problems are here to stay. “What will change is the range of questions we try to answer. As AI develops, companies won’t stop dealing with edge cases; they’ll broaden the edge.”

The future doesn’t promise easy automation. It promises increased capability, increased capacity, along with increased responsibility.

For CFOs and IT leaders investing in AI-driven data strategies, EMA’s Research Director of Data and Analytics has a final message:

1. Don’t confuse volume with value.
2. Don’t replace governance with optimism.
3. Don’t give up scepticism in a gold rush.

AI’s potential is huge. But more data doesn’t always mean better data. In a world eager to gather everything, restraint could be the most radical strategy of all.

Key Takeaways

1. More data doesn’t guarantee better insights — clarity of purpose matters more than volume.
2. AI doesn’t just scale intelligence; it scales errors if governance is weak.
3. Unstructured data is powerful, but without context and oversight, it becomes a liability.
4. Human judgment remains essential — especially in high-stakes domains like finance and healthcare.
5. The most successful organisations move deliberately, not impulsively, in the AI gold rush.

Chapters

1. 00:00 Introduction to Data Quality and Its Importance
2. 02:43 The Rise of Unstructured Data
3. 05:42 Challenges in Ensuring Data Quality
4. 08:46 AI's Role in Data Quality Management
5. 11:30 Human Oversight in AI and Data Quality
6. 14:47 Opportunities in Data Quality
7. 17:32 Governance and Regulation in AI
8. 20:25 Real-World Applications and Case Studies
9. 23:27 Future of Data Quality and AI
10. 26:18 Key Takeaways for Leaders

About Herb Blecher

Herb leads EMA's Data and Analytics practice. He brings more than two decades of experience building solutions across financial services, data product development, and enterprise analytics.

His perspective is shaped by leading national data initiatives for U.S. mortgage servicers and government agencies, as well as driving product innovation and strategy in fast-moving technology environments.

Herb's research spans enterprise data and analytics, including data architecture and platform modernisation, analytics and integration, governance, and AI/ML platforms.

#AI #DataAnalytics #TechPodcast #B2BTech #DataQuality #UnstructuredData #AIGoldRush #HumanInTheLoop #AICorporate #HerbBlecher #EMAPartners #CFOs #ITLeaders #DataStrategy #DontPanicItsJustData #EM360Tech #PodcastClips #DataInsights

In the recent episode of the Don’t Panic, It’s Just Data podcast, McCardel joined host Kevin Petrie, VP Research and Head of Data Management at BARC, to talk about why access alone has never been enough. He also discussed how artificial intelligence (AI) is forcing the analytics community to rethink the purpose of data.

The conversation dives into a familiar issue: how can organisations empower non-technical users without compromising data trust or overwhelming the technical teams responsible for it?

“We’ve spent a decade pretending the problem was solved by self-service,” McCardel says. “But what we actually did was move complexity around instead of removing it.”

As AI becomes part of analytics platforms, that complexity is finally being addressed. This includes long-standing beliefs about roles, ownership, and teamwork.

Addressing the Myth of Data Democratisation

Tracing many of the analytics issues faced by organisations in the present day, McCardel alludes to the early self-service BI, which promised that business users could explore data on their own. This was supposed to allow analysts and engineers to focus on more important tasks. In reality, the outcome often included duplicated logic, inconsistent metrics, and a widening trust gap between teams.

“Access without context is chaos,” McCardel tells Petrie. “If everyone can answer questions, but everyone answers them differently, you haven’t democratized anything; you’ve just created noise.”

This issue has grown more urgent as organisations expand. Different roles—data engineers, analysts, data scientists, and business stakeholders—approach data with distinct goals and skills. Traditional tools forced everyone into the same interfaces, often designed for one group while ignoring the needs of the others.

Petrie notes that many companies responded by adding layers of control, but this approach had drawbacks. Stricter guidelines slowed insight generation and pushed business users back into reliance on centralised teams.

McCardel argues that the main problem isn’t a lack of governance or tools but a lack of shared understanding. “We’ve treated analytics like a handoff,” he explains. “The data team builds it, the business consumes it. That model doesn’t work when questions are fluid, and decisions are continuous.”

He believes AI is revealing the limits of that model and providing a path forward.

Also Watch: “Data Teams Suffer from Fragmentation” | Charles Schaefer @ Big Data LDN 2025

AI is the Bridge, Not the Shortcut

While much of the industry conversation about AI in analytics focuses on automation and natural language querying, the CEO of Hex is cautious about viewing AI as a quick fix. “If AI just gives you faster wrong answers, that’s not progress,” he points out.

Instead, he presents AI as a bridge that helps different roles collaborate in the same analytical space without flattening their expertise. In this view, AI helps translate: it turns business questions into structured analysis, brings relevant context to the surface, and makes assumptions clear instead of hidden in code.

This is where McCardel sees platforms like Hex playing an important role. Instead of separating technical and non-technical users into different tools, Hex is designed to support collaboration within a single environment. Analysts can create rigorous, transparent logic, while business users can interact with the results, ask follow-up questions, and understand how conclusions were made.

“The goal isn’t to turn everyone into a data scientist,” McCardel clarifies. “It’s to let each person contribute at their level without breaking the chain of trust.”

Trust, he stresses, is essential in modern analytics. As more insights come from AI, organisations will need clear lineage, better validation, and shared visibility into how answers are created. Black-box analytics may be quick, but they are also fragile.

“We’re moving away from the idea that insight is a product you deliver,” McMardel added. “It’s a conversation you participate in.”

As AI changes analytics workflows, the challenge for organisations won’t be just adopting the technology. It will redesign how people collaborate around data. The co-founder of Hex suggests that democratisation was never about removing experts from the process. It was about making expertise visible, accessible, and usable.

And that, finally, may be something worth not panicking about.

Takeaways

1. AI is reshaping the future of data analytics.
2. Data democratisation remains a significant challenge for organisations.
3. Trustworthiness in data outputs is crucial for effective decision-making.
4. Integration of different user personas is essential for collaboration.
5. Organisations can start using analytics tools without perfect data.
6. Expert users can help build trust in data analytics.
7. Natural language interfaces are key to making data accessible.
8. The role of AI in data exploration is becoming increasingly important.
9. Data quality and governance are critical for successful analytics.
10. Successful AI adoption requires a step-by-step approach.

Chapters

1. 00:00 Introduction to AI and Data Analytics
2. 02:54 The Genesis of Hex Technologies
3. 06:04 Challenges in Data Democratisation
4. 09:10 AI's Role in Data Exploration
5. 12:14 Trust and Context in Data Analytics
6. 15:00 The Evolution of Analytics Tools
7. 18:10 Integrating Different User Personas
8. 21:09 The Importance of Contextual Understanding
9. 23:52 Data Preparation and Governance Challenges
10. 26:46 Incremental Adoption of AI in Organizations
11. 29:57 The Human Element in AI Adoption
12. 32:47 Conclusion and Next Steps for Leaders

#DataDemocratisation #AIinAnalytics #SelfServiceAnalytics #FutureofData #DataStrategy #BusinessIntelligence #DataGovernance #DataTrust #NaturalLanguageQuery #EnterpriseAnalytics #HexTechnologies #BarryMcCardel #DontPanicItsJustData #KevinPetrie #BARC #CIO #ITLeaders #DataTeams #DataAnalysts #DataScientists #BusinessStakeholders #DataDemocratization #AIforDataTeams #analytics_tool #datastrategy #RethinkAnalyticsStrategy #blackbox #DataFragmentation

Hosted by Dana Gardner, Principal Analyst at Interarbor Solutions, the discussion brings together Jürgen Obermann, Senior GTM Leader EMEA and Piotr Kobziakowski, Senior Principal Solutions Architect from Vespa.ai. Rather than focusing on hype, the conversation centres on the everyday realities of modern e-commerce systems and why progress often feels harder than it should.

When AI Meets Legacy Digital Commerce

AI introduces new expectations around speed, relevance, and adaptability. As a result, many digital commerce platforms are built on foundations designed for a different era. Years of development have resulted in fragmented environments, often based on microservices that once provided flexibility but now introduce complexity.

As Jürgen explains, even small changes can trigger long delivery cycles. Engineering teams may need months to safely update systems, not because the ideas are difficult, but because the infrastructure has become fragile.

Search and Personalisation Are Still Disconnected

Search is where most e-commerce journeys begin, yet many platforms still rely on keyword-focused approaches that struggle to interpret intent. Customers expect results that reflect who they are, what they want, and why they’re searching. Delivering meaningful personalisation requires systems that combine signals, context, and ranking logic in real time. Without that, experiences remain generic even when data is available.

Architecture Becomes the Bottleneck

The conversation then turns to architecture. Traditional search stacks, particularly Lucene-based systems, often hit performance limits when vector operations and advanced ranking are introduced. These capabilities tend to be bolted on rather than designed into the core. Piotr highlights a deeper issue, which is fragmentation. Search, ranking, recommendation, feature stores, and inference engines often live in separate systems. Each integration adds latency, duplicates data, and slows innovation.

A More Grounded Path Forward

This episode of Don’t Panic, It’s Just Data offers a calm, practical view of AI in digital commerce. Progress comes not from adding more complexity, but from simplifying how systems work together. When search, personalisation, and recommendation are designed as part of a cohesive whole, digital commerce platforms become easier to evolve and better equipped to serve both customers and the business.

For more insights into modern search architectures and AI-native commerce platforms, visit Vespa.ai.

Takeaways

• Many teams see the potential of AI, but face practical blockers.
• E-commerce companies struggle with operational, customer experience, and business challenges.
• AI technologies enable sophisticated personalised search experiences.
• Architectural bottlenecks often hinder e-commerce systems' performance.
• AI-native architectures can significantly improve search capabilities.
• Real-time personalisation is crucial for enhancing user experience.
• Separate systems for search and recommendations create inefficiencies.
• Phased migration is essential for transitioning from legacy systems.
• AI's impact on revenue can be profound when implemented effectively.
• Vespa is a comprehensive platform that integrates various functionalities.

Chapters

00:00 Introduction to AI-Driven Search in E-Commerce

01:38 Challenges in Adopting AI for Digital Commerce

04:02 Architectural Bottlenecks in E-Commerce Systems

07:39 Designing AI-Native Search Architectures

12:00 Advancements in Personalisation for E-Commerce

16:21 Inefficiencies of Separate Search and Vector Systems

19:24 Phased Migration to AI-Native Platforms

21:51 Business Implications of AI in Search

23:57 Advice for Technical Leaders in E-Commerce

About Vespa.ai

Vespa.ai is an AI search platform designed for building and operating large-scale, real-time applications. It brings together big data processing, vector search, machine-learned ranking, and real-time inference within a single system, enabling teams to deliver intelligent search, recommendation, and retrieval-augmented generation (RAG) at enterprise scale.

With native tensor support, Vespa allows complex ranking and decision logic to run directly in production, rather than being bolted on as separate services. This architecture reduces latency, simplifies system design, and makes it easier to evolve AI-driven applications as data, models, and business needs change.

They get specific about how modern finance leaders move faster: start with one governed source of truth, then layer automation, and only then AI. They explore how the CFO role is evolving. From reporting numbers to also owning the non-financial “whys” behind them.

In the age of the AI boom, that shift turns every CFO into a product owner of data. But as Pavel Doležal puts it, without a clean, connected foundation, AI is just noise.

According to Vineta Bajaj, Group CFO of Holland & Barrett, the role of the CFO has fundamentally changed. Today’s CFO must act as a product owner for data, not just owning the numbers but also determining how data is defined, structured, and used throughout the business.

Finance and Data: A Complete Product

Drawing on her experience with Ocado Group, Rohlik Group (one of the fastest online grocery businesses in the world), and now Holland & Barrett, Bajaj points out that financial problems remain persistent across organisations.

Issues such as slow month-end closes, duplicated processes, delayed reporting, and limited decision-making speed are still common. These challenges are even greater in complex businesses that operate across multiple entities and countries. Differing charts of accounts, outsourced finance teams, and fragmented systems create added friction.

Bajaj stresses the answer isn’t "add another tool". CFOs should treat finance and data as a complete product, one that serves the business as its customer. This requires understanding finance processes, clearly defining financial and non-financial data, and prioritising what has the greatest impact on the business.

The Holland & Barrett CFO further emphasises that CFOs cannot pass this responsibility off to IT or BI teams. When data ownership is outside finance, it becomes someone else’s problem. However, when finance takes ownership of master data and its definitions while working closely with commercial and operational teams, it creates a single source of truth that the entire organisation can trust.

Also Watch: The Real Future of Data Isn’t AI — It’s Contextual Automation

How to Build the Foundation for Real-Time Financial Intelligence & AI

Analytics, automation, and AI only work if the foundations are solid. Before adding AI assistants or real-time dashboards, CFOs must ensure that finance processes are clean, standardised, and automated. Poorly coded purchase orders, late journal entries, and inconsistent definitions can undermine even the most advanced technology.

At Holland & Barrett, this perspective led Bajaj to create a dedicated data function within finance. It ensures accountability for master data, definitions, and governance. The aim is not just to speed up reporting, but to gain deeper insights by linking financial outcomes with non-financial factors such as foot traffic, pricing, customer behaviour, and external influences like weather.

This integrated viewpoint allows finance teams to go beyond explaining variances and focus on the key business question: why performance changed and what happens next. It also opens up self-service analytics, reducing reliance on central BI teams and enabling decision-makers to act in real time.

Bajaj views AI as a powerful tool but not a shortcut. It prompts organisations to quickly address long-standing data and process issues. When data is well-defined and trusted, AI can facilitate scenario modelling, forecasting, and faster decision-making. Without proper discipline, AI merely adds to the confusion.

Ultimately, the future CFO must take an active role. They should engage with data, map out processes, ask difficult questions, and create a clear plan. Those who do will move faster than traditional finance models allow and help their organisations thrive in an AI-driven future.

Key Takeaways

1. The CFO’s role is evolving from reporter to product owner of data.
2. Slow month-end and fragmented processes block fast decision-making.
3. Finance must own data definitions to create a single source of truth.
4. Financial and non-financial data must be connected to explain the “why.”
5. AI only delivers value when financial data and processes are already clean.

Chapters

1. 00:00 Introduction: The Modern CFO and Data
2. 01:05 What is the New Role of the CFO?
3. 03:34 The 3 Biggest Problems in Finance (Month-End, Reporting, Decisions)
4. 06:21 Why Every CFO is a Product Owner of Data
5. 09:37 Data Ownership: Should Master Data Sit in Finance or IT?
6. 13:30 The 3 Steps to Unleash Data Power (Process, Standardisation, Data Lake)
7. 17:08 How AI is Forcing Speed and Change in Finance
8. 20:25 The Future: Keboola's AI Assistant Roadmap
9. 21:36 Wrap-up and Final Thoughts

In this episode of Don’t Panic, It’s Just Data podcast, Amy Horowitz, Group Vice President of Solution Specialist Sales and Business Development at Informatica, joins moderator Kevin Petrie, VP of Research at BARC, to tackle one of the most pressing topics in enterprise technology today: the role of trusted data in driving responsible AI. Their discussion goes beyond buzzwords to focus on actionable insights for organisations aiming to scale AI with confidence.

Why Responsible AI Begins with Data

Amy opens the conversation with a simple but powerful observation: “No longer is it okay to just have okay data.” This sets the stage for understanding that AI’s potential is only as strong as the data that feeds it. Responsible AI isn’t just about implementing the latest algorithms; it’s about embedding ethical and governance principles into every stage of AI development, starting with data quality.

Kevin and Amy emphasise that organisations must look at data not as a byproduct, but as a foundational asset. Without reliable, well-governed data, even the most advanced AI initiatives risk delivering inaccurate, biased, or ineffective outcomes.

Defining Responsible AI and Data Governance

Responsible AI is more than compliance or policy checkboxes. As Amy explains, it is a framework of principles that guide the design, development, deployment, and use of AI. At its core, it is about building trust, ensuring AI systems empower organisations and stakeholders while minimising unintended consequences. Responsible data governance is the practical arm of responsible AI. It involves establishing policies, controls, and processes to ensure that data is accurate, complete, consistent, and auditable.

Prioritise Data for Responsible AI

The takeaway from this episode is clear and that is responsible AI starts with responsible data. For organisations looking to harness AI effectively:

1. Invest in data quality and governance — it is the foundation of all AI initiatives.
2. Embed ethical and legal principles in every stage of AI development.
3. Enable collaboration across teams to ensure transparency, accountability, and usability.
4. Start small, prove value, and scale — responsible AI is built step by step.

Amy Horowitz’s insight resonates beyond the tech team: “Everyone’s ready for AI — except their data.” It’s a reminder that AI success begins not with the algorithms, but with the trustworthiness and governance of the data powering them.

For more insights, visit Informatica.

Takeaways

• AI is only as good as its data inputs.
• Data quality has become the number one obstacle to AI success.
• Organisations must start small and find use cases for data governance.
• Hallucinations in AI models highlight the need for vigilant data oversight.
• Reputational damage from AI failures can be severe for organisations.
• Metadata plays a crucial role in data management and governance.
• Collaboration between data, AI, and development teams is essential.
• Data governance is a must-have, not a nice-to-have.
• Organisations need to enable their lines of business for effective AI implementation.
• Everyone is ready for AI, except for the quality of their data.

Chapters

00:00 The Importance of Responsible AI and Trusted Data

02:49 Defining Responsible AI and Data Governance

05:40 Challenges in Data Quality and Governance

08:51 Real-World Examples of Data Quality Issues

11:51 The Role of Employees in Data Governance

14:41 Successful AI Outcomes Through Responsible Data Practices

17:42 The Risks of AI Governance and Reputational Damage

20:42 Collaboration Across Data, AI, and Development Teams

23:34 The Future of Metadata and Data Management

26:42 Key Takeaways for Data and AI Leaders

About Informatica

Informatica, founded in 1993, is an enterprise data management company headquartered in Redwood City, California. The company provides software products for data integration, data quality, master data management, and data governance. With approximately 9,000 global customers across various industries, Informatica has positioned itself as a significant player in the data management market.

Too often, we drive all our energy into tools, processes, and outputs, but forget to ask ourselves how what we build actually makes a difference. For enterprises, this means looking beyond AI models and dashboards to see how our data drives real, measurable impact. Understanding the difference between output and outcome is what separates activity from transformation.

In this episode of Don’t Panic, it's Just Data, host Doug Laney and Nadiem von Heydebrand, CEO and Co-founder of Mindfuel, explore how organisations can turn data and AI efforts into actionable business outcomes. They discuss the concept of the “value layer”, a framework connecting data initiatives to business needs, emphasising the importance of understanding business problems before developing solutions.

Nadiem stresses that prioritising initiatives and fostering strong collaboration between business and data teams are critical to unlocking maximum value from data and AI efforts.

Why Data and AI Impact Management Matters

Many organisations are investing heavily in data and AI, but turning these investments into real business value remains a challenge. This is because a critical gap exists between technical execution and business outcomes. Data and AI teams work on initiatives without first clarifying what business problems they're solving or how success will be measured.

Data and AI Impact Management bridges this gap by establishing the “value layer" between business strategy and technical platforms. This approach starts with structured demand management for use cases, enables systematic prioritisation based on actual value potential, and tracks initiatives throughout their lifecycle to ensure they deliver impact against business goals. This shift, from building solutions in search of problems to solving qualified business problems with purpose-built solutions, transforms data and AI teams from technical support functions into strategic partners who deliver value, stronger strategic alignment, and lasting competitive advantage. 

Nadiem says, “Applying a product mindset within data initiatives is key, and it's the foundational effort to be able to drive value.” 

He also notes that not every use case delivers direct financial impact, and the value layer helps clarify demand, manage use cases effectively, and uncover each initiative’s business value

For more insights and solutions, visit Mindfuel

Takeaways

• Organisations struggle to connect data initiatives to business outcomes.
• The value layer is essential for linking data to business demands.
• Understanding the actual business problem is crucial for success.
• Value management encompasses the entire lifecycle of initiatives.
• A product mindset helps focus on outcomes rather than outputs.
• Not all data use cases have direct dollar values.
• Data and AI impact management creates transparency for data teams.
• Establishing a product mindset is key for data products.
• Connecting processes to the operating model enhances effectiveness.
• Collaboration between business and data teams is vital for unlocking value.

Chapters

00:31 Introduction: Don't Panic, It's Just Data

01:37 The Missing Piece: Introducing the Value Layer 

07:11 Value Management Lifecycle

10:46 Product Mindset in Data Initiatives

14:10 Distinguishing Value and Impact 

17:04 Impact Management and Investment Justification 

19:34 Mindfuel's Three-Step Guide to Impact Management 

21:00 Conclusion and Key Takeaways

About Mindfuel

Mindfuel is a data and AI impact management platform that gives data, analytics and AI teams a single source of truth to prioritise high-impact use cases, connect initiatives to business outcomes, and demonstrate ROAI. It replaces scattered tools and reactive, manual processes with a structured approach to managing use cases and data and AI products. This enables organisations to reduce business case bias, eliminate inefficiencies, and clearly communicate the value of AI initiatives, driving enterprise-wide trust, transparency, and impact. 

AI can only deliver if your data is truly ready, but most enterprises are drowning in fragmented, incomplete, or slow-to-update data. In this episode of Don't Panic, It's Just Data, host Doug Laney and Sushant Rai, Vice President of Product of AI and Data Strategy at Relito, explore how modern data unification strategies are changing enterprises, enabling AI to deliver faster, more reliable insights. They focus on the shift from traditional Master Data Management (MDM) to next-generation AI-ready data cores, uncovering the risks of fragmented data and the strategies to overcome them.

Why AI-Ready Data Matters

AI, especially large language models (LLMs), is changing how people interact with data. Analysts, executives, and frontline teams now expect natural language queries and instant, actionable insights.

Sushant explains:

"AI performs at its best when it has full context, empowered with the right data. This allows AI agents to make decisions and take actions on behalf of your business."

When you embed intelligence into your data layer, AI can help you manage and scale your data without drowning your teams in manual work. This will only work if your data is structured, clean, governed, and constantly updated, everything that makes it truly AI-ready.

The Data Scale Challenge

The volume of data being turned over daily is staggering. 

As Sushant notes:

"The amount of data getting generated every single day is so massive that there’s no way to keep up without AI. Even the largest organizations, with massive data stewardship teams, can’t catch up manually."

This gap is driving the change in the modern data platforms, where AI automates stewardship, enriches data continuously, detects anomalies, and maintains quality in real time.

Want to learn more about modern data unification and AI-ready platforms? Visit Reltio.com for insights, resources, and case studies.

Takeaways

• Data unification provides a trusted, real-time view of key business elements.
• Organizations must balance speed and trust in data management.
• Classic MDM is evolving into modern data unification platforms.
• Real-time data access is crucial for AI and analytics.
• AI can enhance data quality and governance processes.
• Successful data initiatives require clear business outcomes and ownership.
• Data unification should be viewed as a business platform, not just an IT project.
• AI agents will play a significant role in automating data governance.
• Organizations need to focus on both structured and unstructured data.
• The future of data management involves continuous unification and enrichment of data.

Chapters

00:00 Introduction to Data Unification and AI

07:52 The Importance of Data Unification in Enterprises

15:44 AI and Data Quality Management

23:20 Organizational Success Factors for Data Initiatives

25:16 Future Trends in Data and AI

About Reltio

At Reltio, we believe data should fuel your success in the enterprise AI era. Reltio Data Cloud™ is the agentic data fabric for the enterprise—powering real-time data intelligence and AI transformation. Reltio’s cloud-native SaaS platform delivers unified, trusted, and context-rich data across domains in real time. With Reltio, organizations gain 360-degree views of customers, products, suppliers, and more—mobilized in milliseconds to any application, user, or AI agent. Trusted by the world’s largest enterprises across life sciences, financial services, healthcare, technology, and more, we help organizations fuel frictionless operations, drive innovation, and reduce risk.

In the latest episode of Tech Transformed, host Kevin Petrie, VP of Research at BARC, speaks with Brett Roscoe, Senior Vice President and GM of Cloud Data Governance and Cloud Ops at Informatica, about the evolving role of CDOs. Their conversation explores how CDOs are transitioning from data stewards to strategic leaders, the importance of data governance, and the challenges of managing unstructured data.

The Role of the CDO in the Agentic Era

As Roscoe notes, “CDOs are now pivotal in AI strategy,” reflecting how the role has grown from compliance oversight to guiding enterprise initiatives that directly support organizational goals.

In this day and age, CDOs are tasked with ensuring that data is both accessible and reliable, providing a foundation for informed decision-making across business units. This includes establishing policies for data quality, access, and governance, which Roscoe highlights as essential: “data governance is foundational for AI.” At the same time, unstructured data ranging from documents and emails to multimedia adds complexity that requires careful management to make it useful while minimizing risk. “Unstructured data presents challenges,” he adds, emphasizing the need for structured oversight to fully leverage these assets.

AI Strategy

Although technology and analytics are evolving rapidly, the CDO’s role in aligning data with strategic initiatives is critical. By connecting data assets to business processes, CDOs help ensure that initiatives are informed by reliable, well-governed information and can deliver measurable results.

For anyone looking to understand the evolving responsibilities of CDOs, the importance of governance, and strategies for handling unstructured data, this episode of Tech Transformed provides a detailed and practical discussion.

For more insights, follow Informatica:

• X: @informatica
• Instagram: @informaticacorp
• Facebook: https://www.facebook.com/InformaticaLLC/
• LinkedIn: https://www.linkedin.com/company/informatica/

Takeaways

• CDOs are now central to shaping AI strategies and driving business growth.
• Robust data governance is crucial for the successful deployment of AI technologies.
• Unstructured data presents unique challenges and opportunities for AI development.
• A balance between centralized governance and federated operations is essential.
• Securing executive support is vital for the success of CDO-led initiatives.
• Engaging business stakeholders enhances the impact of AI projects.
• Demonstrating ROI through clear metrics is key to sustaining AI investments.
• AI governance must extend beyond data to include models and agents.
• New measures are needed to ensure the quality and governance of unstructured data.
• CDOs must navigate the tension between fostering innovation and maintaining governance standards.

Chapters

00:00:00 Introduction to the Podcast and Guests

00:03:00 Brett Roscoe's Background and Role

00:06:00 The Evolving Role of CDOs

00:09:00 Data Governance as a Foundation for AI

00:12:00 Challenges with Unstructured Data

00:15:00 Governance Frameworks for AI and Data

00:18:00 Centralization vs. Decentralization in Data Governance

00:21:00 CDO Strategies for Success

00:24:00 Conclusion and Future Outlook

About Informatica

Informatica, founded in 1993, is an enterprise data management company headquartered in Redwood City, California. The company provides software products for data integration, data quality, master data management, and data governance. With approximately 9,000 global customers across various industries, Informatica has positioned itself as a significant player in the data management market.

In the recent episode of the Don’t Panic It’s Just Data podcast, host Scott Taylor, The Data Whisperer and Principal Consultant at MetaMeta Consulting, speaks with Kevin Gibson, CPA and Principal Solutions Engineer at insightsoftware. They talk about the constantly changing nature of financial reporting. 

Additionally, they discuss the pros and cons of modern financial reporting and the importance of connecting financial data with familiar tools like Excel. The conversation also touches on the future of financial reporting technology and the need for organisations to adapt to changing data access needs.

Uncertainty in a Data-Driven World

“With all this uncertainty, companies are being asked to look at their data in different ways. They want to pivot it, slice it, and dice it,” Gibson tells Taylor, encapsulating the theme of this episode. “They’re being told to do more with the data — what does it mean, how do we read it, how do we understand it, how do we analyse it?”

The issue is that, as enterprises invest in digital transformation, finance teams struggle most with limited access to the data they need to support their analysis.

“The ideal state,” Gibson adds, “would be: I can get what I want, when I want, and how I want it — without asking questions. But let’s be honest — that doesn’t exist today.”

However, the good news is the data exists, Gibson says. The ugly part is that organisations can’t get to it. Many of the data accessibility issues have been attributed to cloud migration. 

“When you move your data to the cloud, you think: it’s cheaper, it’s more secure, it’s easier to maintain. But here’s the problem: you don’t control it anymore. Some cloud providers make access difficult or costly. So finance teams feel stuck,” he explains.

Also Watch: Stop Fighting Excel: How to Turn Your Spreadsheets into a Real-Time Reporting Powerhouse?

Real-Time Access on Excel

For decades, finance professionals have relied on Excel, which Gibson refers to as the “largest data warehouse in the world.” “There are 1.1 billion users of Excel today,” he says. “And let’s be honest, I haven’t met an accountant yet who says they hate it.” 

Finance prospers in Excel, but IT often views it as a risk. This leads to a constant back-and-forth between usability and control. Gibson believes that the solution is to equip both sides- finance and IT with real-time, governed data inside Excel. 

That’s where insightsoftware comes in. “We can connect directly to these systems and give finance teams back their real-time access — not just to pieces of data, but all of it,” says Gibson. “Literally every piece of data can be accessed.” 

With tools like Spreadsheet Server, finance professionals can work in Excel — their “comfort food,” as Gibson calls it — while drawing directly from live ERP data in the cloud. 

“We give them insight — that’s what our software does. It gives them visibility into their data. Excel isn’t going away, and our job is to make it work even better.”

To learn more, watch or listen to the podcast on EM360Tech.

Also Watch: Struggling with ERP Data? How to Get Real-Time Reporting in Excel

Takeaways

• Finance professionals are facing increased pressure to analyse data amidst uncertainty.
• The ideal state for finance teams is immediate access to data without barriers.
• Modern financial reporting has its good, bad, and ugly aspects, primarily revolving around data accessibility.
• Different industries have unique data needs and KPIs that impact financial reporting.
• Excel remains a critical tool for finance professionals despite the rise of cloud-based solutions.
• Organisations must find ways to connect their financial data with familiar reporting tools.
• The future of financial reporting will continue to evolve around Excel and data accessibility.
• Companies are beginning to realise the limitations of cloud-based systems and may revert to on-prem solutions.
• AI is emerging as a significant factor in data access and reporting.
• C-level executives should engage with finance teams to ensure they have the tools they need.

Chapters

• 00:00 Introduction to the Podcast and Guest
• 00:59 Understanding the Role of Finance in Uncertain Times
• 04:00 The Good, Bad, and Ugly of Financial Reporting
• 08:13 Types of Data and Industry-Specific Challenges
• 10:00 Connecting Financial Data with Reporting Tools
• 13:59 The Future of Financial Reporting and Technology
• 17:58 Key Takeaways for C-Level Executives

In this episode of the Don’t Panic It’s Just Data podcast, recorded live at Big Data LDN (BDL) 2025, Will Martin, EMEA Evangelist at Dremio, joins Shubhangi Dua, Podcast Host and Tech Journalist at EM360Tech. They talk about how enterprises can enable the Agentic AI Lakehouse on Apache Iceberg and why query performance is critical for efficient data analysis. 

"If you have a data silo, it exists for a reason—something's feeding information to it. You usually have other processes feeding off of it. So if you shift all that to a warehouse, it disrupts a lot of your business," Martin tells Dua. 

This is where a lakehouse comes into play. Organisations can federate their access through a lakehouse data approach. They can centralise access to the respective organisation’s lakehouse while keeping their data in its original location. Such a system helps people get started quickly.

In terms of data quality, if you access everything from one location, even with separate data silos, you can see all your data. This visibility allows you to identify issues, address them, and enhance your data quality. That’s beneficial for AI, too, Martin explains. 

Lakehouse Key to AI Infrastructure?

Lakehouse has been recognised for unifying and simplifying governance. An imperative feature of a lakehouse is the data catalogue, which helps an organisation browse and find information. It also secures access and manages permissions.

"You can access in one place, but you can do all your security and permissions in one place rather than all these individual systems, which is great if you work in IT,” reflects Martin. "There are some drawbacks to lakehouses. So, a big component of a lakehouse is metadata. It can be quite big, and it needs managing. Certain companies and vendors are trying to deal with that."

With AI and AI agents, it’s become even harder to optimise analytics on a lakehouse. However, this has been improved as technical barriers are disappearing. Martin explains that anyone can prompt a question; for instance, an enterprise CEO could ask questions about the data and demand justifications directly. 

In the past, a request would have to be submitted, and then a data scientist or engineer would create the dataset and hand it over. Now, engineers' roles have changed to focus on better optimisation. They help queries run smoothly and ensure tables are efficient. Agents cannot assist with that.

Also Listen: Dremio: The State of the Data Lakehouse

Optimise Lakehouse

Vendors such as Dremio provide services to manage and optimise lakehouses. They offer autonomous features to help set up the workflow. Martin says that in many cases, Dremio learns from the clients’ actions and improves their system. “This is evident in our reflections, which are optimised datasets that speed up performance,” he added. 

“In other situations, we handle tasks like file compaction and garbage collection, which are often less exciting for engineers. Now, there’s no need for engineers to manage those tasks, which benefits everyone.”

As a lakehouse provider, Dremio is Iceberg native. They began their journey as a lakehouse provider and continue down this road. Now the industry has shifted gears to focus on lakehouses too, first with Snowflake and now even Databricks, which has developed its format with Delta Lake.

The ultimate goal is to incorporate more features—permissions, governance, and fine-grained access control. “These capabilities are things vendors typically sell, but they will soon become widely available for free,” Martin tells Dua.

Learn More: Visit dremio.com for more information on open data lakehouse technology.

Key Takeaways

• Agentic AI and Apache Iceberg are current hot topics.
• Lakehouses offer quicker, less disruptive data access for AI compared to data warehouses.
• Centralised access in a lakehouse improves data quality and simplifies AI integration.
• Lakehouses, with their data catalogues, ease governance and permission management for AI agents working with sensitive data.
• Apache Iceberg is resolving metadata format issues, though metadata management remains an overhead.
• Dremio, an Iceberg-native provider, champions open source and interoperability, offering autonomous optimisation features to free engineers from mundane tasks.
• Beyond technology, a robust data strategy is crucial for organisational data improvement.
• Agentic AI will evolve to handle more delegated, multi-step tasks with less supervision.
• The open-source ecosystem will see consolidation and improved features, making advanced catalogue and governance tools widely available.
• Ultimately, for IT decision-makers, the quality of data is paramount for all analytical endeavours, including AI.

Chapters

• 0:00 - Introduction to Agentic AI
• 0:35 - Discussing Big Data London, Hot Topics: Agentic AI and Apache Iceberg
• 1:37 - Data Lakehouse vs. Data Warehouse for AI
• 2:30 - Data Quality and AI with a Lakehouse
• 3:18 - AI Agents and Sensitive Data: Governance with a Lakehouse
• 4:19 - Challenges and Solutions in Lakehouse Technology (Apache Iceberg)
• 5:47 - Dremio's Use Cases and Interoperability
• 7:40 - Dremio's Standout Features and Autonomous Optimisation
• 9:39 - The Importance of Data Strategy
• 10:29 - Future of Agentic AI
• 11:34 - Future of the Open-Source Ecosystem
• 12:51 - Final Takeaway for IT Decision Makers: Data Quality is Critical
• 13:51 - Conclusion

“While we typically assist data engineers with building the pipeline, we took the same data agent and built a different environment for it — a chat-like environment. By default, the chat has context, knows what to do, knows where not to go,” the Keboola CEO unveiled on the Don’t Panic It’s Just Data podcast. 

In the EM360Tech podcast recorded live at BDL, Dolezal spoke to Christina Stathopoulos, the Founder of Dare to Data in the recent episode of the Don’t Panic It’s Just Data podcast. They talked about the new Keboola Data Agent and it plays a key role in AI-backed change and the growth of large language models (LLMs) in business.

Context for AI-Backed Data Strategy Matters More

“Anyone can be technical now. It’s context that matters,” stated Dolezal, also the co-founder of Keboola. He presented a strong argument for why enterprise data strategies are falling short and how a new wave of smart tools will change that.

“The pipeline of what you can do is limitless if you build it for people in business,” he added. “You can't keep data and AI just in the hands of engineers anymore. That model doesn’t scale.”

As businesses face a growing number of data sources — sometimes over 300 SaaS platforms and more than 80 departments — managing, governing, and activating that data has become a challenge. The appealing promise of AI often adds another layer of complexity.

When AI Adds More Complexity

Enterprise leaders were told that AI would simplify data workflows. Instead, many found themselves managing disconnected tools and failed pilot projects.

“We all read the MIT study. 95 per cent of AI proofs of concept don’t make it to production,” the CEO of Keboola highlights. “Why? Because large language models (LLMs) need context. And enterprise data environments are anything but simple.”

At Keboola, context is crucial, he emphasised. It includes not just metadata but also event logs, debug trails, and orchestration details, including the complete story behind every data product.

“LLMs thrive on context. The more relevant context you provide, the better the outcome. But in today’s data stack, where your context is spread across 15 tools, that's nearly impossible.”

This is where Keboola’s new Data Agent comes in. It is a generative AI interface built directly into data workflows, capable of understanding and acting on both the structure and state of a company’s data.

Watch the podcast for further insights on EM360Tech. 

Key Takeaways

• Focus on context and domain knowledge rather than technical skills.
• Ease of acquiring technical skills compared to the importance of understanding business processes.
• Agents will run business processes both internally and externally.
• Need for infrastructure to support agents 
• Challenges of provisioning ad hoc environments
• Use agents to automate business processes.
• Importance of data governance.

Chapters

• Introduction & Keboola's Mission: 0:00
• Challenges in Modern Data Management: 2:23
• Impact of Complexity on Teams: 4:04
• LLMs in Data: Potential and Pitfalls: 5:50
• Keboola Data Agent: Bridging the Gap: 8:12
• Evolution of Keboola Data Agent: 13:00
• Future Vision for LLMs & Agents: 15:05
• Key Takeaway for Leaders: 17:12

However, with the rise of generative AI and large language models (LLMs), Reece Williams Griffiths, Field CTO of Collibra, says that we can no longer overlook 80 percent of enterprise content—from transcripts and PDFs to emails and images.

In this episode of the Don't Panic It's Just Data podcast, host John Santaferraro, CEO and Head Research Analyst at Ferraro Consulting, talks with Griffiths, also Co-Founder and CEO of Deasy Labs (acquired by Collibra). They also talk about the change brought to Collibra after acquiring Deasy Labs. 

Governing Structured & Unstructured Data

Following Collibra’s acquisition of Griffiths firm, Deasy Labs, he explains how this merger is making AI truly achievable for businesses. Deasy became renowned for its goal of simplifying data preparation. With Collibra, it’s leading the development of the tools necessary to create order from the chaos and build a unified AI enterprise.

Together, they created the first unified governance and catalogue platform for both structured and unstructured data. This single-hub approach is vital for a future where AI agents treat all data types equally.

Griffiths tells Santaferraro that, historically, Collibra, like others, focused only on structured data. Now, by combining Deasy’s capabilities, the platform provides a single entry point and a smooth experience for all data assets.

One outcome of a unified data strategy is simplified AI use cases. Since AI applications often need to access both tabular data (structured) and documents (unstructured) to give complete answers, unification offers the necessary routing and flexibility, the Field CTO explains.

Preparing Unstructured Data for AI

To effectively use a huge quantity of unstructured content, it must be prepared. Griffiths describes a four-layer data preparation funnel that goes beyond simple classification to deep semantic embedding, ultimately creating a Knowledge Product.

The talk of the moment is the knowledge data product, which the Collibra speaker says is familiar in the structured data scenario; however, not so much on the unstructured data. “We define a knowledge product with four elements – sensitivity, unstructured data quality, metadata for humans, and metadata for AI tools.”

"One key difference to note about them is that in the structured data world, data products are typically consumed by analytics, data and AI teams. Knowledge products, conversely, I think will be consumed by everyone."

Overall, Collibra’s system offers a multitude of solutions, including AI-based taxonomies which can automatically create meaningful taxonomies and segments directly from the data. This dramatically cuts down on the lengthy effort that manual mapping requires from subject matter experts.

This shift lets companies focus on areas where effort brings real value. For example, businesses can scan supplier contracts to identify auto-renewal clauses that trigger in 30 days. Enterprises can prioritise high-value, validated items that need human review. As technology evolves, this entire system is moving toward AI-managed workflows, representing a significant advance toward an autonomous enterprise.

Key Takeaways 

• 85 percent of enterprise content is unstructured (documents, transcripts)
• Unstructured data is the new foundation for scalable AI
• Governance must be unified (structured + unstructured) to simplify tools and serve AI agents effectively.
• Manual data labeling is impossible. 
• AI/LLMs must automate metadata generation via a Continuous Tagging System.
• Leaders should adopt the Knowledge Product idea—a governed, AI-ready asset for unstructured data consumed by the entire enterprise.

Chapters

0:00: Intro: The Exciting Moment in Tech

1:42: Unified Governance via Dz Acquisition

4:27: The 4-Layer Unstructured Data Funnel

7:35: Automation: Continuous AI Tagging

12:55: The Value of Knowledge Products

Collibra Bio

Collibra frees your data from the constraints of silos by unifying data and AI governance across your entire ecosystem, regardless of source or compute engine, for ultimate flexibility in how you manage data. Our Collibra Platform gives you automated visibility, control and tracing from input through output, and it automates documentation and data traceability for AI use cases to power speed, data observability and safety. Our enterprise metadata graph enriches data context with every use, and our intuitive UX brings technical and business users into the fold to access and steward data.

Real-World Applications and Governance

Petrie begins by noting that these technologies are already delivering real value today, particularly in areas like software development and customer service. The key, he emphasizes, is starting with strong oversight. “Governance starts with data,” he says, pointing out that reliable, well-managed data is the foundation for successful adoption.

Human oversight is equally important; automation alone cannot replace careful monitoring and decision-making. Effective governance also needs to extend beyond structured data to include unstructured information such as text, images, and other content types. As organizations adopt new models, they must be aware of the risks these systems introduce and put controls in place to mitigate them.

BARC DATA festival online

Building on these themes, BARC will host the DATA festival online on October 21, a virtual gathering designed to help leaders turn insight into action. The free event will bring together data professionals, decision-makers, and industry experts to share real-world use cases, operational frameworks, and lessons learned from practical implementations. The festival provides a clear roadmap for organizations seeking to make technology adoption both effective and sustainable.

Takeaways

• Emerging technologies are already delivering measurable value in business operations.
• Governance begins with reliable data and extends to all types of information, including unstructured formats.
• Human oversight is essential to maintain accountability and manage risks.
• Practical, real-world use cases illustrate successful adoption strategies.
• Cost efficiency, culture, and cross-functional programs are critical for sustainable implementation.
• Responsible, accountable practices are a must-have for long-term success.

Chapters

00:01: Introduction to AI Adoption

01:00: Real-World AI Applications

03:03: Balancing Automation and Human Oversight

05:19: Governance and Data Strategy

07:02: BARC DATA festival online preview

09:54: Audience and Takeaways

12:13: Rapid Fire Questions

14:02: Closing Thoughts

About BARC

BARC is a leading analyst firm for data and analytics and enterprise software, recognized for providing trusted, unbiased insights. Its expert analysts deliver in-depth research, advisory services, and industry events to help organisations make informed decisions about technology, data strategy, and analytics.

With over 25 years of experience in data strategy, governance, architecture, and software selection, BARC empowers clients to become truly data-driven organizations. Its research highlights market trends, evaluates software and vendors rigorously, and provides actionable guidance to help enterprises innovate with data, analytics, and AI.

On the Don't Panic, It's Just Data podcast, host Doug Laney and Søren Lundtoft dive into how business leaders can overcome digital-first challenges, especially when managing product information across a fragmented landscape. They discuss how PXDC—Product Experience Data Cloud—provides a unified, cloud-native solution for centralizing, governing, and syndicating product data. PXDC eliminates manual bottlenecks, automates data onboarding, and ensures every stakeholder works from a single source of truth, enabling faster launches and more consistent brand experiences.

The conversation explores the future of product experience, highlighting how AI and agentic workflows are transforming data management. PXDC leverages AI to automate content creation, localization, and compliance, while its composable architecture adapts to rapid market changes. The ultimate advice for data leaders: design systems for change, build on a solid data foundation, and embrace PXDC to unlock agility and growth in the age of AI

Error Caused by Multiple Platforms

The Sr. Director of Product Management at Stibo Systems identified the main issue as the use of multiple platforms and internal systems. He said that communication between these tools often occurs in spreadsheets, which are tedious and prone to errors. This leads to a situation where the last mile of data management becomes the longest mile, as companies struggle to share information with third parties. 

Alluding to the downsides of unstable downstream channels, where requirements change frequently. As he explained, a company might download a spec sheet from a retailer, but by the time they complete it and are ready to go live, a lot of changes have taken place. This ongoing state of change makes it nearly impossible to avoid mistakes and delays.

Lundtoft ended with an important piece of advice for C-suite leaders. He urged them to "decide for change" and shape their data systems so they can adjust to quick advancements in the market. He believes that data is "more important than it is right now" and serves as the true foundation for the current AI wave.

To learn more about the solution to managing product data and AI and its future, tune into the podcast on YouTube or Spotify at @EM360Tech.

Takeaways

• Brand owners face significant challenges in a digital-first world.
• Many companies struggle with multiple data platforms.
• Communication between tools often relies on spreadsheets.
• The last mile of data delivery is the most challenging.
• Consistency in product information is crucial for trust.
• AI can optimise data management processes.
• A single source of truth is essential for data governance.
• Data inconsistency can lead to costly errors.
• Future data management will involve agentic workflows.
• Adaptability in data models is necessary for future changes.

Chapters

• 00:00 Introduction to Data Challenges in a Digital World
• 01:31 Navigating Complex Data Systems
• 05:46 Ensuring Consistency in Product Information
• 07:44 Streamlining Data Management for Efficiency
• 11:35 The Future of Product Experience and AI
• 15:04 Key Advice for Data Management Leaders

About Stibo Systems

Stibo Systems is a global leader in Master Data Management (MDM) solutions, dedicated to helping businesses achieve data transparency and operational efficiency. With a rich history dating back to 1794, Stibo Systems empowers companies to connect, govern, enrich, and syndicate their data across various domains, ensuring a single, accurate view of their information.

Headquartered in Aarhus, Denmark, Stibo Systems is a trusted partner for many of the world’s largest and most innovative companies. Their solutions enhance the quality and value of master data, driving informed decision-making and business success.

In this episode of the Don't Panic It's Just Data podcast, host Kevin Petrie, Vice President, Research and Head of Data Management Practice, BARC, speaks with Maeghan Carriere, Divisional Vice President, Software Sales NA, insightsoftware; and Nate Cook, Director of Product Marketing, insightsoftware. 

They discuss the importance of automation, the role of CFOs as strategic leaders, and, most importantly, how mid-market companies can leverage tools like Spreadsheet Server for digital transformation. The conversation also spotlights the need for finance professionals to upskill in AI and data analysis to remain competitive.

All speakers agree that the key is in equipping finance professionals with direct, real-time access to their data in the environment they know and trust – Excel.

CFOs as Strategic Leaders

Cook believes that the role of the Chief Financial Officer (CFO) and their team has changed massively. "We really expect them to look back only as it helps them find a path forward.”

This new role requires finance teams to become effective data teams but how can they do that? A recent Gartner study found that 75 per cent of CFOs "said they own or co-own enterprise data and analytics at their organisations." Unfortunately, these teams "sometimes struggle to have access to the data that they need." The time wasted on tedious data entry and report generation is time lost for important analysis and strategic thinking.

"The short answer is Spreadsheet Server," Cook told Petrie, explaining that better decisions rely on better data, and Spreadsheet Server provides the latter.

Also Watch: Struggling with ERP Data? How to Get Real-Time Reporting in Excel

Excel with AI Upskills Finance Teams

Automating reporting doesn't just save time; it allows finance professionals to develop skills in areas like Excel, data analysis, and AI. Carriere points out that AI is an important tool that every professional needs to learn to use effectively.

"Once you understand how data flows and connects in real time, you're better positioned to use AI tools because you can quickly tell if the results make sense," she explains.

The key message for IT decision-makers is clear, as per insightsoftware’s divisional VP, "Stop fighting Excel and make it more powerful instead." 

By embracing the tool that finance teams prefer, organisations can achieve quicker results, faster adoption, and ultimately free their financial experts from manual tasks. Cook also notes, Spreadsheet Server is "one way to help remove a lot of that toil and refocus the time that your folks are spending on the more strategic parts of analysis and decision-making that can help drive your organisation forward."

Takeaways

• Finance teams are facing scaling pressures and resource constraints.
• The need for speed in decision-making is critical for finance leaders.
• Automation can save finance teams significant time and improve productivity.
• CFOs are increasingly expected to be strategic leaders within organisations.
• Excel remains a preferred tool for finance teams and should be leveraged in digital transformation.
• Mid-market companies should identify specific areas for technology to deliver efficiencies.
• Real-time data access is essential for better decision-making.
• Upskilling in AI is necessary for finance professionals to remain competitive.
• Finance teams can develop analytical skills while automating reporting processes.
• Building trust in data is crucial for effective AI implementation.

Chapters

• 00:00 Introduction to the Podcast and Guests
• 02:22 Current Challenges in Financial Reporting
• 05:18 Transforming Finance Teams with Automation
• 11:21 Digital Transformation for Mid-Market Companies
• 13:43 Accessing ERP Data for Better Decision Making
• 18:04 Upskilling Finance Teams for AI Integration
• 20:45 Key Takeaways for CXOs

In this Big Data special episode of the Meeting of the Minds, our expert panel – Ravit Jain, Podcast host,  Christina Stathopoulos of Dare to Data and a data and AI evangelist, Wayne Eckerson, data strategy consultant and president of the Eckerson Group and Kevin Petrie VP of Research at BARC, come together again to discuss the key data and AI trends, particularly focusing on data ethics. 

They discuss ethical issues related to using AI, the need for data governance and guidelines, and the essential role of data quality in AI success. The speakers also look at how organisations can measure the value of AI through different KPIs, stressing the need for a balance between technical achievements and business results. 

Our data experts examine the changing role of AI across various sectors, with a focus on success metrics, the effects on productivity and employee stress, changes in education, and the possible positive and negative impacts of AI in everyday life. They highlight the need to balance productivity with quality and consider the ethics of autonomous AI systems.

In the previous episode, new challenges and opportunities in data governance, regulatory frameworks, and the AI workforce were discussed. They looked at the important balance between innovation and ethical responsibility, looking at how companies are handling these issues.

Tune in to get new understandings about the future of data and AI and how your enterprise can adapt to the upcoming changes and challenges. Hear how leaders in the field are preparing for a future that is already here.

Also watch: Meeting of the Minds: State Of Cybersecurity in 2025

Takeaways

• Generative AI is creating a supply shock in cognitive power.
• Companies are eager for data literacy and AI training.
• Data quality remains a critical issue for AI success.
• Regulatory frameworks like GDPR are shaping AI governance.
• The US prioritises innovation, sometimes at the expense of regulation.
• Generative AI introduces new risks that need to be managed.
• Data quality issues are often the root of implementation failures.
• AI's impact on jobs is leading to concerns about workforce automation.
• Organisations must adapt to the probabilistic nature of generative AI.
• The conversation around data quality is ongoing and evolving. AI literacy and data literacy are crucial for workforce success.
• Executives are more concerned about retraining than layoffs.
• Younger workers may struggle to evaluate AI-generated answers.
• Incremental changes in productivity are expected with AI.
• Job displacement may not be immediate, but could create future gaps.
• Human empathy and communication skills remain essential in many professions.
• AI will augment, not replace, skilled software developers.
• Global cooperation is needed to navigate the evolving AI landscape.
• Data quality is critical for mitigating risks in AI applications.
• Organisations must prepare for the proliferation of AI models.

Chapters

• 00:00 Introduction to the Future of Data and AI
• 03:30 Significant Shifts in Data and AI Landscape
• 06:52 The Role of Education and Training in Data Literacy
• 09:59 Regulatory Perspectives: GDPR vs. US Approaches
• 13:06 Risks and Challenges in Implementing Generative AI
• 16:53 Data Quality: The Foundation of AI Success
• 21:59 The Impact of AI on Jobs and Workforce Dynamics
• 27:49 The Future of Workforce and AI Literacy
• 30:15 Job Displacement and the Importance of Junior Roles
• 32:05 AI's Impact on Professional Roles
• 34:26 The Role of AI in Software Development
• 39:54 The Necessity of Human Involvement in AI
• 41:10 Data Governance and Global Cooperation

In this episode of the Don't Panic It's Just Data podcast, EM360Tech host Trisha Pillay speaks with Andreas Blumauer, Senior Vice President at Graphwise, about how retrieval-augmented generation (RAG) and its advanced application, Graph RAG, are levelling up enterprise AI. Together, they explore the limitations of traditional AI, the critical role of knowledge graphs in improving data accuracy, and what it takes for organisations to successfully adopt these technologies.

Why Graph RAG Matters

While RAG enhances Generative AI by enabling it to retrieve relevant data from large knowledge bases, Graph RAG takes it further. By integrating knowledge graphs, Graph RAG preserves the relationships, sequences, and meaning inherent in enterprise data. This ensures AI outputs are not just collections of facts, but structured insights that reflect the logic of an organisation’s knowledge.

These advantages include:

• Higher accuracy: Retrieval precision can increase from 80% to 95%, reducing errors in AI outputs.
• Trustworthy results: Outputs are explainable and traceable, providing transparency that enterprises require.
• Scalable integration: Connects data across silos and departments, making AI adoption enterprise-ready.

“Graph RAG respects the structure of enterprise data instead of flattening it. That’s what makes it trustworthy,” explains Blumauer.

Generative AI opened the door to possibilities. RAG made it actionable. Graph RAG takes it to the next level. By transforming dark, siloed data into structured, actionable knowledge, Graph RAG helps organisations achieve the accuracy, trust, and scalability essential for navigating the next frontier of enterprise intelligence.

Takeaways

• 80 per cent of enterprise data remains unused or dark.
• Traditional AI struggles to interpret complex enterprise data.
• RAG retrieves information from within the enterprise data landscape.
• Graph RAG improves the accuracy of AI outputs.
• Knowledge graphs link data points across different silos.
• Building a knowledge graph is a strategic investment.
• Incremental growth is possible with knowledge graphs.
• Graph RAG can increase accuracy from 80 per cent to 95 per cent.
• Data quality and governance are essential for AI success.
• The future of enterprise AI relies on effective knowledge management.

Chapters

00:00 Introduction to RAG and Graph RAG

03:04 Understanding the Importance of Knowledge Graphs

05:46 Adopting RAG: Organisational Readiness and Strategic Investment

08:51 Real-World Applications and Benefits of Graph RAG

11:56 The Evolution of Knowledge Graphs in AI

14:46 Future of GraphRAG and Enterprise AI

17:36 Rapid Fire Questions and Closing Thoughts

About Graphwise

Graphwise is a leading enterprise AI company specialising in knowledge graph technologies. By combining retrieval-augmented generation (RAG) with advanced graph-based approaches, Graphwise helps organisations turn siloed, complex data into accurate, actionable insights, enabling smarter decisions, scalable integration, and trustworthy AI outcomes.

In this episode of Don’t Panic, It’s Just Data podcast, host Trisha Pillay is joined by Andy Steed, Event Director at Big Data LDN, and Roisin McCarthy, Founder of Women in Data. The conversation examines how Big Data LDN has become a cornerstone of the data community over the past decade and why advancing inclusivity is crucial to the conference’s continued impact in its 10th year.

A Decade of Big Data LDN

Over the past 10 years, Big Data LDN has grown beyond a traditional tech conference. As Andy explains, keeping the event relevant means constantly adapting to the changing needs of the data community. From established thought leaders to rising stars, the agenda is designed to showcase the breadth of talent driving innovation across industries.

Roisin highlights how Women in Data has grown to an incredible 95,000 members worldwide, becoming an anchor of community engagement in the data industry. At Big Data LDN, this partnership translates into a stronger focus on inclusivity, mentorship, and ensuring that diverse voices are not only present but heard. The Women in Data Lounge at this year’s event will be a dedicated hub for networking, support, and inspiration.

Inclusion and Accessibility in Practice

Both guests stress that real inclusion doesn’t happen by accident. It requires intention, continuous reflection, and a willingness to address barriers head-on. Big Data LDN is taking deliberate steps to create a welcoming environment, one where accessibility, representation, and community-building are central to the experience.

What Attendees Can Expect

Big Data LDN brings together world-class keynotes, hands-on workshops, and community-driven sessions, offering both deep technical insights and opportunities to connect. Networking is built into the event, with spaces designed for conversations that continue well beyond the conference floor. Taking place September 24–25, this year’s edition will be the largest yet, including a new conference that explores AI agents, AI governance and data products on September 23 for those seeking more focused, technical exploration.

Listen to the full episode of Don’t Panic, It’s Just Data to hear more about how Big Data LDN is shaping the future of data events.

Takeaways

• Big Data LDN has significantly evolved over the past decade.
• Women in Data has grown to 95,000 members, enhancing community engagement.
• Inclusivity and accessibility are ongoing challenges in data events.
• Real inclusion requires continuous effort and reflection.
• Big Data LDN showcases both established and rising stars in data.
• The event aims to create a welcoming environment for all attendees.
• Expect exceptional content and interactive community events at Big Data LDN.

Chapters

00:00 Introduction to Big Data LDN and Women in Data

02:37 Evolution of Big Data LDN and Its Impact

04:39 Challenges of Keeping Events Fresh and Relevant

08:36 Inclusivity and Accessibility in Data Events

09:01 Creating Real Inclusion in the Data Space

11:19 Opportunities and Challenges for Big Data LDN

16:17 What to Expect at Big Data LDN

18:37 Women in Data Lounge and Community Engagement

20:32 Final Takeaways and Closing Remarks

Also available on #Youtube, Apple and EM360tech.com

#BigDataLDN #DataCommunity #WomenInData #InclusionInTech #TechEvents #DataInnovation

In this episode of the Don't Panic, It's Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, speaks with Michael Ferguson, CEO of Intelligent Business Strategies and BDL Conference Chair, and Palesa Amadi, BDL Conference Manager. 

The speakers provide a sneak peek of the upcoming Big Data LDN conference. They discuss how AI affects data and analytics, and spotlight the importance of data governance. Additionally, they talk about the different theatres and sessions available at the conference. The conversation also shines a light on keynotes and notable speakers, the changes in data engineering and architecture, and the learning opportunities for attendees. 

AI Agents, Data Products in the Limelight

Get ready for "AI madness", says the Conference Chair. This year's event will feature AI in many forms, from AI agents walking the aisles to its integration into almost every software product on the exhibition floor.

"It's going to be hectic," Ferguson expresses. He expects a strong push toward AI-driven data governance and data management. Such changes aim to simplify complex tasks and make tools easier for more users to access. Instead of just seeing copilots, attendees will experience multi-agent capabilities and even "AI apps."

With 16 theatres and numerous speakers, the conference focuses on learning. Conference Manager describes how the content is tailored for everyone—from engineers and analysts to students.

"You're going to come to this to learn and to add to your knowledge, and of course, network," says Amadi. The show has changed its focus to include new theatres like Data Products and Streaming Analytics, along with Decision Automation. Some theatres have been renamed to highlight the growing influence of AI, such as the Data and AI Strategy Theatre.

A new "Data for Good" theatre will feature sessions on sustainability and using data for unconventional purposes, like tackling global food needs.

Unmissable Keynotes and Speakers

The speaker lineup is a big draw, featuring well-known experts and thought leaders. Amadi is particularly excited about keynote speaker Zhamak Dehghani, who will discuss the "apparent redundancy" of the term data mesh. Dehghani, who wrote the viral white paper on this topic, will advocate for her views, as Amadi puts it. Another highlight will be a session on the key role of big data and AI in the gaming industry, led by Carly Taylor.

The event's closing keynote will be delivered by the world-renowned physicist Professor Brian Cox. He will talk about quantum computing and the universe as a quantum computer. 

Beyond the Main Event

For those looking for more, Big Data LDN is hosting a new paid pre-show conference on September 23rd called Data-Driven LDN. This event is mainly designed for attendees in a high-level enterprise role, like CDOs and CTOs, and will be led by Mike Ferguson. It’s a chance to explore topics such as agentic AI, data governance, and data products in depth.

Whether you’re an industry expert or a curious newcomer, the 10th anniversary of Big Data London promises to be an engaging and transformative experience, demonstrating how quickly data and AI are changing our world.

For further information, tune into the podcast.

Takeaways

• Big Data LDN is celebrating its 10th anniversary.
• AI will dominate this year's conference with new technologies.
• Data governance now includes privacy, security, and compliance.
• The conference will feature various theatres focusing on specific topics.
• Sustainability will be a key theme in the Data for Good theatre.
• Speaker quality is ensured through a rigorous selection process.
• The event aims to cater to all levels of expertise.
• Real-world case studies will provide practical guidance.
• Data architecture is evolving to lower latency and improve access.
• Networking opportunities will be abundant at the conference.

Chapters

• 00:00 Introduction to Big Data London 2023
• 01:48 AI's Dominance at Big Data London
• 06:15 Navigating the Conference: Theatres and Content
• 15:02 Data Governance Challenges for AI
• 20:00 Improving Speaker Quality and Content
• 32:35 Key Technical Lessons from the Conference

From the switch to Vespa and the advantages of supporting multiple languages and complex queries, the podcast offers insights on the trade-offs organisations must think about when updating their search systems, especially regarding AI and machine learning applications.

Vinted Elasticsearch Challenges

Vinted’s search architecture was built on Elasticsearch before they switched to Vespa. Elasticsearch is a functional system that presents a few major challenges. With over 20 supported languages, the company's "index per language" approach created significant sharding problems, leading to infrastructure imbalances and constant adjustments.

"The index for the French language, the biggest language that we support, was more than three times bigger than the second biggest language, which created imbalances in the Elasticsearch data nodes' load," Jocas explained.

In addition to these technical obstacles, organisational issues arose as teams responsible for different parts of the search process found themselves "pointing fingers at each other at an increasing rate." The need for a more integrated, effective solution became clear.

The Solution: A New Platform for a New Era

The search for a better solution led Vinted to Vespa. The initial adoption was a "one success story" when a machine learning engineer, working on recommendations, discovered that Vespa was ten times faster than Elasticsearch for their use case. 

This initial benchmark, run on a single decommissioned server, was a "true testament to how efficient Vespa is when it comes to serving requests,” Jocas told Petrie.

Vespa helped Vinted solve their language problem by allowing it to set a language per document. Thus, it eliminates the need for separate indexes and the associated sharding headaches. As Jocas put it, "We got out of the sharding problem once and for all."

Takeaways

• Vinted faced challenges with its initial Elasticsearch architecture.
• The need for better integration between matching and ranking was identified.
• Vespa outperformed Elasticsearch in handling image search and recommendations.
• Transitioning to Vespa involved significant learning and support from developers.
• Vespa allows for language-specific document handling, simplifying architecture.
• Organisations must evaluate the complexity and volume of their data before transitioning.
• Vespa is optimised for query performance, while Elasticsearch excels in data writing.
• The learning curve for Vespa can be steep, but support is available.
• It's important to focus on optimising new systems rather than emulating old ones.
• Partial updates in Vespa are more efficient than in Elasticsearch.

Chapters

• 00:00 Introduction to Vinted and Vespa
• 02:05 Vinted's Initial Data Architecture Challenges
• 06:40 Common Trade-offs in Modernising Search Architecture
• 10:21 Transitioning to Vespa: Key Steps and Lessons Learned
• 14:11 Supporting Multiple Languages with Vespa
• 15:44 Vespa's Architecture and Its Flexibility
• 16:48 Use Cases: Vespa vs. Elasticsearch
• 20:34 Advice for Organisations Modernising Search Architecture
• 23:00 Final Thoughts on Transitioning to New Technologies

About Vespa.ai

Vespa.ai is an AI Search Platform for building and operating large-scale RAG, recommendation, and personalisation systems. It unifies data, inference, and ranking in a single query path for real-time results over massive datasets. Vespa delivers high throughput and low latency, and is available as a managed service.

About Vinted

Vinted.com is an online marketplace making second-hand a first choice. It connects buyers and sellers of pre-owned clothing, electronics, and home décor in a community-driven space. Active in 21 European markets plus the USA, Vinted serves 120 million users and 77 million monthly visitors. Its advanced search and recommendation engines handle 1 billion items and process 25,000 searches every second, with over 10,000 real-time data updates per second to keep the experience fast, relevant, and engaging.

The discussion covers the current state of financial reporting, the obstacles finance departments encounter, and how tools like Spreadsheet Server can help. Such tools allow finance teams to become strategic partners in their organizations. The conversation also shares real-world applications and case studies, showing how these solutions affect efficiency and decision-making.

The speakers share imperative insights on how enterprises can remake their finance teams from traditional "bean counters" to proactive strategic partners within their organizations. This is driven by the power of real-time data and efficient reporting.

From ERP to Excel

Considering how advancements, especially with AI in the picture, the business world is tunning at an exorbitant high pace. Due to this, access to real-time financial data is no longer a luxury but a necessity.

Despite that, many finance teams and IT decision makers face significant delays. These delays restrict timely and competent decision-making. Essentially, it all comes down to the way employees use ERP systems to deliver decision-makers reports in a usable format.

They depend on Excel because it is familiar and flexible. However, obtaining live, useful data from an ERP system into spreadsheets often requires tedious manual processes, exports, and reformatting. Unfortunately, it creates a significant blockage, wasting valuable time and delaying important insights.

"ERPs do a fantastic job with organising data, classifying it, structuring it, but then the reality is you want to then grab that data, pull it out and put it in a meaningful format so that you, your board, your leadership or your owner can digest that data,” expressed Wiley.

He added that it’s challenging to take data out of a system and put it in those unique formats. “That's where Excel is great. And that's where we typically find our users."

Spreadsheet Server Aids Real-Time Reporting

Imagine an Excel-based reporting solution that connects you to your ERP, providing dynamic reports that refresh with the click of a button. That's Spreadsheet Server. It cuts down the time spent on manual data extraction, reformatting, and reconciliation. This allows your finance team to move from simple number tracking to becoming strategic partners.

“Spreadsheet Server is your bridge,” says Wiley. “It connects you from your ERP to Excel in a dynamic fashion. It's a dynamic report. You can leverage, drill down and dive into details without having to reinvent the wheel all the time, export, put it in a pivot table, reformat, cleanse your data, remove all those duplicates, all those tedious steps get removed in that process."

For more information on insightsoftware, click here.

Takeaways

• The current state of financial reporting often lags behind real-time needs.
• Finance teams are looking to become strategic partners within organizations.
• Spreadsheet Server connects ERP data to Excel dynamically.
• Automation can significantly reduce the time spent on repetitive tasks.
• Real-world examples illustrate the efficiency gained with Spreadsheet Server.
• insightsoftware offers a range of solutions beyond just Spreadsheet Server.
• Evaluating time spent on tasks can reveal opportunities for improvement.
• Understanding user needs is crucial for selecting the right technology.
• The complexity of data reporting requires multi-source integration.
• Optimising processes can lead to better decision-making and efficiency.

Chapters

• 00:00 Introduction to the Podcast and Guests
• 02:59 Current State of Financial Reporting
• 06:29 Challenges in Real-Time Reporting
• 10:26 Spreadsheet Server: Bridging the Gap
• 13:39 Real-World Examples of Spreadsheet Server
• 20:16 insightsoftware's Broader Solutions
• 22:37 Key Takeaways for Decision Makers

It’s a demand that is forcing businesses to completely rethink how they engage with customers.

In the recent episode of the Don’t Panic It’s Just Data podcast, John Santaferraro, CEO and Head Research Analyst at Ferraro Consulting, speaks to Gary Class, Industry Strategist, Financial Services at Teradata and Sareen. They discussed how data, AI, and hyper-personalisation are reshaping customer experience (CX). 

The time of one-size-fits-all customer service is long over. Sareen emphasises this change by quoting Jeff Bezos: "If you have 4.5 million customers, then you cannot just build one store. You have to build 4.5 million stores." 

This isn't just about dividing customers into groups; it’s about providing a truly unique and personal experience for each person. 

How AI is Enhancing Customer Experience (CX)

Alluding to the use of gen AI and AI agents in customer experience (CX), Class highlights a key application of AI – analysing unstructured data. He says, “The one time that customers tell you what they really want and need, it's on the phone." 

By "grafting the unstructured data analysed through speech and text onto the structured data," businesses get equipped with rich insights on untapped possibilities. Overall, it also provides a 360-degree preview of the customer experience (CX), which allows for increased proactive and custom developments.

AI agents are set to take hyper-personalisation to new heights, the podcast guests believe. Alluding to the pros, Sareen points to their "predictive and proactive nature," which lets AI agents "identify and resolve issues before even customers are aware of them." 

Imagine a situation where a device automatically finds a problem, starts a support case, identifies a solution, and even applies a fix—all in real-time and without any input from the customer. Sareen calls this the "Nirvana state" of AI-driven customer service. It promises a smooth and genuinely smart experience.

Unified Platform for Simplified Quality Data

Simplifying how we manage raw data and turn it into quality data in a single system is critical for meeting the needs of AI in customer experience (CX).

This is where Teradata comes in. Class says the company’s approach centres on "harvesting all the data that you can collect in a conformed and harmonised framework." Their strategy includes providing industry-specific data models and leveraging the "capacity of a relational database engine that can deal with complexity at scale." 

This involves bringing analytic capabilities directly to the data, minimising data movement, and embracing open data formats. It allows customers to "bring their own model" and integrate with various model repositories.

Similarly, AWS focuses on delivering unified, low-code/no-code platforms designed to make development easy. Sareen articulated, "Rather than looking at multiple tabs, multiple services, how can we provide that unified experience?" 

Platforms like Amazon Connect for contact centre solutions and Amazon SageMaker Unified Studio for AI development aim to make it easier to deploy advanced customer experience applications by removing the complicated details.

The partnership between AWS and Teradata is crucial for achieving the vision of a customer experience (CX) powered by AI-driven hyper-personalisation. It relies on unified data platforms and real-time decision-making.

Takeaways

• Customer experience is about listening to your customers.
• Hyper-personalisation requires a unified data platform.
• AI agents can proactively resolve customer issues.
• Organisations must balance automation with human interaction.
• Data complexity necessitates a structured approach to analytics.
• Emerging technologies like NLP are crucial for understanding customer needs.
• The future of CX is being built today with accessible technologies.
• Seamless integration across channels enhances customer satisfaction.
• Partnerships are essential for scaling customer experience solutions.
• Understanding customer behaviour is key to effective CX strategies.

Chapters

00:00 Introduction to the Future of Customer Experience

02:56 Innovative Applications of AI in Customer Experience

05:58 The Complexity of Data and Simplifying Processes

09:05 Hyper-Personalisation in Customer Experience

12:03 The Role of AI Agents in Enhancing Personalisation

14:44 Unifying Technology for Seamless Customer Experience

17:55 Partnerships Driving Transformation in Customer Experience

20:43 Key Takeaways for CIOs

About Teradata

At Teradata, we believe that people thrive when empowered with trusted information. We offer the most complete cloud analytics and data platform for AI. With harmonised data, Trusted AI, and faster innovation, we enable more confident decision-making that improves business results. Find out more at www.Teradata.com.

“There’s a lot of hype around AI, but many initiatives aren't founded in a business value proposition,” says Paul Brownell, CTO, Growth Acceleration Partners (GAP). “People wander in without an intentional path for ROI.” 

In this episode of the Don't Panic, It's Just Data podcast, host Douglas Laney, BARC Research and Advisory Fellow, and author of Infonomics and Data Juice, speaks with Paul Brownell from GAP and Frank Lavigne, Advisory Board Member of CloudArmy.

The speakers ultimately agree that AI promises greater returns on investment (ROI). However, it’s imperative to note – without a strong data foundation and strategy, AI can quickly turn into a financial nightmare.

AI’s Potential to Cause Technical Debt 

Alluding to a significant “leak in the bucket” for AI initiatives, Brownell says, "a lot of these projects aren't founded in a business value proposition." This can often lead to organisations "wandering in without an intentional path." 

Both Brownell and Lavigne agreed that the most overlooked and costly area is data engineering. Lavigne exemplified this by referring to a meme depicting a sleek F-35 jet labelled "your AI" flying above a pockmarked, potholed road labelled "your data infrastructure."

"I think that pretty much says it all," Lavigne stated, highlighting the critical and often unglamorous role of data engineering. Brownell resonated with this, calling it "mundane, routine, detail, hard pick and shovel work."

Without mighty data quality, data governance, and traceability, AI projects are built on unsteady ground. Such AI initiatives occasionally result in inaccuracies and create a lack of trust.

Scientific Path to AI Initiatives in Data

Brownell advocated for a scientific approach to AI initiatives to overcome the hidden costs and maximise ROI. He said, "Come up with a hypothesis around where the business value is going to be, then apply some prototyping. Do real-life experiments to prove out your theory." 

Such an approach allows organisations to adjust course quickly. "The larger the ship, the harder it is to turn. So if you have these smaller kinds of proofs of concept, you can kind of find out in smaller increments how far we're off course,” explained Lavigne. This lowers risk and paves the way for more experimentation.

Takeaways

• AI investments can create hidden financial burdens.
• Data readiness is crucial for successful AI initiatives.
• A hypothesis-driven approach can guide AI projects.
• Iterative experimentation leads to better outcomes.
• Data engineering is essential but often overlooked.
• Generative AI can assist in data pipeline management.
• Selecting AI tools requires flexibility and speed.
• Purpose-built AI models may outperform generative models.
• Organisations must foster a culture of continuous learning.
• Understanding the total cost of ownership for AI is vital.

Chapters

00:00 Uncovering AI Technical Debt

04:56 Data Readiness for AI Initiatives

09:55 Selecting the Right AI Tools

13:06 Generative AI vs Predictive AI

18:14 The Future of AI Development

About GAP 

Growth Acceleration Partners is a consulting and technology services company that provides custom software, data engineering and modernisation solutions. GAP works to consult, design, build, and modernise revenue-generating software and data engineering solutions for clients. 

Their remote, integrated engineering teams help businesses achieve a competitive advantage through technology. Using AI, proprietary tools and seasoned experience, GAP’s end-to-end approach spans from consultation and migration services to full-cycle digital product development and managed services, ensuring the solutions delivered are innovative and align with your business goals. 

GAP transforms the way companies do business, helping clients to stay ahead of technology trends in a rapidly evolving digital landscape. Working with GAP, you are able to achieve operational efficiency and revenue goals with custom software that drives results.

“It's a trap that many financial institutions, banks, insurance companies, and more fall into. This loop or this cycle is a process of focusing on fixing problems, patching,” describes Errol Rodericks.

In this episode of the Don't Panic It's Just Data podcast, Errol Rodericks, Product & Solutions Marketing and Sales Enablement Specialist at Denodo, discusses the challenges faced by financial services in steering the infinity loop of reactive data management. 

He emphasises the importance of breaking free from this cycle to achieve real innovation and success in AI-led initiatives. The conversation explores the significance of AI-ready data, the journey from bronze to gold data products, and how Denodo helps bridge the last-mile gap in data management. 

The Cost of Poor Data

"The cost of poor data isn't just bad decisions. It's the decisions you never knew you could make. You never get around to that."

This statement sets the stage for the podcast. It captures the profound effect of being stuck in a reactive data mode and how organisations can overcome it. 

Rodericks believes that financial services are entirely missing out on strategic opportunities that could redefine their market position.

While many financial institutions have invested heavily in centralised lakehouse architectures like Snowflake or Databricks. These alone are not enough. They often struggle to deliver the trusted, real-time insights that Gen AI and business teams require. 

Missed Opportunities to Measurable Outcomes

It becomes more challenging for financial institutions to deliver adequate Gen AI real-time data insights when dealing with the "missing mile" of data. Approximately 30 per cent of crucial data is often overlooked or inaccessible. 

To overcome the Gen AI challenges, Denodo facilitates logical data management. The firm provides direct access to live, relevant, and governed data when it's needed. It becomes critical for achieving measurable outcomes with AI-led initiatives.

For Chief Financial Officers (CFOs), Rodericks offered a succinct but powerful message – "Modern finance isn't about reports... It's about your ability to predict, to personalise, and to prevent – the three Ps."

Takeaways

• The infinity loop is a trap for financial institutions.
• Reactive data handling leads to missed insights and customer churn.
• Breaking out of the infinity loop is essential for innovation.
• AI projects often fail due to unreliable data inputs.
• AI-ready data must be trusted, timely, contextual, and reusable.
• The journey from bronze to gold data products is strategic.
• Ownership of data products is crucial for success.
• Timeliness of data is critical in financial services.
• Denodo provides real-time access to data without copying it.
• Modern finance focuses on prediction, personalisation, and prevention.

Chapters

00:00 Understanding the Infinity Loop in Financial Services

04:23 Breaking Free from the Infinity Loop

06:03 The Cost of Firefighting Mode

09:14 The Importance of AI-Ready Data

19:42 The Journey from Bronze to Gold Data Products

22:53 Bridging the Last Mile Gap

27:06 Real-World Examples of Data Product Success

32:20 Key Takeaways for CFOs

In this episode of the Don’t Panic It’s Just Data podcast, Kevin Petrie, BARC analyst, sits down to chat with Thomas Gorr, Director of Product Management for xP&A and BI at insightsoftware, and Henri Rufin, Head of Responsible Data & Analytics at Radiall.

The speakers stress how the integration of financial planning in BI platforms is driven by the need to move beyond traditional Excel-based planning. This is because it often leads to data silos and errors. 

Their conversation spotlights how integrated BI and planning solutions improve collaboration across departments. Going deeper, BI can also provide a unified view of data and help organizations be more agile and proactive in volatile markets.

Gorr and Rufin explain how separating your financial data makes less sense, and why traditional tools like Excel are no longer significant.

Watch this podcast to discover how embedding planning capabilities within BI platforms, such as Qlik and Power BI, offer a seamless experience, greater flexibility, and real-time collaboration.

Learn how organisations are adapting to volatile market conditions through agile planning, and gain a glimpse into the evolving landscape of financial planning. 

Tune in to gain expert insights and practical strategies for a more collaborative and data-driven future!

Takeaways

• The integration of planning into BI platforms is essential for collaboration.
• Excel is prone to errors and creates silos in data management.
• Organizations need to adapt to dynamic market conditions for effective planning.
• Stakeholder engagement is crucial for successful financial planning.
• Advanced planning solutions offer flexibility and real-time collaboration.
• Data governance is necessary to support planning processes.
• The total cost of ownership is lower with integrated planning solutions.
• BI platforms provide a unified experience for users.
• Future planning will focus on platform integration and advanced analytics.
• Companies must evolve their planning capabilities to remain competitive.

Chapters

00:00 Introduction to Financial Data Management

03:04 The Shift Towards Integrated Planning

08:30 Collaboration in Dynamic Markets

11:05 The Role of Stakeholders in Planning

15:10 Moving Beyond Excel

19:19 Total Cost of Ownership in Planning Solutions

25:06 Future of Integrated Financial Planning

About insightsoftware

insightsoftware is a global provider of comprehensive solutions for the Office of the CFO. They believe actionable business strategies begin and end with financial data that’s accessible and easy to understand. 

They offer solutions across financial planning and analysis (FP&A), accounting, and operations. This transforms how teams operate, empowering leaders to make timely and informed decisions.

The duo explore the idea of focusing on data modernisation and improving accessibility rather than constantly implementing new technologies.

Both Petrie and Ram highlight the importance of traditional mainframes, especially in modern data strategies. They delve into how companies can combine cloud tools with data in order to handle diverse data systems. This removes the need for replacing the mainframe, keeping the data accessible for all users.

Going into the critical role of data quality and governance — especially in the age of AI — Ram emphasises that “garbage in, garbage out” has never been more relevant, as AI outputs are only as good as the data feeding the model.

What's needed is a more comprehensive approach to data integration, quality, governance, and enrichment that helps ensure data is always ready for confident business decisions.

Listen to this latest episode to learn how Precisely’s Data Integrity Suite provides a comprehensive approach to data modernisation.

Takeaways

• Data modernisation is about accessibility, not technology.
• The mainframe continues to play a crucial role in data strategies.
• High-quality data is essential for successful AI initiatives.
• Data governance is critical to comply with regulations and ensure data quality.
• Cloud solutions offer flexibility, but on-premise systems provide control.
• Companies need to adapt to a heterogeneous data environment.
• Integrating people and processes is key to successful data programs.
• Future data roles will require broad functional knowledge rather than deep technical skills.

Chapters

00:00 Introduction to Data Modernization

02:56 Understanding Data Modernization

06:01 Challenges in Data Modernization

08:53 The Role of AI in Data Strategies

11:56 Data Quality and Governance

15:08 Cloud vs On-Premise Data Solutions

18:14 Adapting to Diverse Data Environments

20:47 Advice for Modernizing Data Strategies

23:58 The Importance of People and Process

27:07 Future Skills for Data Teams

About Precisely

Precisely is a leading global data integrity provider, ensuring organisations have accurate, consistent, and contextual data. In today's data-driven world, where businesses rely on information for critical decisions, data integrity is crucial. Precisely offers a comprehensive portfolio of solutions designed to transform raw data into a reliable asset.

Trusted by over 12,000 organisations in more than 100 countries, Precisely plays a critical role in helping businesses effectively leverage their data. By providing reliable software and strategic services, Precisely empowers organisations to confidently embark on their AI, automation, and analytics initiatives. 

High-quality, trustworthy data is the bedrock for successful AI models, efficient automation processes, and insightful analytics. Without it, these initiatives risk delivering inaccurate results and misleading insights. Precisely's commitment to data integrity allows businesses to make confident decisions and achieve strategic objectives.

Scheduled to take place in Seattle from May 5th to 8th, 2025, The Peak is an exciting gathering for data and AI innovators. This conversation offers a preview of some of the practical applications and insights that will be shared at the event.

The podcast also talks about the development of creative solutions for enhancing accessibility in urban environments. The UBC speakers particularly refer to their creation of an accessible university campus navigation system, a project that showcases the power of integrating FME with platforms like ArcGIS. This discussion spotlights the challenges and ingenuity involved in building inclusive wayfinding solutions that cater to the diverse needs of a community.

The conversation sheds light on some tangible ways in which FME is being used across different sectors to tackle specific challenges and boost creative innovations. It provides valuable context for the types of practical knowledge and problem-solving approaches that will be central to The Peak of Data and AI event.

For further information on what we’ve talked about and to register for The Peak of Data and AI event in Seattle, please head over to peakofdataintegration.com.

Key Highlights

• Discover how to use tools like FME for preemptive IT issue resolution.
• Learn the approach to creating inclusive navigation systems with FME and ArcGIS.
• Get practical insights into current industry applications.
• Preview actionable data and AI solutions.
• Explore the versatile application of FME in your organisation.

About Safe Software

Founded in 1993, Safe is headquartered in Surrey, BC with over 200 team members and counting. We’re always looking for talented individuals with diverse backgrounds who are determined to learn and grow.

Over 20,000 organisations around the world use FME in industries like AEC, government, utilities, and transportation to maximise the value of their data.

• #Financeteams are often resistant to change, clinging to outdated tools.
• Real-time data integration can help break down data silos and better decision-making.
• The shift from FP&A to XP&A emphasises collaboration across departments.
• #Datagovernance is crucial when handling financial data.
• Real-time data enables more accurate forecasting and budgeting.
• Organisations should define their data strategy before implementation.
• Proactive adaptation to data technologies is essential for future success.

Summary

In this episode of "Don't Panic, It's Just Data," host Christina Stathopoulos explores the world of real-time analytics and its impact on financial decision-making. She is joined by Thomas Gore, insightsoftware's Director of Product Management for extended planning and analysis (XP&A), and Cody Riemenschneider, Director of Solutions Engineering, and they discuss the challenges and opportunities of integrating real-time data into finance.

We explore the issue of data silos and how real-time AI integration can break them down. Thomas explains: "Each department has its own data report, its own planning tools, which are mostly Excel files... They don't collaborate."

Listen to our latest podcast now for an insightful conversation on how real-time data is reshaping the future of #financialplanning and analysis. 

For the latest tech insights visit: EM360Tech.com

In this episode of the 'Don't Panic, It's Just Data' podcast, Shawn Rogers, CEO at BARC US, speaks with Errol Rodericks from Denodo. They explore the crucial link between trusted data and successful AI initiatives.  They discuss key factors such as data orchestration, governance, and cost management within complex cloud environments. 

We've all heard the horror stories – AI projects that fail spectacularly, delivering biased or inaccurate results. But what's the root cause of these failures? More often than not, it's a lack of focus on the data itself. Rodericks emphasises that "AI is only as good as the data it's trained on." 

This episode explores how organisations can avoid the "garbage in, garbage out" scenario by prioritising data quality, lineage, and responsible AI practices.  

Learn how to avoid AI failures and discover strategies for building an AI-ready data foundation that ensures trusted, reliable outcomes. Key topics include overcoming data bias, ETL processes, and improving data sharing practices.

Takeaways

• Bad data leads to bad AI outputs.
• Trust in data is essential for effective AI.
• Organisations must prioritise data quality and orchestration.
• Transparency and collaboration are key to building trust in data.
• Compliance is a responsibility for the entire organisation, not just IT.
• Agility in accessing data is crucial for AI success.

Chapters

00:00 The Importance of Data Quality in AI

02:57 Building Trust in Data Ecosystems

06:11 Navigating Complex Data Landscapes

09:11 Top-Down Pressure for AI Strategy

11:49 Responsible AI and Data Governance

15:08 Challenges in Personalisation and Compliance

17:47 The Role of Speed in Data Utilisation

20:47 Advice for CFOs on AI Investments

About Denodo

Denodo is a leader in data management. The award-winning Denodo Platform is the leading logical data management platform for transforming data into trustworthy insights and outcomes for all data-related initiatives across the enterprise, including AI and self-service.

Denodo's customers in all industries all over the world have delivered trusted AI-ready and business-ready data in a third of the time and with 10x better performance than with lakehouses and other mainstream data platforms alone.

• #CMDM is essential for creating a unified and accurate view of the customer. 
• Trust comes from how responsibly data is handled.
• Ethical data use is a competitive advantage and builds trust.
• Executive sponsorship and cross team buy-in is crucial for MDM projects.
• A strong customer #datastrategy is a competitive edge, not a side project.

Summary

Customer Master Data Management (CMDM) as Matthew Cawsey, Director of Product Marketing at Stibo Systems describes, "about having a master record of a customer, as opposed to many organisations where customer data gets created and authored in potentially dozens of different CRMs, ERPs, and finance systems.”

In this episode of our 'Don't Panic, It's Just Data' podcast series, Christina Stathopoulos, Founder at Dare to Data, speaks with Matthew Cawsey and Arnjah Dillard from Stibo Systems. Matthew and Arnjah explain the importance of CMDM and CXDC for maintaining positive customer engagement.

Learn how to move from basic personalisation, ethically use #data, leverage #AI for a better customer experience. Let’s transform your messy data into a competitive advantage. 

For the latest tech insights visit: EM360Tech.com

This premier gathering, hosted by Safe Software, the creators of FME, will be a hub for data and AI innovators, and this podcast episode offers an exclusive look into what attendees can expect.

Our conversation featured Margaret Smith and Reshma Joy from the West Virginia Department of Transportation. They shared their crucial work in ensuring data integrity through rigorous validations of their Linear Reference System data. This foundational work underpins much of their operational efficiency and decision-making. 

They further revealed how they’ve achieved seamless integration between Survey123 and their R&H data, showcasing a strong example of how disparate systems can be harmonized for greater insight. This presentation will provide attendees with actionable strategies for enhancing data quality and interoperability.

We also spoke to Bruno Blanco, a GIS Engineer from Shelby County 9-1-1. Bruno walked us through how FME supports critical aspects of their 911 addressing workflow—particularly data aggregation, QA/QC, and attribution—within a larger automation framework. 

This work highlighted the power of automation in critical public safety infrastructure. By streamlining their addressing processes, Shelby County 9-1-1 is improving response times and ensuring more accurate location data, ultimately saving lives. Bruno’s presentation will offer valuable insights into how organisations can leverage FME to automate complex workflows and enhance operational efficiency.

This episode serves as a compelling preview for the main event at The Peak of Data and AI. If you’d like to learn more about Bruno and Shelby County 9-1-1’s story, check out their success story with Safe.  For further information on what we’ve talked about and to register for The Peak of Data and AI event in Seattle, please head over to peakofdataintegration.com.

Takeaways

• Data validation is essential for accurate operations.
• FME enables seamless integration of disparate systems.
• Automation of critical processes improves public safety.
• Networking and community learning are key benefits of The Peak.
• Breakout sessions provide valuable hands-on FME knowledge.
• AI is increasingly influencing data integration workflows.

In this episode of the 'Don't Panic, It's Just Data' podcast, Debbie Reynolds, CEO and Chief Data Privacy Officer at Debbie Reynolds Consulting LLC, speaks with Leitch from insightsoftware. They discuss the vital role of financial strategy and collaborative planning, particularly as it pertains to the decisions made by IT executives.

The question they address is: In a world awash with data, how do we transform it into actionable insights? Warwick shares his wealth of experience, offering practical advice and illuminating the path to successful budgeting and forecasting.

One such challenge addressed in the podcast is how organisations are securing executive buy-in. "And 51 percent of people find it difficult to engage senior executives to buy into the process, which is a roadblock. And 57 percent of organisations struggle cross-functionally," Warwick reveals. 

It's not just about the numbers. Warwick also emphasises the human element, reminding us that "Without people, we don't have anything." In an era where AI looms large, it's crucial to remember that technology serves to enhance, not replace, human collaboration.

Tune in to the podcast and learn how to navigate the complexities of financial strategy and collaborative planning.

Takeaways

• Executive buy-in is crucial for successful budgeting.
• A clear vision helps guide the budgeting process.
• Thoughtful execution is key to effective planning.
• Fostering a culture of collaboration enhances participation.
• Data accuracy is vital in today's fast-paced environment.
• Avoid overcomplicating the budgeting process.
• Gamification can improve engagement in budgeting.
• AI can significantly streamline forecasting and reporting.
• Regularly updating forecasts leads to better accuracy.
• Understanding business measures is essential for effective planning.

Chapters

00:00 Introduction to Collaborative Financial Planning

05:01 The Importance of Executive Buy-In

10:09 Thoughtful Execution in Budgeting

15:01 Fostering a Culture of Collaboration

19:48 Defining Business Measures and Data Accuracy

24:52 Common Pitfalls in Collaborative Budgeting

29:52 The Future of Collaborative Planning with AI

In this episode, we discuss the stark reality of escalating energy consumption, driven by factors like the rise of AI, and the critical importance of moving beyond superficial "green" initiatives to implement genuine, impactful solutions.

From talking about the historical context of data centres energy usage, the evolution of energy demands and the challenges of achieving net-zero goals, Rolf provides valuable insights into innovative solutions such as smart grids, microgrids, and virtual power plants. These hold immense potential for managing energy distribution efficiently and sustainably. 

Beyond technological solutions, the podcast addresses the critical role of regulatory frameworks and industry standards in fostering sustainable practices. The frameworks are necessary to adapt to modern energy consumption patterns, ensuring interoperability and reducing costs. It spotlights the importance of collaboration between IT and utility sectors, as well as open communication with the public, to address concerns about energy consumption and build trust. 

Takeaways

• Data centres are increasingly becoming significant consumers of energy.
• Sustainability in data centre is often perceived as branding rather than genuine effort.
• AI's demand for processing power is escalating energy needs.
• Smart grids are essential for managing energy distribution effectively.
• Microgrids and virtual power plants offer promising solutions for energy sustainability.
• Enterprises can leverage renewable energy to become energy providers.
• Regulatory frameworks need to adapt to modern energy consumption patterns.
• Standards are crucial for ensuring interoperability and reducing costs.
• Collaboration between IT and utility sectors is vital for sustainable energy management.
• Open communication is key to addressing public concerns about energy consumption.

Chapters

00:00 Introduction to Data Centres Sustainability

03:22 Historical Perspective on Data centres Energy Consumption

08:32 The Role of Smart Grids in Energy Management

12:40 Understanding Microgrids and Virtual Power Plants

21:30 Enterprise Strategies for Sustainable Data Centres

29:51 Regulatory Challenges and Opportunities

32:34 The Importance of Standards in Data Centres Growth

With the rapid advancement of technology, prioritising trust in data is essential for unlocking AI's full potential and driving meaningful results. Conversely, a lack of data trust can undermine decision-making, operational efficiency, and the success of AI initiatives.

In this episode, Christina Stathopoulos, Founder at Dare to Data, speaks to Jay Limburn, Chief Product Officer at Ataccama, to explore these pressing topics. Together, they share actionable insights, real-world examples, and innovative strategies to help businesses harness the power of trusted data.

Key Takeaways

• Data trust is essential for confident decision-making.
• AI can significantly reduce mundane tasks.
• Organizations must focus on their data strategy.
• Customer experience is a key area for AI application.
• Data teams are crucial for successful AI initiatives.
• Proactive data management is becoming the norm.
• The chief data officer's influence is growing.
• Data quality and security are critical challenges.
• AI can enhance regulatory reporting processes.
• Trust in data is vital for successful AI projects.

Chapters

00:00 - Introduction to Data Trust and AI Integration

09:36 - The Role of AI in Operational Efficiency

12:47 - Balancing Short-term and Long-term Data Priorities

15:00 - Enhancing Customer Experience through AI

19:08 - Aligning Workforce and Culture for AI Success

21:03 - Innovative Strategies for Data Quality and Security

24:35 - Final Thoughts on Data Trust and AI Success

A key highlight of SARA’s success with FME is its use in predictive flood modelling and the standardisation of data workflows. By leveraging FME, SARA can more accurately predict flood risks, improving public safety and response times.

This innovation not only enhances internal operations but also helps SARA lead in sustainable water management. With its versatility in handling diverse data sources and streamlining communication between systems, FME is a powerful investment for organisations seeking to improve operational efficiency and long-term strategic decision-making.

In this episode, Debbie Reynolds, Founder and Chief Data Privacy Officer at Debbie Reynolds Consulting, speaks to Jordan Merson, Enterprise Applications Supervisor at San Antonio River Authority, about the game-changing impact of FME.

Key Takeaways: 

• Data management challenges often stem from a lack of standardisation.
• FME allows for the integration of various data sources seamlessly.
• Predictive modelling can enhance emergency response efforts.
• FME provides tools for real-time data monitoring and alerts.
• The user-friendly interface of FME accelerates onboarding for new team members.
• FME can handle both spatial and non-spatial data effectively.
• Collaboration and knowledge sharing are key to successful data management.

Chapters: 

00:00 - Introduction to Data Management and FME

02:30 - Jordan's Journey in IT and Data Management

05:43 - Challenges Before FME Implementation

08:36 - Transformative Impact of FME on Data Processes

10:02 - Real-World Applications of FME at San Antonio River Authority

14:06 - Predictive Flood Modeling and Emergency Operations

16:31 - Standardization and Efficiency with FME

17:59 - Final Thoughts and Recommendations on FME

This discussion explores the complexities and strategies surrounding edge computing and data management, highlighting the importance of security, the challenges of vendor lock-in, the implications of data repatriation, and the necessity of ensuring high-quality data for AI systems. It emphasises the need for organisations to balance edge processing with centralised storage while future-proofing their data strategies against rapid technological changes.

Building on their discussion, Jimmy Tam highlights the transformative role of edge computing in modern data management, emphasising the importance of governance, compliance, and interoperability to address the challenges of data sprawl and vendor lock-in.

Takeaways

• Edge computing is transforming how organisations manage data.
• Security at the edge is paramount to prevent intrusions.
• Data sprawl poses significant challenges for edge data management.
• Governance and compliance are essential for effective data management.
• Vendor lock-in can limit flexibility and adaptability in technology.
• Data interoperability is crucial for avoiding vendor lock-in.
• Data repatriation is a growing trend among organisations.
• AI systems require access to comprehensive data for training.
• Speed of data relevance is critical for effective AI applications.
• Flexibility in data strategies is essential for future-proofing organisations.

Sound Bites

"Data sprawl is a significant problem."

"Governance and compliance are crucial."

"Data repatriation is absolutely real."

"Speed of data relevance is critical."

Chapters

00:00 Introduction to Edge Computing and Data Management

02:53 Security Strategies for Edge Data

06:06 Vendor Lock-In and Data Interoperability

09:00 Data Repatriation and Cost Optimisation

11:57 Ensuring Quality Data for AI Systems

14:46 Balancing Edge Processing and Centralised Storage

17:59 Future-Proofing Data Strategies

This episode explores the evolution of real-time analytics and its crucial role in modern data processing. We delve into how technology is reshaping the way businesses interact with data and the importance of user-centric design in creating powerful data applications.

Joining Christina Stathopoulos, Founder of Dare to Data, is Rahul Rastogi, Chief Innovation Officer at SingleStore. Together, they discuss the necessity of real-time data in today’s fast-paced business environment, tackle the challenges organizations face in adapting to this shift, and highlight how data serves as the foundation for AI-driven innovation. 

Don’t miss this insightful discussion packed with practical strategies and forward-looking ideas!

Key Takeaways

• Real-time analytics has evolved from a luxury to a necessity.
• Streaming technologies like Kafka and Spark have revolutionized data processing.
• Legacy systems are often monolithic and ill-suited for real-time analytics.
• Modern data platforms enable easier data management and integration.
• Continuous intelligence requires a solid analytics foundation.
• User experience is critical for the adoption of data applications.
• Organizations must treat data as a valuable asset.
• Data governance and quality are essential for effective analytics.
• The separation of compute from storage enhances scalability.
• Real-time processing with low latency improves user satisfaction.

Chapters

00:00 - Introduction to Real-Time Analytics

06:14 - The Evolution of Technology in Data Processing

10:09 - Challenges of Legacy Systems

14:23 - Innovative Approaches to Data Management

18:06 - Building a Foundation for AI Innovations

21:27 - User Experience in Data Applications

When paired with AI, this clean and structured data becomes a powerful asset, enabling advanced analytics, predictive insights, and intelligent automation. MDM and AI help businesses uncover hidden patterns, streamline operations, and make more informed decisions in real-time.  

By integrating MDM with AI, organisations can move beyond simply managing data to actively leveraging it for competitive advantage. AI algorithms thrive on high-quality, well-structured data, and MDM ensures just that—minimising errors and redundancies that could compromise results. This synergy empowers companies to personalise customer experiences, optimise supply chains, and respond proactively to market changes. 

In this episode, Kevin Petrie, VP of Research at BARC US, speaks to Jesper Grode, Director of Product Innovation at Stibo Systems, about the intersection between AI and MDM. 

Key Takeaways: 

• AI and master data management should be integrated for better outcomes.
• Master data improves the quality of inputs for AI models.
• Accurate data is crucial for training machine learning models.
• Generative AI can enhance product launch processes.
• Prompt engineering is essential for generating accurate AI responses.
• AI can optimise MDM processes and reduce operational costs.
• Fast prototyping is vital for successful AI implementation.

Chapters: 

00:00 - Introduction to AI and Master Data Management

02:59 - The Synergy Between AI and Master Data

05:49 - Generative AI and Master Data Management

09:12 - Leveraging Master Data for Small Language Models

11:58 - AI's Role in Optimizing Master Data Management

14:53 - Best Practices for Implementing AI in MDM

Usage-based reporting is pivotal in this process, providing the granular visibility needed to understand real-time consumption patterns and optimise costs. Businesses can align expenses directly with value-driven activities by tracking how, where, and when resources are used. From preventing overspending to fostering accountability, usage-based reporting empowers teams to proactively manage their cloud expenses, turning cloud cost management into a strategic advantage rather than a recurring headache.

In this episode, George Firican, Founder of LightsOnData, speaks to Rem Baumann, Resident FinOps Expert at Vantage, about usage-based reporting and its benefits. 

Key Takeaways: 

• Organisations face challenges in tracking complex cloud costs.
• Usage-based reporting provides context to cloud spending.
• Metrics should align with business goals for effective decision-making.
• Communication between finance and engineering teams is crucial.
• Identifying cost optimisation opportunities can lead to significant savings.
• Different industries require customised cost metrics.
• Cloud providers offer basic tools, but deeper insights are needed.
• Regular monitoring of metrics ensures financial transparency.

Chapters: 

00:00 - Introduction to Cloud Cost Management

03:03 - Understanding Cloud Complexity and Cost Tracking

05:53 - The Role of Usage-Based Reporting

09:06 - Metrics for Cost Optimization

12:02 - Industry-Specific Applications of Cost Metrics

14:49 - Aligning Cloud Costs with Business Goals

18:09 - Conclusion and Key Takeaways

The ethical dimensions of data custodianship add further complexity as organisations balance the need for data-driven insights with privacy rights and transparent usage. Mismanagement can lead to significant financial, legal, and reputational risks, making effective custodianship essential for maintaining customer trust and regulatory compliance.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Debbie Reynolds, Founder and Chief Data Privacy Officer at Debbie Reynolds Consulting, about compliance with global regulations, the role of AI in data management, and the necessity of human oversight in technology.

Key Takeaways: 

• Data custodianship emphasises that data belongs to individuals, not companies.
• Organisations must have a comprehensive plan for data management throughout its lifecycle.
• Transparency and communication with consumers are essential in data handling.
• Different types of data require different levels of protection based on risk.
• Building trust with consumers requires responsible data practices.
• Organisations need to prioritise basic data protection strategies over compliance with every regulation.

Chapters: 

00:00 - Introduction to Data Custodianship

03:03 - Understanding Responsibilities in Data Handling

05:59 - Balancing Innovation and Data Protection

08:45 - Building Trust Through Responsible Data Practices

12:07 - Navigating Compliance and Data Governance

14:54 - Leveraging AI for Enhanced Data Custodianship

18:06 - The Role of Humans in Technology and Data Management

The integration of these technologies is reshaping operations, driving efficiency, and enhancing decision-making, helping businesses stay competitive and agile in a rapidly changing landscape. Organizations that embrace these innovations can better adapt to customer needs and market demands, positioning themselves for long-term success.

In this episode, Doug Laney speaks to Katrina M. Conn, Senior Practice Director of Data Science at Teradata, and Sri Raghavan, Principal of Data Science and Analytics at AWS, about sustainability efforts and the ethical considerations surrounding AI. 

Key Takeaways:

• Generative AI is being integrated into various business solutions.
• Unstructured data is crucial for enhancing customer experiences.
• Real-time analytics can improve customer complaint resolution.
• Sustainability is a key focus in AI resource management.
• Explainability in AI models is essential for ethical decision-making.
• The combination of structured and unstructured data enhances insights.
• AI innovations are making analytics more accessible to users.
• Trusted AI frameworks are vital for security and governance.

Chapters: 

00:00 - Introduction to the Partnership and Generative AI

02:50 - Technological Integration and Market Expansion

06:08 - Leveraging Unstructured Data for Insights

08:55 - Innovations in Customer Experience and Internal Processes

11:48 - Sustainability and Resource Optimization in AI

15:08 - Ensuring Ethical AI and Explainability

23:57 - Conclusion and Future Directions

The conversation delves into integrating AI into business operations, emphasizing the importance of a robust data foundation. Additionally, data security and compliance challenges in the context of GDPR and other regulations are explored. The episode concludes with insights on the benefits of hybrid deployment for organisations.

Key Takeaways: 

• Big Data London 2024 is a significant event for data leaders.
• Fivetran Hybrid Deployment caters to organizations with mixed IT environments.
• AI integration requires a strong data foundation.
• Data security and compliance are critical in today's landscape.
• Organizations must understand their data sources for effective AI use.
• Hybrid deployment allows for secure data management.
• Compliance regulations are becoming increasingly stringent. 
• Data readiness is essential for AI integration.

Chapters: 

00:00 - Introduction to Big Data London 2024

02:46 - Launch of Fibntran Hybrid Deployment

06:06 - Integrating AI into Business Operations

08:54 - Data Security and Compliance Challenges

11:50 - Benefits of Hybrid Deployment

reports are critical in providing detailed insights into data movement across cloud

environments. These reports help organisations identify usage patterns, track

bandwidth consumption, and uncover inefficiencies that may lead to higher expenses.

With a clear understanding of how data flows, businesses can make informed decisions

to optimise traffic, reduce unnecessary data transfers, and allocate resources more

effectively. This helps lower cloud costs, improves network performance, and enhances

security by revealing unusual or potentially harmful traffic patterns.

In this episode, Wayne Eckerson from Eckerson Group speaks to Ben Schaechter,

CEO of Vantage, about optimising network traffic costs with Vantage’s Network Flow

Reports.

Key Takeaways:

● Network Flow Reports provide detailed insights into AWS costs.

● They help identify specific resources driving network traffic costs.

● Organisations can reduce costs by up to 90% with proper configuration.

● The shift towards cost management in cloud services is critical.

● FinOps teams are becoming essential for cloud cost optimization.

● Anomaly detection can alert teams to unexpected cost spikes.

● Vantage integrates with multiple cloud providers for comprehensive cost

management.

● Effective cost management does not have to impact production workflows.

Chapters:

00:00 - Introduction to Vantage and Network Flow Reports

02:52 - Understanding Network Flow Reports and Their Impact

06:09 - Real-World Applications and Case Studies

09:03 - The Shift in Cost Management Focus

11:54 - Tangible Benefits of Implementing Network Flow Reports

15:07 - The Role of FinOps in Cost Optimization

18:00 - Conclusion and Future Insights

FME’s robust data integration capabilities are at the core of Omaha’s advancements. The data integration platform enables real-time tracking of e-scooters, offering precise data on their locations and usage patterns. This innovation enhances the management and accessibility of e-scooters, making urban mobility more efficient and user-friendly.

Automated parking management processes, facilitated by FME, streamline city operations and reduce manual efforts. This automation leads to smoother parking experiences for residents and visitors, while dynamic rate adjustments, powered by FME, ensure that parking fees are responsive to real-time demand, optimising availability and revenue.

In this episode, Wayne Eckerson from Eckerson Group speaks to Jacob Larson, an Applications Analyst from the City of Omaha, to discuss Omaha’s usage of FME. 

Key Takeaways:

• FME helps automate the tracking of e-scooters in real time.
• Data sharing agreements with providers like Lime enhance tracking capabilities.
• Omaha's parking management has been transformed through automation.
• FME allows for dynamic changes in parking rates based on events.
• The integration of GIS data with third-party APIs is crucial for parking management.
• Omaha is pioneering a real-time parking information system in the US.

Chapters: 

00:00 - Introduction to Omaha's Data Initiatives

01:03 - FME's Role in Asset Management

04:53 - Real-Time Tracking of E-Scooters

07:48 - Automating Parking Management

10:06 - Innovations in Parking Availability

12:59 - Dynamic Parking Rate Management

FME’s robust data integration capabilities are at the core of Omaha’s advancements. The data integration platform enables real-time tracking of e-scooters, offering precise data on their locations and usage patterns. This innovation enhances the management and accessibility of e-scooters, making urban mobility more efficient and user-friendly.

Automated parking management processes, facilitated by FME, streamline city operations and reduce manual efforts. This automation leads to smoother parking experiences for residents and visitors, while dynamic rate adjustments, powered by FME, ensure that parking fees are responsive to real-time demand, optimising availability and revenue.

In this episode, Wayne Eckerson from Eckerson Group speaks to Jacob Larson, an Applications Analyst from the City of Omaha, to discuss Omaha’s usage of FME. 

Key Takeaways:

• FME helps automate the tracking of e-scooters in real time.
• Data sharing agreements with providers like Lime enhance tracking capabilities.
• Omaha's parking management has been transformed through automation.
• FME allows for dynamic changes in parking rates based on events.
• The integration of GIS data with third-party APIs is crucial for parking management.
• Omaha is pioneering a real-time parking information system in the US.

Chapters: 

00:00 - Introduction to Omaha's Data Initiatives

01:03 - FME's Role in Asset Management

04:53 - Real-Time Tracking of E-Scooters

07:48 - Automating Parking Management

10:06 - Innovations in Parking Availability

12:59 - Dynamic Parking Rate Management

As data demands grow, open-source solutions ensure that businesses stay agile, reduce vendor lock-in, and maintain full control over their cloud infrastructure. The result? Faster insights, smarter decision-making, and enhanced performance—all powered by open source. 

In this episode, Paulina Rios Maya, Head of Industry Relations at EM360Tech, speaks to Mikhail Epikhin, Chief Technology Officer at Double Cloud, about The Power of Open Source in Cloud Platforms. 

Key Takeaways:

• Open-source technologies provide standard building blocks for products.
• Community-driven innovation is essential for the evolution of technology.
• Flexibility in data infrastructure is crucial for real-time processing.
• Observability and monitoring are vital for performance optimisation.
• Managed services can accelerate product development and feature implementation.

Chapters:

00:00 - The Power of Open Source in Cloud Platforms

05:24 - Apache Airflow: Enhancing Real-Time Data Management

10:08 - Balancing Open Source and Managed Services

13:57 - Best Practices for Scalability and Performance

Taking place at the Kensington Olympia in London on September 18-19, this year’s event features fifteen theatres and over 300 expert speakers sharing insights on some of the industry’s hottest topics – from generative AI to data analytics and privacy. 

With the event less than a week away, EM360Tech’s Head of Podcast Production, Paulina Rios Maya, grabbed Big Data LDN’s Event Director, Andy Steed, for a chat about his expectations for this year’s event and its growing importance in the data world.

In the episode, they discuss: 

• The exciting themes or breakthroughs attendees can expect to see showcased this year
• How Big Data London remains relevant in such a rapidly evolving field
• The unique networking opportunities or interactive experiences attendees have at the conference
• The standout sessions or keynote speakers at the conference

Chapters:

00:00: Introduction to Big Data LDN 2024

01:35: Showcasing Data Stories, Transformations, and Challenges

02:33: The Networking Opportunities with Industry Leaders and Peers at Big Data LDN 2024

05:01: Staying Relevant with a Focus on Generative AI and Real-World Use Cases

06:55:The Importance of Data Events for Community Building and Learning

About Big Data LDN 2024

Big Data London is the UK's largest data and analytics event, attracting over 16,500 visitors each year. Taking place at the Olympia in London on September 18-19, this year’s event features fifteen theatres and over 300 expert speakers across the two-day conference. 

Attendees can meet face-to-face with tech providers and consultants to find solutions to your data challenges and view the latest product releases and software demos to enhance your business' data capabilities.

It’s also a great opportunity for attendees to strengthen their business network with new and existing partners, and immerse themselves within the data community and network with speakers, colleagues and practicioners all in 2 days at Big Data LDN.

Sign up to Big Data LDN 2024 today and be part of the UK’s leading hub for the Data Community to learn and share best practices, build relationships and find the tools needed to develop an effective data-driven business.

We can’t wait to see you there!

MDM is the foundation of a successful sustainability strategy. By providing a single source of truth for all critical data, MDM helps businesses monitor and track their sustainability efforts effectively. With accurate data, companies can identify opportunities to improve resource efficiency, reduce carbon footprints, and ensure compliance with environmental standards, ultimately leading to a more sustainable and resilient business model.

In this episode, George Firican, Founder of LightsOnData, speaks to Matthew Cawsey, Director of Product Marketing and Solution Strategy, and Paarijat Bose, Customer Success Manager at Stibo Systems, to discuss sustainable sourcing and why accurate data matters. 

Key Takeaways:

• Sustainable sourcing involves understanding the provenance and environmental impact of products, ensuring compliance with regulations, and meeting sustainability goals.
• Data completeness and accuracy are crucial in meeting regulatory requirements and avoiding issues like greenwashing.
• Managing sustainability data requires a solid foundation of MDM to ensure data accuracy, stewardship, and semantic consistency.
• MDM solutions help companies collect, manage, and share sustainability data, enabling them to meet compliance requirements and achieve their sustainability goals.

Chapters:

00:00 - Introduction and Overview

01:07 - The Challenge of Collecting Data for Compliance and Reporting

02:31 - Data Accuracy and Completeness in the Supply Chain

05:23 - Regulations and the Demand for Transparent and Complete Data

08:41 - The Role of Master Data Management in Sustainability

15:51 - How Data Management Technology Solutions Help Achieve Sustainability Goals

21:02 - The Need to Start Early and Engage with Data Management Solutions

22:01 - Conclusion and Call to Action

Implementing data provenance does not require significant changes or large investments. Existing technologies and techniques can be seamlessly integrated to provide greater transparency and control over data. With data provenance, businesses can confidently manage their data, enhancing decision-making and fostering stakeholder trust.

In this episode, Jon Geater, Co-Chair of the Supply Chain Integrity Transparency and Trust (SCITT) Working Group, speaks to Paulina Rios Maya, Head of Industry Relations, about data provenance. 

Key Takeaways: 

• Data provenance is knowing where data comes from and how it has been handled, ensuring trust and integrity.
• The fundamental principles of data provenance include identity, timestamps, and the content of the data.
• Data provenance can be implemented by integrating existing technologies and techniques without significant changes or investments.
• Data provenance helps with compliance, such as GDPR, by providing a transparent record of data handling and demonstrating compliance with requests.

Chapters: 

00:00 - Introduction and Background

02:01 - Understanding Data Provenance

05:47 - Implementing Data Provenance

10:01 - Data Provenance and Compliance

13:50 - Success Stories and Industry Applications

18:10 - Conclusion and Call to Action

During the Maui wildfires, FME and the Pacific Disaster Centre were crucial in managing and analysing critical data, allowing for effective coordination and response. By facilitating seamless data sharing and collaboration among stakeholders, FME helps ensure that the correct information reaches the right people at the right time.

In this episode of the EM360 Podcast, Alejandro Leal, an Analyst at KuppingerCole, speaks to Jorma Rodieck, a GIS Specialist at the Pacific Disaster Centre, about the importance of FME. 

Key Takeaways:

• FME is an essential tool in disaster management and response, allowing for the integration and transformation of geospatial data.
• FME enables real-time data analysis and decision support for disaster management professionals.
• During the Maui wildfires, FME was instrumental in managing and analyzing critical data, providing a common operating picture for response efforts.
• FME ensures effective data sharing and collaboration among various stakeholders, enabling smooth interoperability between departments and agencies.

Chapters:

00:00 - Introduction and Background

02:35 - The Role of FME in Disaster Management

06:44 - Managing and Analyzing Critical Data with FME

10:34 - FME's Impact during the Maui Wildfires

11:59 - Ensuring Effective Data Sharing and Collaboration

15:20 - The Future of FME in the Pacific Disaster Center

18:15 - Conclusion

This is where DoubleCloud comes in, offering a managed platform that addresses these obstacles by handling crucial responsibilities such as backups, high availability, and security updates, allowing businesses to focus on leveraging their data.

In this podcast, Christina Stathopoulos speaks to Vladimir Borodin, Co-Founder and CEO of DoubleCloud, about open source strategies and the advantages of the DoubleCloud solution.  

Key Takeaways:

• DoubleCloud's managed platform helps overcome the challenges of open source, such as clunkiness and a lack of expertise.
• Successful customer use cases demonstrate the performance and cost benefits of DoubleCloud's solution.
• The transition phase to DoubleCloud's solution depends on the complexity of the application.
• Using open source whenever possible is recommended.

Chapters:

00:00 - Introduction and Background

02:29 - The Advantages of Open Source

04:21 - Challenges of Open Source

06:47 - The Power of Real-Time Analytics

09:11 - Success Stories: Improved Performance and Reduced Costs

12:54 - Navigating the Transition to DoubleCloud's Solution

15:14 - The Importance of Using Open Source

By embedding these practices, organisations enhance trust and accountability while meeting regulatory requirements. However, challenges such as resistance to change and the need for cultural transformation must be addressed to implement this principle effectively.

In this episode of the Don’t Panic It’s Just Data, Tudor Galos, Senior Privacy Consultant, speaks to Paulina Rios Maya, Head of Industry Relations, about the impact of privacy by default and design extend to user experience, where issues like consent fatigue and the necessity for user-friendly interfaces arise. 

Key Takeaways:

• Organisations face challenges in implementing privacy by default and design, including resistance to change and the need for cultural transformation.
• Privacy by default and design impact user experience, with issues like consent fatigue and the need for user-friendly interfaces.
• Regulations like GDPR and CCPA incorporate privacy by default and design principles, emphasising compliance and accountability.

Chapters:

00:00 - Introduction and Overview

01:00 - Core Principles of Privacy by Default and Design

02:19 - Difference from Traditional Privacy Practices

04:09 - Challenges in Implementing Privacy by Default and Design

05:33 - Impact of Privacy by Default on User Experience

08:14 - Alignment of Privacy by Default with Regulations

09:04 - Ensuring Compliance and Trust

11:24 - Implications of Emerging Technologies on Privacy

13:15 - Innovations in Privacy-Enhancing Technologies

15:50 - Conclusion

FME also drives innovation in projects like the DroneSense programme and their Cityworks asset management integration. With seamless data integration and powerful visualisations, FME empowers the City of Fremont to enhance operations, improve asset management, and support informed decision-making.

In this episode, Jonathan Reichental, founder at Human Future, speaks to John Leon, GIS Manager for the City of Fremont, to discuss: 

• FME 
• RIPA
• Public Safety 

Chapters:

00:00 - Introduction and Overview of the City of Fremont and IT/GIS Division

03:01 - Explanation of the Racial and Identity Profiling Act (RIPA)

04:27 - Challenges in Meeting RIPA Standards and Utilizing FME

06:21 - How FME Ensures Error-Free RIPA Data Submissions

09:40 - Benefits of Using FME for RIPA Compliance

10:39 - Other Innovative Projects Utilizing FME in the City of Fremont

13:30 - Future Plans for FME in the City of Fremont

17:17 - Recommendations for Government Agencies: Leverage FME for Data Submissions

Achieving high-concurrency data processing is crucial for overcoming performance bottlenecks in real-time analytics. Embracing real-time analytics is not just a necessity, but a way to transform your data into actionable insights, optimise performance, and fuel business growth.

Yellowbrick is a modern data platform built on Kubernetes for enterprise data warehousing, ad-hoc and streaming analytics, AI and BI workloads that ensures comprehensive data security, unparalleled flexibility, and high performance. 

In this podcast, Doug Laney, a Data Strategy Innovation Fellow with West Monroe, speaks to Mark Cusack, the CTO of Yellowbrick, about the power of real-time analytics. 

Key Takeaways:

• Real-time analytics enables faster business decisions based on up-to-date data and focuses on enabling actions.
• Using a SQL data platform like Yellowbrick, designed for high-concurrency data processing, can address performance bottlenecks in real-time analytics.

Chapters:

00:00 - Introduction and Overview

01:07 - The Benefits of Real-Time Analytics

06:23 - Overcoming Challenges in Implementing Real-Time Analytics

06:51 - High Concurrency Data Processing for Real-Time Analytics

13:59 - Yellowbrick: A Secure and Efficient SQL Data Platform

Integrating Product Information Management (PIM) with MDM ensures accurate and consistent product data across all channels, crucial for data-driven marketing. This combination centralises customer and product data, enabling precise targeting and personalised experiences. MDM and PIM integration leads to higher ROI and improved customer satisfaction by supporting effective marketing strategies.

In this episode of the EM360 Podcast, Paulina Rios Maya speaks to Philipp Krueger about integrating PIM and MDM functionalities and how it streamlines operations, improves data accuracy and supports data-driven marketing strategies. 

Chapters

00:00 - Introduction and Importance of Data Quality in AI Models

05:27 - Core Capabilities of an MDM System

08:13 - The Role of Data Governance in Data Management

13:37 - Enhancing Customer Experience and Driving Sales with Pimcore

19:47 - Integration of PIM and MDM Functionalities for Data-Driven Marketing Strategies

22:59 - The Impact of Accurate Data on Revenue Growth

27:28 - Simplifying Data Management with a Single Platform

There's a common misconception that effective data visualisation must be fancy and interactive, but simple visuals can be just as powerful. Ann K. Emery, an expert in the field, believes that accessibility doesn't have to be time-consuming or expensive. 

In this podcast, she shares actionable strategies for creating accessible visualizations with Paulina Rios Maya, Head of Industry Relations at EM360Tech. 

Key Takeaways

• Avoiding red-green colour combinations, 
• Ensuring proper colour contrast
• Using direct labelling instead of legends. 
• Avoiding using all-caps
• Using grey to highlight important information
• Employing small multiples to simplify complex visualisations. 

Chapters: 

00:00 - Introduction

00:54 - Defining Accessibility in Data Visualization

02:17 - Big A Accessibility Tips

06:36 - Little a Accessibility Strategies

12:28 - The Future of Data Accessibility

By embracing a centralised team, organisations can instil a culture of governance and efficiency in cloud cost management. This approach can lead to enhanced resource utilisation and substantial cost savings. With Vantage, your organisation can cultivate a robust cloud cost governance and efficiency culture, ensuring your cloud investments yield maximum value.

In this episode of the EM360 Podcast, Kevin Petrie, VP of research at BARC US, speaks to Ben Schaechter, CEO and co-founder of Vantage, to discuss: 

• FinOps
• Vantage’s platform 
• Cloud costs and FinOps practices

Chapters

00:00 - Introduction and Overview

02:02 - Understanding FinOps and Cloud Cost Governance

07:45 - Best Practices in FinOps: Centralization and Collaboration

13:50 - The Role of Data-Driven Insights in Optimizing Cloud Costs

The requirements of data management are changing with the advent of generative AI, requiring more flexibility and the ability to handle larger volumes of data. Pimcore leverages AI and machine learning to automate data utilization and improve data intelligence. 

By streamlining data management and integrating various data sources, Pimcore drives revenue growth for its customers. The platform combines data management and experience management to deliver personalized data across communication channels. Pimcore’s MDM solution addresses the challenges of integrating data for both human and machine consumption. The choice between physical and virtual MDM hubs depends on the use case and industry. 

In this episode of the EM360 Podcast, Doug Laney, Data and Analytics Strategy Innovation Fellow at West Monroe speaks to Dietmar Rietsch, Managing Director and Co-Founder of Pimcore, to discuss: 

• Data management
• AI
• Machine learning
• Data quality

Integrating Ontotext's solutions is a game-changer, empowering organisations to extract, organise, and visualise complex information from unstructured data sources. With Ontotext's expertise in semantic technology, businesses can construct robust knowledge graphs that offer a comprehensive understanding of their data landscape. This comprehensive approach not only facilitates better analysis and interpretation of data but also ignites innovation and propels business growth in today's increasingly data-driven world.

In this episode of the EM360 Podcast, Paulina Rios Maya, Head of Industry Relations, speaks to Doug Kimball, Chief Marketing Officer at Ontotext, to discuss: 

• AI in Enterprise Knowledge 
• LLMs 
• Knowledge Graphs 

Chapters

00:00 - Challenges of Integrating LLMs into Enterprise Knowledge Management Systems

04:35 - Enhancing Compatibility and Efficacy with Knowledge Graphs

07:21 - Innovative Strategies for Integrating LLMs into Knowledge Management Frameworks

11:07 - The Future of LLM-Driven Knowledge Management Systems: Intelligent Question Answering and Insight Enablement

Vantage addresses this issue head-on by providing organizations with a powerful platform equipped with automated cost recommendations, customizable reports, and real-time monitoring capabilities. By leveraging advanced analytics and machine learning, Vantage empowers teams to gain unparalleled visibility into their cloud spending and make informed decisions to optimize costs. 

In this episode of the EM360 Podcast, Dana Gardner, Principal Analyst at Interarbor Solutions speaks to Ben Schaechter, CEO and Co-founder of Vantage, to discuss: 

• Cloud cost management
• FinOps
• Cost optimization
• Automated cost recommendations 

Most practitioners rely on vector embeddings to surface content based on semantic similarity. While this can be a great step forward, achieving good quality requires a combination of multiple vectors with text and structured data, using machine learning to make final decisions. 

Vespa.ai, a leading player in the field, enables solutions that do this while keeping latencies suitable for end users, at any scale. 

In this episode of the EM360 Podcast, Kevin Petrie, VP of research at BARC US speaks to Jon Bratseth, CEO of Vespa.ai, to discuss: 

• the opportunity for Generative AI in business
• why you need more than vectors to achieve high quality in real systems
• how to create high-quality GenerativeAI solutions at an enterprise scale

With the help of cutting-edge platforms like Safe Software’s FME, cities like Burnaby, British Columbia, have revolutionised their operations. This has led to a significant enhancement in the quality of life for its residents. From predictive modelling to real-time data analysis, the potential for innovation appears boundless, underscoring the importance of GIS technology in improving urban operations.

In this episode of the EM360 Podcast, Wayne Eckerson speaks to Herman Louie, GIS Analyst at the City of Burnaby, to discuss: 

• Design and implementation of GIS solutions
• Safe Software’s FME platform 
• Transition to NG9-1-1 
• The future of GIS 

With FME, government organisations can overcome the barriers that hinder efficient data management. FME enables streamlined operations and improved decision-making processes by seamlessly connecting disparate systems and applications. Whether it's digital plan submissions, emergency services coordination, or interagency health data sharing, FME empowers government agencies to achieve their data integration goals with ease.

In this episode of the EM360 Podcast, Doug Laney, Data and Analytics Strategy Innovation Fellow at West Monroe speaks to Tom Seymour, Government Sales Team Lead at Safe Software, to discuss:  

• Data integration and interoperability 
• Safe Software’s FME platform 
• FME in governments 
• Advantages of FME 
• ROI with FME 

Imagine a giant web of information where everything is connected and easy to find. Knowledge graphs are revolutionizing how computers understand and process information, making it richer and more relevant to our needs. 

Ontotext is a leading provider of knowledge graph technology, offering a powerful platform to build, manage, and utilise knowledge graphs for your specific needs. Whether you're looking to enhance search capabilities, improve data analysis, or unlock new insights, Ontotext can help you leverage the power of connected information.

In this episode of the EM360 Podcast, George Firican, Founder of LightsOnData, speaks to Sumit Pal, Strategic Technology Director at Ontotext, to discuss: 

• Knowledge Graphs 
• Use Cases 
• Ontotext GraphDB
• Integration of AI 
• Industry best practices 

Yellowbrick, a leader in private data cloud solutions, empowers businesses to leverage their data on their terms. Their Bring Your Own Cloud (BYOC) approach offers unmatched flexibility and control. You can deploy Yellowbrick anywhere your data needs to be—public cloud, private cloud, or even the network edge. This ensures compliance with regulations and keeps your data exactly where you want it and can bring down costs. 

In this episode of the EM360 Podcast, Wayne Eckerson, President of Eckerson Group, speaks to Mark Cusack, Chief Technology Officer of Yellowbrick, to discuss:

• The need for hybrid multi-cloud data platforms 
• How Yellowbrick differentiates
• The future of private data cloud
• Why Yellowbrick? 

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos guides Deborah Leff (CRO at SQream) and Jason Hardy (CTO, AI at Hitachi Vantara) as they dissect:

• The Bottlenecks of Traditional Systems
• Unlocking GPU Potential
• Fueling Insights with Structured Data
• Faster Time to Insights

This episode goes beyond theory, exploring real-world examples (like a company querying a staggering 64 quadrillion rows) It showcases the potential for SQream and Hitachi Vantara to empower organisations to make data-driven decisions with unprecedented speed and accuracy.

The result? You're not reaping the full benefits of cloud-native, and your competitors are leaving you in the dust. 

Chronosphere, a leader in modern observability, can empower your developers and unlock the true potential of cloud-native. Buckle up and get ready to discover how observability can become your secret weapon for unleashing developer agility and innovation.

In this episode of the EM360 Podcast, VP of Research at Eckerson Group, Kevin Petrie speaks to Ian Smith, Field CTO at Chronosphere, to discuss: 

• Observability 
• Cloud-native software developers
• Developer inefficiency
• The Chronosphere solution
• Data pipeline best practices 

While handling structured data is considerably straightforward, processing and analyzing unstructured data is laborious. IDP equips users with the ability to process a multitude of document types, including PDFs, spreadsheets, and Word documents, among others. 

IDP platforms offer a powerful solution that streamlines data extraction from these documents by eliminating the need for any manual intervention. The extracted data, when integrated, enables you to make reliable decisions and improve business efficiency.

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Jay Mishra, COO at Astera, to discuss:

• Traditional document processing and the problems it causes
• AI-powered solutions
• The future of document processing and data extraction

Find out more about Astera’s unified, no-code data management platform here.

Whether (no pun intended) it’s an agricultural organisation looking to leverage precipitation data for planting schedules, energy companies looking at temperature trends to predict energy consumption patterns or transportation outfits looking to avoid delays, mastering weather data can really help modern companies to protect themselves against the unknown. 

In this episode of the EM360 Podcast, Analyst Susan Walsh speaks to Christian Schluchter, CTO at Meteomatics, to discuss: 

• Key use cases of weather data
• Mastering data access and predictive analytics
• The importance of being data driven

Data products contain datasets, related metadata and a wide range of functionality to understand if data is fit for use. But unlike relying on a raw dataset or data pipeline to generate value, data products deliver a comprehensive, packaged solution that enables data users to achieve their data-driven goals with easier accessibility.

In this EM360 Podcast episode, Head of Content Matt Harris sits down with Nathan Turajski, Senior Director of Product Marketing at Informatica. They explore how to:

• Drive better business outcomes with data products
• Understand the benefits of user-friendly and efficient consumption
• Improve AI, analytics and customer experience initiatives

With large volumes of data from various sources, it’s important that enterprises have a strategy in place to make the accessing process faster and more efficient while remaining compliant, secure and accurate. 

In this episode of the EM360 Podcast, Christina Stathopoulos, Founder at Dare to Data speaks to Anthony Cosgrove MBE, Co-Founder at Harbr, to discuss:

• Data access challenges and pitfalls
• Facilitating business use cases with improved data access
• Advice for CDOs and data professionals 

Combining elements of data lakes and data warehouses, the data lakehouse aims to address the challenges associated with both in a way which helps companies reach their data and business goals. 

In this episode of the EM360 Podcast, Christina Stathopoulos speaks to Read Maloney, CMO at Dremio, as they discuss:

• The state of the data lakehouse
• How an effective lakehouse strategy can be the key to digital transformation
• How data lakehouses empower business

Important for SREs, DevOps, ITOps, SecOps, DataOps and the wider tech professional, taking control of your telemetry data to address data challenges in a compliant and secure way should be a common goal for the modern business. 

In this episode of the EM360 Podcast, Analyst Kevin Petrie speaks to Tucker Callaway, CEO at Mezmo, to discuss:

• Understanding telemetry data pipelines
• Addressing data challenges with telemetry data
• Optimising performance and enhancing security with telemetry data

These architects of the digital realm are entrusted with the formidable task of not only harnessing the power of data but also steering their organizations through the complex and ever-evolving data landscape.

In this episode of the EM360 Podcast, Head of Content Matt Harris speaks to Niamh O’Brien, Senior Manager of Solution Architecture at Fivetran to discuss:

• Challenges faced by data leaders today
• Becoming GenAI ready
• The big opportunity for CDOs and their teams

It involves collecting and integrating data from various sources and departments to create a unified, real-time view. This panoramic perspective enables businesses to make informed decisions, identify opportunities, and address challenges across the entire enterprise.

In this episode of the EM360 Podcast, Head of Content Matt Harris speaks to Prash Chandramohan, Senior Director of Product Marketing at Informatica. They discuss:

• Having a 360-degree view of your business
• Driving great customer experiences
• Is your data ready for AI?

Using a residential proxy network, companies have been able to fine-tune their data collection for a range of use cases - from threat intelligence and email protection to ad verification and market research. 

But why do companies need this now? How do the different stacks of data extraction work and how can you make sure which one works best for you? And how is AI being used in this technology to reach industry-leading scalability and reliability?

In today’s episode of the EM360 Podcast, Head of Content Matt Harris speaks to Eitan Bremler, VP of Products at NetNut, to discuss:

• Data extraction and why its a problem for companies
• The different stacks of data extraction
• How AI/ML should fit in to your data strategy

When your customers can easily find the products they're looking for you can expect sales to increase, product returns to all but disappear, and brand loyalty to continue to grow. The problem is, how do you consistently deliver this level of quality in a world of disparate data sources, data silos, and the burden of existing technical debt?

AI is a powerful tool that can strategically help this industry unlock the true value of their data and meet the increasing demand for better data. By automating data cleaning, enhancing data quality, and generating actionable insights, organizations can deliver more meaningful experiences to their customers while eliminating the clutter of irrelevant data. This technology accelerates the industry's journey towards data-driven innovation and customer-centricity, ultimately giving trust to the end consumer.

In today’s episode of the EM360 Podcast, Analyst Susan Walsh speaks to Sam Russo, Practice Director of Automotive & Heavy Duty at Pivotree as they discuss:

• Why “perfect” product data is so important
• Issues in the automotive industry
• What AI/ML means for those modelling and enriching product data

In this episode of the EM360 Podcast, Head of Content Matt Harris speaks with Andy Bell, VP of Global Data Product Management at Precisely. They discuss and discover how businesses are transforming their strategies by enriching their data with comprehensive demographic, industry, social, and consumer insights, enabling them to make profoundly informed and accurate decisions.

Explore how data enrichment revolutionises various sectors:

• Retail: Craft highly targeted marketing messages using precise customer profiling, identify lucrative markets, and pinpoint expansion opportunities.
• Insurance: Enhance policy pricing decisions with a deep understanding of a property’s proximity to diverse risk factors, ensuring more informed and competitive offerings.
• Telco: Optimize network planning and infrastructure rollout strategies by leveraging enriched demographics, property data, and detailed building footprints.
• Government: Pinpoint essential community services such as aged care, child care, education, and medical facilities, ensuring resources are allocated where they are needed most.

Tune in to the podcast and unlock the immense value of enriched data. Learn how this rich portfolio of data can fuel your analytics, empowering you to make decisions that drive unparalleled growth and success.

It automates the most time-consuming parts of the ELT process from extracts to schema drift handling to transformations, so data engineers can focus on higher-impact projects with total pipeline peace of mind. 

With 99.9% uptime and self-healing pipelines, Fivetran enables hundreds of leading brands across the globe, including Autodesk, Condé Nast, JetBlue, Lufthansa, Morgan Stanley and Pitney Bowes, to accelerate data-driven decisions and drive business growth. 

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Fivetran’s Founder and COO Taylor Brown at Big Data LDN to discuss:

• The data space and how Fivetran fits in
• How customers are reacting to generative AI and ML
• Trends set to shake up the industry in 2024 and beyond

Applying product management principles to data management, data productization can connect your business objectives to data and stay ahead of the curve. 

But what things should you consider when building a data product? What challenges are likely to arise? And how can you effectively bridge the gap between data teams and business units to ensure the successful adoption and utilization of these products by end users?

In this episode of the EM360 Podcast, Analyst Kate Strachnyi speaks to Colin Brown, CMO at Revelate and Nicolas Doyen, CPO at Revelate, to discuss:

• Building a data product
• Ensuring successful adoption by end users
• Technologies shaping the future of data productization

A symbiotic one, say experts, but what technologies need to be implemented and how can companies bring in new tools to eliminate their digital debt?

In this episode of the EM360 Podcast, Analyst George Firican speaks to Derek Corrick, GM of Data at Pivotree and Justin Anovick, Chief Product Officer at Syndigo, to discuss:

• The relationship between product content and CX
• Leveraging AI/ML and how that impacts clients
• Best practices for solid content strategy

CPM encompasses various activities that help businesses plan, monitor, and analyze their performance against key performance indicators (KPIs) and targets.

But why is it so important? How does this technology interact with other business functions? And how can companies get started?

In this episode of the EM360 Podcast, Analyst Doug Laney speaks to Arthur Forbus, Managing Director and COO at HollandParker, to discuss:

• CPM and why every company needs it
• Misconceptions around CPM
• How to raise your CPM game

These were questions posed at the recent Modern Data Stack Roadshow; Fivetran's follow-up to the successful Modern Data Stack Conference. These half-day events convene top industry experts to discuss how to drive impact and business growth using data.

With plenty of exciting talking points raised by the industry leaders in attendance, how are some of the biggest movers and shakers talking about the data space right now?

In this episode of the EM360 Podcast, Analyst Susan Walsh speaks to Mark Van De Wiel, Field CTO at Fivetran, to explore: 

• Keeping up with the modern data stack
• Leveraging your modern data stack
• Advice on tackling complex data challenges

By leveraging data from external sources, businesses can gain valuable insights, make informed decisions, and identify new opportunities. But what challenges arise when implementing external data integration, and how can companies navigate some of the common pitfalls?

In this episode of the EM360 Podcast, Wayne Eckerson speaks to Dan Lynn, SVP of Product at CRUX, about:

• External data integration and its advantages
• Addressing challenges
• Best practices and avoiding common mistakes

From scalability to improved security, as well as cost-saving benefits, moving to the cloud bring a lot of benefits. But what challenges are enterprises facing in their migration, and how can this process be made easier?

In this episode of the EM360 Podcast, Analyst Susan Walsh speaks to Alex Merced, Developer Advocate at Dremio, to discuss:

• On-prem data lakes 
• Moving to a frictionless cloud
• How to streamline the migration process

This new approach to data management is challenging the traditional modern data stack, which relied on a series of disparate tools to handle different aspects of data processing. With data pipeline automation, organizations can simplify their data stack, relying on a single tool to manage the entire process. 

This shift will allow businesses to focus on insights and decision-making, rather than spending time wrangling data. As a result, data pipeline automation will become the key component of the post-modern data stack.

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Sean Knapp, Founder and CEO at Ascend.io, and we’re here to talk about:

• Solving data stack challenges
• Automation in data pipelines
• What the ultimate solution will be

However, enterprises face three significant problems in achieving frictionless commerce: getting the right data, managing tech debt, and ensuring data interoperability. And much like the old children's book "I Know an Old Lady Who Swallowed a Fly" - each problem seems to lead to another.

That's where Data-as-a-Service (DaaS) comes in. A cloud-based service that provides users with on-demand access to high-quality data, it can help organisations with these challenges by providing them with accurate and reliable data while reducing the burden of managing and maintaining it.

In this episode of the EM360 Podcast, Analyst Doug Laney speaks to Derek Corrick, General Manager of Data Management at Pivotree, to discuss:

• Frictionless commerce and how it can be accelerated
• Common challenges in data management 
• Data-as-a-Service and how to get started

However, managing data can be challenging for retailers due to the volume, variety, and velocity of data generated by multiple sources, such as social media, mobile devices, and online platforms. 

Retailers must also comply with data privacy regulations and ensure the security of their customers' personal information. Additionally, they need to invest in advanced technology and skilled personnel to manage data effectively.

In this episode of the EM360 Podcast, Head of Content Matt Harris speaks to Ramesh Shurma, Founder and CEO at Orion Governance, to discuss:

• Major data challenges faced by retailers
• Protecting data assets in the face of recession
• Governance predictions for the next five years

However, with the rise of distributed systems, microservices, and agile methodologies, this approach has proven to be inadequate for managing data at scale. Data Mesh proposes a different approach, where data is treated as a product and owned by the teams that produce it. 

In other words, it's a way of thinking about data management that focuses on decentralisation and autonomy. But how can companies use this to their advantage?

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos spoke to Paolo Platter, Co-Founder and CTO at Agile Lab, about: 

• Successful data mesh strategies
• Benefits vs monolithic data lakes
• Customer success stories

Agile Lab works with an array of clients across the globe, creating business vertical solutions to address specific challenges and needs in their field of activity. To hear about their success stories and the ways in which they exceed their clients' expectations, skip to 19:40.

By analyzing location-specific information, companies can make informed decisions that drive growth, reduce costs, and increase profitability. Ultimately, location intelligence is a valuable tool for companies seeking to make strategic decisions that optimize operations, enable growth, and gain a competitive edge in their respective industries.

In this episode of the EM360 Podcast, Analyst Susan Walsh speaks to Adam Ejsmont, Co-Founder of Echo Analytics, about:

• Location intelligence in the modern world
• Data collection and ensuring data privacy
• Why investing in location intelligence is critical

By collecting, processing, and analyzing data from various sources such as customer interactions, financial transactions, and employee performance, your business can gain valuable insights into its operations. 

This 360-degree view of the business data will allow your company to take a data-driven approach to decision-making, leading to better results and improved performance.

In this episode of the EM360 Podcast, Analyst Susan Walsh speaks to Dan Everett, VP of Product and Solution Marketing at Informatica about:

• Connecting different types of data 
• Collecting, analysing and storing data while complying with regulations
• Business-wide observability challenges

This includes monitoring the system's performance, availability, and errors, as well as identifying patterns and anomalies in the data. 

By implementing data observability, organisations can gain insights into their systems and make data-driven decisions to improve performance, optimize resources, and reduce costs. Common tools used for data observability include logging, metrics, tracing, and alerting. 

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Lior Gavish, Co-Founder and CTO at Monte Carlo, to discuss:

• Getting started with data observability
• Observability trends for 2023
• How to implement and common challenges

The goal of predictive analytics is to go beyond simply describing data to enable data consumers to accelerate the next-best actions to take for more effective decision making. For example, a retailer can understand their most relevant and high-quality data sets to better forecast consumer demand for a product, or a financial institution may identify the most accurate, complete customer data used to fuel analytics that help identify the likelihood of default on a loan.

In this episode of the EM360 Podcast, Analyst John Santaferraro speaks to Informatica’s Ian Stahl, Senior Director of Product Management and Chris Phillips, VP of Product Management to discuss:

• Shifting from business intelligence to predictive data intelligence
• Data sharing and data marketplaces
• The data consumption revolution

A critical practice as companies are pressured to make more and more data-driven decisions, data engineering is essential for enterprises to get ahead. 

In this episode of the EM360 Podcast, Editor Matt Harris speaks to Salma Bakouk, Co-Founder and CEO at Sifflet, to discuss;

• Trends shaping the data engineering industry
• Caring about data reliability and observability
• What the future brings

But what exactly is good data management? Why do we need it? And what are some of the unacknowledged ramifications that have serious impacts on our day-to-day lives?

In this episode of the EM360 Podcast, Analyst Doug Laney speaks to Misha Kapushesky, CEO at Genestack and Vivek Iyer, Team Lead, Human Genetics Informatics, from the Wellcome Institute, to discuss:

• What makes good data management
• How data impacts everyday life
• Driving behavioural and cultural change

Making data-driven decisions is critical to the success of a company in today’s world, and there really is no better way to do that than ensuring a data culture runs through the firm.

There are plenty of studies that show a correlation between the maturity of a data culture and the success of a business, including this one from Collibra which states “Organisations with higher levels of maturity across their data culture are likely to realise higher business benefits and performance versus peers.”

In this episode of the EM360 Podcast, Editor Matt Harris spoke to Databricks’ Field CTO Robin Sutara at Big Data London 2022 to discuss:

Building and maintaining a data culture

Data culture maturity

The Women in Data movement

Poor data quality can hit all enterprise levels hard, from marketers annoying prospects to supply chains becoming unable to automate processes properly.

In this second episode of the three-part EM360 Podcast series with Anomalo on data quality, Elliot Shmukler, Co-Founder and CEO at Anomalo joins Analyst Christina Stathopoulos to discuss:

• The right time for data quality monitoring
• What happens if you wait too long
• Who should be involved

A term used to describe data related to locations on Earth’s surface, spatial data can be used in everything from heat maps and political maps to smaller-scale indoor representations like in hospitals and offices. 

In this episode of the EM360 Podcast, Analyst John Santaferraro speaks to Todd Lewis, Founder and CEO of Spatial DNA, to discuss:

• Digital revolution and the challenges it’s produced
• Helping customers overcome complexity 
• The future of spatial data

Spatial DNA is a partner of Safe Software.

Performed through strategies like automation and full-stack monitoring, companies set their own data quality metrics and KPIs to measure and evaluate it.  

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Jeremy Stanley, Co-founder and CTO of Anomalo, to discuss:

• Data quality monitoring in a data reliant market
• Using unsupervised ML
• Changes in the last 5 years

Well-designed data governance programs often include governance teams, steering committees and data stewards. But behind these lie data standards, policies and philosophies that can help businesses handle astounding amounts of data in the right way.

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Ramesh Shurma, Founder and CEO at Orion Governance, to discuss:

Current data governance space

Bottom-up vs top-down approaches

Lowering TCO and increasing ROI

A cloud modernization program accelerates your analytics initiatives to gain the agility, speed, and scale needed to drive business value and deliver a competitive advantage. Informatica has enabled many leading customers to move their on-premises workloads to the cloud seamlessly and to innovate at scale. Learn how Informatica’s Cloud Modernization Program has allowed customers to streamline processes, improve data delivery, and accelerate time to insights while reducing migration costs and mitigating risk with minimum disruption to ongoing business operations.

On this episode of the EM360 Podcast, Analyst John Santaferraro speaks to Pratik Parekh, Group Vice President and General Manager of Cloud Modernization at Informatica, to explore:

• Cloud modernization challenges
• Recommended cloud modernization approaches to reduce migration cost, risk, and time 
• How customers accelerated their cloud journey

It, of course, has the potential to become real and actionable information - but the transformation requires selective extraction, organisation and formatting.

Businesses today are really beginning to recognise the value of raw data. With consumer data being one of the hottest commodities of the 21st century, the ability to make more informed decisions is driving the enterprise to make this unprocessed data work for them.

In this episode of the EM360 Podcast, Analyst John Santaferraro speaks to Nick Jewell, Senior Director of Product Marketing at Incorta, about:

• Hidden gem of modern analytics
• Acting with intelligence to improve outcomes
• The world of raw data

This makes it much easier to investigate and detect patterns, as well as apply real-time data controls to ensure safe storage and analysis.

In this third episode of the three-part EM360 Podcast, Analyst Christina Stathopoulos is joined by Richard Heyns, Founder and CEO of Brytlyt, to discuss:

Scaling analytics speed

How to optimise the processing of geospatial data

The next generation of BI

From 5G network deployments and geomarketing to Dominos Pizza understanding where you are and what you’re likely to order as soon as they pick up the phone, it’s clear that every sector is adopting LI to strengthen their processes.

In this episode of the EM360 Podcast, Ciaran Kirk, Operations Director at IMGS joins Analyst John Santaferraro to discuss Location Intelligence aspects such as:

Newest trends in data integration

Streaming data challenges

Modern data pipelines and what they give enterprises

From driving your car to shopping to fighting pandemics such as Covid-19. Everything is impacted by data and understanding is improved by embracing and using it 

Data powers many technologies and itself is neither good nor bad. As data influences more of our life, we have to ensure that we use it responsibly so as to improve the lives of everyone from customers, employees, citizens, and the planet we all live on. We face many challenges and data plays a critical role in helping us make the best decisions.

In the last episode of a three-part podcast series with Safe Software, Analyst Susan Walsh speaks to Don Murray, President and co-founder of Safe Software, to explore:

• How data was critical in the fight and management of Covid 19 around the world. 
• The importance of using data responsibly so that people and organizations understand the often unintended consequences of their actions.    
• The critical role of data is to drive decisions to meet the challenges we face as individuals, organizations, and a planet. Our future depends on using data.

There is no time like now for all of us to embrace data.

From driving your car to shopping to fighting pandemics such as Covid-19. Everything is impacted by data and understanding is improved by embracing and using it 

Data powers many technologies and itself is neither good nor bad. As data influences more of our life, we have to ensure that we use it responsibly so as to improve the lives of everyone from customers, employees, citizens, and the planet we all live on. We face many challenges and data plays a critical role in helping us make the best decisions.

In the last episode of a three-part podcast series with Safe Software, Analyst Susan Walsh speaks to Don Murray, President and co-founder of Safe Software, to explore:

• How data was critical in the fight and management of Covid 19 around the world. 
• The importance of using data responsibly so that people and organizations understand the often unintended consequences of their actions.    
• The critical role of data is to drive decisions to meet the challenges we face as individuals, organizations, and a planet. Our future depends on using data.

There is no time like now for all of us to embrace data.

But how can we take the power of deep learning and make it more accessible to the citizen data scientist?

In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Richard Heyns, Founder and CEO of Brytlyt, about:

• Deep learning and artificial neural network systems
• DL vs ML
• Embedding AI into the database

Similar to the DevOps approach, MLOps seeks to increase automation and improve the quality of production models while focusing on business requirements.

In the first part of this three-episode series on the ‘Transformational Serverless Acceleration for Analytics’, Analyst Christina Stathopoulos speaks to Richard Heyns, Founder and CEO of Brytlyt to cover:

• Why do two-thirds of models built with the intention to deploy never make it to deployment
• Databases and the critical role they play
• Scaling up analytics and ML