Don't Panic! It's Just Data

A massive organisation in the pharmaceutical industry deploys about six to seven master data management (MDM) platforms in addition to a few customised solutions. After facing challenges of scattered systems and their tools, the pharma company has decided to consolidate them.

The problem is that every drug, clinical trial, or product is named differently across the world. On top of that, if you had to prompt an AI agent to decipher the proportion of the business that comes from every product, it would have to read through several systems, with no agreement between them, and no way of knowing which one is right.

This is the challenge that LakeFusion, a master data management vendor, was tasked with solving for a major pharmaceutical firm.

In this episode of the Don’t Panic, It’s Just Data Podcast, recorded live at Big Data London, Podcast Host, Producer and B2B Tech Journalist, Shubhangi Dua sits down with three guests: Sanjeev Mohan, Principal at SanjMo; Cooper Kenny, Founding Sales and Alliances Manager at LakeFusion; and Panos Athanasiou, Chief Field Technologist at Databricks. They explain why agents are unable to make up for poor master data, what it means to govern context rather than just data, and the reason why MDM has to cover the tools and data products that agents depend on.

• MDM is forecast to grow from $20 billion to $42 billion by 2030.
• Humans spot wrong records. Agents don’t.
• Governance now has to cover context, not just data.
• Context is generated at query time, so it is hard to inspect.
• LLMs can match structured records to audio, PDFs and social posts.
• Zero-copy architecture removes the need for CDC pipelines in MDM.
• Ontology needs semantics and connections, not only graphs.
• Human operators still decide what counts as a golden record.
• Bloated context drives token costs up and invites hallucination.
• Agentic data stewards can work through human-review backlogs.
• Start with a thin slice of value deliverable in weeks.

• 00:00 Introduction to Big Data London and Participants
• 05:02 Master Data Management: Evolution and Trends
• 09:54 AI Native MDM: The Future of Data Management
• 15:07 The Role of Context in Data Governance
• 19:55 Challenges of Bad Data and Its Impact
• 24:36 Cultural and Technological Shifts in Data Management
• 25:00 The Importance of Master Data in AI Initiatives
• 25:24 The Future of Data Management Solutions
• 26:43 Lake Fusion's Unique Position in the Market
• 29:30 The Role of AI in Understanding Business Dynamics
• 32:06 The Evolution of AI Agents in Workflows
• 35:37 Key Takeaways for C-Suite Executives

@Bigdataldn @LakeFusionAI @Databricks

Read the full article: https://em360tech.com/podcasts/big-data-ldn-why-mdm-back-c-suite-agendahttps://em360tech.com/podcasts/big-data-ldn-why-mdm-back-c-suite-agenda

LakeFusion: https://lakefusion.ai

EM360Tech: https://em360tech.com

Watch more from the Don’t Panic, It’s Just Data Podcast: https://www.youtube.com/playlist?list=PLXoSlBGE35NoUm5jhXSTu4DdytBNXTgwY

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#BigDataLDN #MasterDataManagement #MDM #AgenticAI #AIAgents #DataGovernance #Databricks #EnterpriseAI #DontPanicItsJustData #EM360Tech

master data management, MDM, AI agents, agentic AI, golden record, data governance, context layer, Big Data LDN, Databricks, LakeFusion, enterprise AI, data management

An acquisition is just about to close this Friday. That means, by Monday, finance wants to know where the group is losing margins. The problem is the new enterprise functions on its own systems, and onboarding a single data source is quoted at weeks or months. Meanwhile, the foundation model your board approved last quarter sits on top of all of it, waiting for data it cannot yet read.

That is the position one of Advancing Analytics' hospitality customers, a global food and beverage group built through acquisitions, was in before it changed how it brings data on board.

In this episode of the Don't Panic It's Just Data podcast, recorded live on the show floor at Big Data London, host Shubhangi Dua, Podcast Host and B2B Tech Journalist, speaks with Andrew Phethean, UKI Industry Lead, Enterprise Growth at Databricks, and Terry McCann, CEO of Advancing Analytics.

This podcast episode is especially relevant to CDOs, CEOs, and CEOs who are now beyond AI experimentation and are now pursuing plugging agentic systems into production. The speakers cover why the foundation of the platform’s model is the key factor when it comes to the outcome, what Databricks calls the four C's, and why the cost of doing nothing rarely makes it into the business case.

"I think a lot of the time enterprises are experimenting with the big vendors of the world and putting a lot of investment in that, but not necessarily putting that into production properly,” Phethean tells Dua.

• Buying a foundation model without a governed data platform stalls projects.
• Newer models aren't needed for every business operation, says Phethean.
• Open source, lower-cost models and machine learning can carry production work.
• Databricks frames AI obstacles as four C's: context, control, cost, choice.
• Cost discipline now means maximising value, not tokens.
• Vendor lock-in isn't a strategy: choice spans vendors, clouds and LLMs.
• Data governance now serves AI agents as well as people.
• LakeForge cut data source onboarding from weeks or months to minutes.
• The cost of inaction can outweigh the ROI case for investing.
• CDOs and chief AI officers need a seat at board level.
• McCann's CDO advice is speed to market; Phethean's is translating IT to business.

• 00:07 – Welcome and introductions
• 00:48 – How has Big Data London been?
• 02:32 – From generative AI to agents to context
• 04:33 – The biggest challenge: getting the plumbing right
• 05:49 – The four C's (and a fifth)
• 07:31 – The Advancing Analytics and Databricks partnership
• 09:52 – Customer story: LakeForge and Genie Code
• 11:22 – The shift CEOs aren't watching
• 15:08 – ROI versus the cost of inaction
• 16:44 – What the tech industry gets wrong about AI
• 18:38 – If your AI agent had a personality
• 20:02 – One takeaway for CDOs
• 21:30 – Closing and where to find the guests

For more information, visit: advancinganalystics.co.uk

Check out more data podcast episodes in the enterprise tech landscape: https://youtube.com/playlist?list=PLXoSlBGE35NoUm5jhXSTu4DdytBNXTgwY&si=iQzaMGoNb7wnRUaF ​

⁨@AdvancingAnalytics⁩ ⁨@Bigdataldn⁩ ⁨@enterprisemanagement360⁩

#BigDataLDN #BigDataLDN2026 #AdvancingAnalytics #Databricks #AIAgents #LakeForge #GenieCode #AIROI #AIProfit #AILoss #AIMarketValue #AIValue #AIProduction #AIinProduction #AICosts #DataAnalytics #aianalytics #AIData

With the boom in online shopping, the risk of parcel theft has scaled to an unprecedented level. According to security.org, the 2025 porch piracy data shows that 64 million Americans (one in four) have experienced package theft, with 9 million adults falling victim within the past three months alone. Cameras exist, but robbers wear masks and steal anyway; the risk grows every hour. That was United Parcel Service (UPS), an American parcel delivery company, in that position. The fix, however, was built with Striim and Google BigQuery. They came together and calculated the risk score for every delivery in real time, delivering enhanced security.

In this episode of the Don't Panic! It's Just Data podcast, recorded live at Big Data LDN in the Y-Axis theatre in front of 200 people, host Christina Stathopoulos, Founder of Dare to Data, sat down for a fireside-style chat with Alok Pareek, Founder and EVP of Product Development at Striim.

They critically examine how agentic workloads impact cost, predictability, performance, and consistency. Pareek also explains why data pipelines that simply move data can no longer keep up, and where processing should happen. Pareek, who in his early career worked on the Oracle database kernel and later served as CTO of GoldenGate, also strongly opposes zero-copy ETL.

For CIOs, CDOs, and leaders in the field of data engineering who already manage mission-critical pipelines for use in operations, analytics, and business intelligence, the fact is that a new type of consumer is now situated atop these pipelines.

“A simple question posed to an AI agent is quickly becoming a distributed computing problem,” states Pareek. “Especially when asking relatively complex inquiries such as ‘is this customer really someone I need to care about deeply?’”

So when accessing a Large Language Model (LLM) for inference, the AI agent might be accessing its own tools, going through multiple database systems, files, websites, or simple search functions. He says the agent may be exasperated and, as a result, it ends up creating a classic GPU cost problem and a distributed computing problem.

Also Watch: Can AI Agents Make Decisions Without Real-Time Enterprise Data?

• Agents create databases at 4x the human rate; half last under 10 seconds.
• Snowflake saw agent queries scanning trillions of records to return two.
• One agent question becomes a distributed computing problem.
• Agents querying several systems receive states from different moments.
• Pipelines built only for data movement can't explain why they break.
• Shift-left analytics acts on data in flight, before the warehouse.
• Stale data compounds agents' hallucination and people-pleasing tendencies.
• AI sovereignty now covers where models run, not only where data sits.
• Agent-guarded replicas keep agents away from production systems.
• Zero-copy ETL moves complexity onto the source database.
• UPS scores delivery addresses from 100 to 1000 in real time.
• The four Cs: current, consistent, consumable, contextual.

• 03:13 Welcome and Alok Pareek's background
• 06:17 What the agentic era demands of data pipelines
• 14:34 Moving data versus intelligent pipelines
• 18:40 Shift-left analytics
• 20:49 Data and AI sovereignty
• 25:05 Why zero-copy ETL falls short
• 28:51 UPS Defense Delivery and real-time risk scoring
• 31:26 The four Cs

MORE RESOURCES:

Striim: https://www.striim.com/

Episode article: Subscribe to the podcast: https://open.spotify.com/show/4jgUESBM1jQpq4Wx2xfou8

@striiminc @enterprisemanagement360 @Bigdataldn

#AIGovernance #AgenticAI #DataPipelines #EnterpriseAI #BigDataLondon #Striim

AI governance, enterprise AI, agentic AI, data pipelines, real-time data, shift-left analytics, AI sovereignty, data governance, Striim, Alok Pareek, Big Data London, tech podcast

Two different teams in an enterprise built two AI agents, but both asked the AI the same question: What are last quarter’s customer stats? Both agents answer promptly and quite assertively. However, the figures on the same inquiry are different. The agents’ answers don’t match. Nothing has broken because neither team is aware of the other’s AI agent.

What happened here is that each agent drew the answer from different contextual data. The business holds two variations of truth delivered with equal confidence.

This is the kind of oblivious failure Angelita Frozza Sanches, Head of Data at Scout24, is working to prevent at the leading German real estate platform where AI agents have already been deployed in production.

In this episode of the Don't Panic It's Just Data podcast, recorded live at Big Data London 2026 in Kensington Olympia, host Mike Ferguson, Managing Director of Intelligent Business Strategies and Conference Chair of Big Data London, sits down with Sanches and Suresh Srinivas, CEO and Co-Founder of Collate and OpenMetadata.

They talk about who now consumes enterprise data, how much of the context layer should be centralised, and why enterprises making progress are the ones that start small and tie every agent to a business outcome.

According to Sanchez, the concerns from 20 years ago to now remain the same, and it comes back to governance. “How people consume, how they interact with data,” remains the same. What’s new is that “AI has changed the velocity and the impact of all those aspects.”

“Now we can take advantage of AI to help us build governance,” Sanchez says. “How can we prepare everything not just for humans but agents–another persona in the game.”

Also Watch: How to Increase Agent Accuracy & Reduce Cost with Context

#AIAgents #DataGovernance #ContextLayer #AgenticAI #OpenMetadata #BigDataLondon #EM360Tech #AIAgents #DataGovernance #ContextLayer #AgenticAI #OpenMetadata #BigDataLondon #EM360Tech

Companies pouring money into AI are seeing only incremental gains, and One Data Chief Executive Officer Dr Andreas Böhm says poor data is a major reason. In a recent episode of the Don't Panic. It's Just Data podcast, Böhm joined the data whisperer Scott Taylor to discuss why SAP Business Warehouse migrations can give enterprises a valuable opportunity to improve the quality and usability of their data.

Böhm, who started his first data company 20 years ago, told host Taylor that the underlying task has not changed through successive waves of big data, data science and machine learning. Organisations still need to get the right data to the algorithms. The algorithms are what have improved.

Böhm said enterprises are spending heavily on AI but are not yet seeing the return on investment (ROI). This is especially true when it comes to large jumps in efficiency, cost savings or product development. In his view, the cause is that data is not being used properly. He identified three factors that determine whether AI is adopted in practice.

• The first is trust. If finance controllers do not believe the data a system produces, for example because it does not match their own spreadsheets, they will not act on it. Böhm questioned how an AI agent could be expected to act on data that people themselves distrust.
• The second is consistency over time. Getting data right once is relatively easy, he said, but keeping quality high day to day is a continuing challenge. He compared it to asking his daughter to tidy her room: it looks fine for a day.
• The third is context. Data accurate enough for a forecast, where a five per cent swing may be acceptable, may not be good enough for a profit and loss statement or regulatory reporting to the US Securities and Exchange Commission.

Taylor noted that AI amplifies the consequences of poor-quality data. The familiar principle of “garbage in, garbage out” becomes a much larger enterprise risk when flawed data is processed and scaled across AI systems. As Böhm put it, the result is “scaled garbage.”

Böhm said SAP BW systems hold decades of financial and operational data, along with business logic buried in ABAP code, mappings and calculation views. That logic covers how sales forecasts are calculated and how controlling processes work, and the people who built it have often left the company. Simply lifting and shifting that content to a new platform carries the old problems with it, the two agreed. Taylor said it amounts to building a faster way to repeat the same mistakes.

Böhm recommended instead starting with a landscape analysis and end-to-end lineage mapping to see what is actually used. Typically, he said, about 50 per cent of legacy objects can be discarded, and reducing scope raises quality on its own. His team then uses an explainer agent to document what past logic did, so new staff and AI agents can understand it.

He described a client with 32 SAP systems, a mix of BW and ERP, that wanted to free up capital by understanding how cash flowed across the group. BW was trusted at the level of individual organisations but not company-wide. One Data translated the BW logic into a modern lakehouse architecture, then built a group-wide model on top of it. The migration took months rather than years, Böhm said, and the improvement in managed cash was visible in the company's profit and loss account.

Böhm cautioned that AI is not a shortcut around the work. Pasting ABAP code into a large language model does not capture logic spread across classes, reusable objects, hidden calls and customer exits. What has changed, he said, is that AI agents can now handle tasks such as migrating code, quality checks and fixing syntax errors, which would have been far harder four years ago. He dismissed the idea that LLMs remove the need to clean data. The funding angle matters too. Migration spending is already required, Böhm noted, so it can double as the investment in data management that organisations often struggle to secure.

Böhm said governance after migration should follow a data product approach, built on several practices:

• Ownership: Someone must understand and be responsible for the data, or it will not stay clean.
• Readability: Business users need understandable column and table names, not cryptic technical labels.
• Business validation: Checks should go beyond technical rules such as "revenue must be a number" to business rules, such as whether negative revenue is valid because of returns.
• Focus: Not everything needs governing. Priorities should be core entities such as customers, revenues, purchase orders and product lifecycles.

Taylor described governance as a lifestyle rather than a project. Böhm added that it remains a people business, in which data owners take pride in their work and promote it internally.

His closing advice to organisations preparing SAP BW data for AI was to use the migration as a chance to clean up the data landscape and to apply the budget to making at least one or two areas AI-ready. If you would like to find out more, visit onedata.ai or follow Dr Andreas Böhm on LinkedIn.

• The recurring hype cycles in AI and the importance of data quality.
• The critical role of trust, context, and governance in AI adoption.
• Transforming legacy SAP BW systems into modern data architectures.
• Strategies for cleaning up and understanding enterprise data landscapes.
• The significance of data ownership, understanding, and management in data governance.

What happens when an enterprise data agent asks you: “What is our churn this quarter?” The AI agent has access to the warehouse, the tables, and the query engine. It carries out the necessary calculations. However, the answer it provides turns out to be inaccurate. Why?

It’s not because the model is inadequate. It’s because it didn’t know which definition of the term 'churn' the business had in mind, which table was out of date, or which join resulted in customers being counted twice. When you apply this situation to all the questions your enterprise directs at AI, you begin to understand why so many agent projects come to a halt at the demo stage.

In this episode of the Don't Panic, It's Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B journalist at EM360Tech, sits down with Suresh Srinivas, the CEO and Co-Founder of Collate and also a Co-Founder of the open-source project OpenMetadata. They talk about the context layer and the reasons why AI agents fail when it comes to enterprise data, even though the underlying models are continually getting better. He explains why the solution must be an open context layer rather than a proprietary one.

Srinivas cites a use case to Dua to explain how the solution can be deployed at scale. He talks about OpenAI's own internal data agent, Kepler, which is used by more than 3,500 people and carries out reasoning across about 70,000 datasets and over 600 petabytes of data, as proof that the issue is not particular to Collate's customers.

OpenAI developed a seven-layered context system based on OpenMetadata, and is one of its largest users as well as a major open source contributor. Srinivas maintains that even though there are seven layers, they all come down to three basic elements: the context of the data (what exists), the ontology and semantics (what it means, expressed in business terms such as "revenue" or "churn"), and memory (the corrections and feedback that the agent learns from).

Srinivas states that after OpenAI had invested in the context layer, query performance on Kepler decreased from 22 minutes to 90 seconds, representing a roughly 16-fold improvement, together with improvements in token efficiency.

Anthropic too has published its own research on the importance of context layers for data agents, which shows that there is convergence among the leading research labs, not merely a point made by Collate.

• McKinsey: fewer than 10 per cent of enterprises have scaled AI agents to value
• 80 per cent of enterprises cite data limitations as the scaling barrier
• On the Spider 2.0 benchmark, the top models achieved only 59 per cent accuracy when tested on enterprise data.
• Collate's context layer raised accuracy from 59 per cent to 94 per cent on the same benchmark
• That's an 86% reduction in wrong answers from context alone, not a bigger model
• Token spend fell 75 per cent once a context layer was added
• Database queries per question dropped from 190 to 27, an 86 per cent reduction in compute
• OpenAI's Kepler agent spans 70,000 datasets and 600+ petabytes, built on OpenMetadata
• OpenAI's query performance improved from 22 minutes to 90 seconds with context
• Context layer reduces to three primitives: data context, ontology/semantics, memory
• OpenMetadata has 15,000+ community members and 4,000 production deployments
• Collate curates context per persona rather than serving the full context to every agent
• Collate's AI Governance Studio audits what agents access and the risk they pose

• 00:00 Introduction to AI and Data Context Challenges
• 02:16 The Root Cause of Data Problems: Missing Context
• 04:20 Evolving Definition of Context in AI and Data
• 06:52 Why Data Limitations Still Hinder AI Scaling
• 07:22 The Impact of Context on AI Model Accuracy and Cost
• 09:21 How Providing Context Improves AI Performance
• 11:02 Verifying Data Accuracy in a Constantly Changing Enterprise
• 12:25 AI Agents and Continuous Data Quality Management
• 14:25 OpenAI's Use of Context Layers for Better AI Performance
• 16:03 Avoiding Noise and Cost in Context Management
• 18:47 The Role of Persona-Based Context Curation
• 19:45 Open Source as a Foundation for Context Layers
• 21:37 Accountability for Incorrect or Stale Context
• 22:24 Collaborative Role of Data Teams and AI Agents in Context Management
• 24:01 Guardrails and Deterministic Answers in Enterprise AI
• 25:53 Reducing Token Consumption with Context Layers
• 28:47 Key Takeaway: Building a Robust Context Layer for AI
• 30:37 Upcoming Industry Discussions and Challenges at Big Data London
• 32:28 The Future of AI Agents and the Importance of Context in Data Strategy
• 33:12 Closing Remarks and Next Steps for AI and Data Leaders

@CollateData @enterprisemanagement360

#AIagents #EnterpriseAI #DataGovernance #OpenMetadata #ContextLayer #AIaccuracy #AgenticAI #CIO #DataStrategy #EM360Tech #DontPanicItsJustData #opensourcesoftware #opensourceai

AI agents, enterprise AI, CIO, CTO, IT leadership, data governance, AI governance, context layer, OpenMetadata, data strategy, agentic AI, digital transformation, tech podcast, enterprise technology

Envision a fraud detection model embedded in a bank's AI system, continuously monitoring transactions for unusual activity. The warehouse behind it updates itself every 15 minutes; however, that seems like a short interval. A fraudulent transaction took place 13 minutes ago, and the model still hasn't picked it up.

The thing is, the model is not faulty. The data being fed to it had already overlooked the critical moment. But how?

In this episode of the Don't Panic! It's Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sat down with guest Sundar Nathan, VP of Product Marketing, Enablement and Academy at Striim. They discuss the gap that exists between the time an event takes place in a production system and the time an AI agent becomes aware of it.

As the frontier models from companies such as OpenAI and Anthropic become more commonplace, the model ceases to be a source of competitive advantage for any enterprise. What remains then is the data beneath the model and the speed at which that data actually moves.

“I'd go even a step further and state that in 18 months the model you select will be less important to you than the data which is feeding that model,” Nathan said.

Alluding to his personal experience, Nathan tells Dua that his "superpower was memory,” particularly, the ability to remember circuit diagrams and organic chemistry structures as clearly as in a photograph when taking an exam. But with the introduction of Google search, that memory ceased to be an advantage since everyone had the same tool at their disposal.

Why is that experience relevant? It’s similar to the use of AI in the enterprise today. "Your competitor next door has access to the same frontier models at the same price, roughly on the same day," he says.

According to the Striim Marketing VP, what a competitor cannot rent is an enterprise’s operational data, its business insights and "the institutional knowledge accumulated over decades", in particular, how close an agent's view of production data is to the actual data.

"The time gap between the agent and where your production data is measured is the new source of competition," he adds.

Also Read Use Case: UPS Leverages Striim and Google BigQuery for AI-Secured Package Delivery

Read Use Case: The Hyper-Responsive Payments Organisation

• The model matters less than the data feeding it within 18 months, Viswanathan predicts.
• Four Cs of AI-ready data: current, consistent, consumable, contextual.
• Only 23% of enterprises scale AI agents past pilot stage (McKinsey).
• Change data capture reads transaction logs directly, in real time.
• Governance built for non-human users masks PII/PHI before agents see it.

00:00 - Why AI advantage is moving from models to real-time data

02:06 - Sundar’s path from memory-based learning to simplifying complex tech

04:10 - Why “everyone is renting the same brain”

06:52 - Why impressive pilots fail in regulated production workflows

08:30 - The four audit questions that block deployment

10:43 - Why 15-minute refreshes are not real time for fraud

13:25 - Why enterprises need a nervous system, not a warehouse scan

15:52 - How consumable data turns raw events into agent-ready context

18:29 - What Stream’s intelligent pipe does in practice

21:12 - Governance for non-human users and mission-critical guarantees

22:26 - Stop benchmarking models and start benchmarking data staleness

25:45 - Final takeaway: it really is just data, but it must move faster

Two teams pitch the same figure for ‘active customers’ right before a leadership meeting. One dashboard displays 40,000 while the other dashboard displays 52,000. No mistakes were detected by the teams; however, the difference in the stats is due to one team or employee deploying a different data filter and the other using another join path.

Now there's an AI agent involved, who is simply asked to 'retrieve the most recent number of active customers'. Not realising that there are two versions, it selects one, states that figure with complete confidence, and then goes about its business. In that moment, the semantic gap ceases to be merely an internal discussion and becomes a business risk.

On this episode of the Don't Panic! It's Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, is joined by Ananya Devraju, a Business Intelligence Developer at Premier Inn, to discuss the semantic gap and how AI is rendering an old data problem. They look at the situation in which metric definitions are spread across five different dashboards rather than kept in a single central location, and explain why an AI agent can produce a perfectly valid SQL query yet return a completely incorrect answer.

The problem, consistent even with AI in the picture, is governance. AI has just made the issue of governance more apparent. “It was never a data quality problem, but it's a governance of meaning problem, and it's been sitting there long before AI showed up. AI has just made it louder now,” Devraju tells Dua.

Devraju will be presenting a talk at Big Data LDN (BDL) this year on Thursday, September 24, on When Migrations Break Your Metrics: Rebuilding Data for Commercial Analytics. The talk is scheduled for 3:20 pm to 3:50 pm in the Data Architecture Modernisation Theatre.

• Semantic gaps existed before AI; AI has only made them louder and faster.
• The same metric is displayed on different dashboards, each using different date logic, filters, or join paths.
• There can be five different definitions of an "active user" across the five dashboards, all of which are incompatible.
• SQL can be syntactically correct and yet provide the wrong answer to a business question.
• Validation should be placed at the definition level, not at the SQL level.
• AI agents can query the raw staging tables rather than referring to the governed marts by name alone.
• Incorrect joints or the wrong grain result in numbers that look plausible but are actually wrong.
• According to dbt Labs research, 72 per cent of peopleprioritise AI-assisted coding while 24 per cent prioritise validation.
• Incorrect data is more dangerous than having no data; it is the confident errors that pose a risk.
• Since end users are unable to check the answers at the time they occur, trust has to be established earlier on.
• Provide a semantic layer that is governed and has version control, with designated owners for each metric.
• Ad hoc SQL statements on no side-channel should generate the "official" figures that are reported.

• 00:00 Introduction to the Semantic Gap in AI
• 02:56 Understanding Data Interpretation and Governance
• 05:56 The Importance of Centralized Definitions
• 09:05 AI Hallucinations and Data Validity
• 12:02 Navigating Governance in AI
• 14:56 The Role of AI in Data Validation
• 17:56 Final Thoughts on AI and Data Meaning

Ananya Devraju — Business Intelligence Developer, Premier Inn

Shubhangi Dua — Podcast Producer & B2B Tech Journalist, EM360Tech

Listen to more episodes of Don't Panic! It's Just Data on EM360Tech.

#SemanticGap #AIAgents #DataGovernance #DataAnalytics #SemanticLayer #AIData #BusinessIntelligence #DataEngineering #EnterpriseAI #DataQuality

While AI-generated code, also known as vibe coding, is aiding developers in releasing software faster than ever, it seems to be simultaneously causing a greater number of production failures.

According to research carried out by New Relic, 94% of technology leaders think that AI-generated code is of higher quality when it is reviewed, but 78% say they are experiencing more incidents after deployment. Let’s explore why this is happening.

In the recent episode of the Don't Panic It's Just Data podcast, host Christina Stathopoulos, Founder of Dare to Data, was joined by Nic Benders, Chief Technology Strategist at New Relic to talk about "agent debt,” what happens when AI writes great code that breaks in production, and why SREs (Site Reliability Engineers) are left cleaning it up.

Benders states that the issue does not lie with AI itself but rather with a growing phenomenon that he refers to as "agent debt".

After gathering telemetry data, New Relic discovered that the core issues stem from Large Language Models (LLMs). These problems date back to the early days of ChatGPT, when the models were remarkably convincing at generating software code.

“The code looks really good, but it doesn't work in production. It creates incidents down the road,” he tells Stathopoulos.

This is where agent debt comes in.

• AI-generated code can increase production incidents despite passing code reviews.
• 'Agent debt' is emerging as the AI-era equivalent of technical debt.
• Observability must extend beyond applications to AI development workflows.
• Engineering teams should measure AI value—not just token consumption.
• AI may deliver greater value reviewing code than writing it.
• SRE agents will compete on data quality, not model intelligence.
• Human judgement remains essential for high-risk engineering decisions.

• 00:00 Introduction to the episode and guest
• 00:30 Overview of Nick Benders and New Relic's mission
• 01:14 Impact of AI on software development speed and review process
• 02:55 Shift from coding to downstream operations and role changes
• 06:31 Discrepancy between AI-rated quality and incident reports
• 08:08 Understanding agent debt and its signs
• 09:35 The role of observability in AI-generated code in production
• 12:22 Measuring and understanding agent debt through observability
• 14:10 Rethinking the AI software development lifecycle
• 17:30 Automating code review and process checks with AI
• 20:18 Production debugging and the role of AI in incident management
• 21:09 Use case of SRE agents in production environments
• 24:00 Future trends in SRE agents and data focus
• 25:27 Key advice for using AI tools effectively in software and operations

For more information on SRE and agent debt, visit newrelic.com and em360tech.com.

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For a long time, financial crime prevention has been a massive issue for banks. This is because of several often complex navigation around rules and regulations, such as AML, KYC, CTF, and SAR. However, a bigger issue has emerged. As more and more transactions happen and financial crime networks get smarter, the bigger problem is the way banks handle data.

In the recent conversation on the Don’t Panic It’s Just Data podcast, host Herb Blecher, Research Director, Data and Analytics, Enterprise Management Associates (EMA), sat down with guest Manish Andhy, Financial Services AI & Industry Executive at Teradata.

They talked about how criminal enterprises are changing fast, like the internet, while compliance systems are still using old methods. Andhy tells Blecher, "The difference between how fast criminals can change and how slow compliance systems are is a big weakness."

More than $3 trillion in money moves through the global financial system every year, but only a small part of it is caught. Banks have spent a lot of time and money building systems to stop money laundering. Many of them still have separate systems for onboarding, transaction monitoring, investigations and risk management.

The strange thing is that big financial institutions have already spent a lot of money to solve this problem. They built data warehouses to get rid of systems and data lakes to put all the information in one place. In fact, they often created the same separate systems they were trying to get rid of.

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• Over $3 trillion of illicit funds moves through the global financial system annually.
• Only about 1% of illicit funds are detected or intercepted.
• AML programs have grown in a fragmented, siloed way across enterprises.
• The pace of change in compliance has historically been slow.
• Traditional rule-based systems generate enormous amounts of false positive alerts.
• Data silos persist due to organizational and technology issues.
• Data products allow for a unified view of data without creating new silos.
• Generative AI can automate parts of the compliance process.
• Financial institutions must treat data as a strategic asset.
• Compliance can inform broader business decisions beyond regulatory obligations.

• 00:00 Introduction to Financial Crime and Data Analytics
• 05:06 Challenges in Financial Crime Compliance
• 10:05 Data Silos and Their Impact
• 15:12 The Concept of Data Products
• 19:57 Modernizing Financial Crime Strategies
• 24:59 Conclusion and Key Takeaways

For more information on financial crime and how financial institutions should manage their data securely and compliantly, follow Terada across its official channels:

• Website: Teradata
• YouTube: @Teradata
• LinkedIn: @Teradata
• X: @Teradata

For more information on enterprise tech analyst-led insights, please visit em360tech.com

• EM360Tech YouTube: @enterprisemanagement360
• EM360Tech LinkedIn: @EM360Tech
• EM360Tech X: @EM360Tech

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