Don't Panic! It's Just Data

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.Key TakeawaysBuying 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.Chapters00:07 – Welcome and introductions00:48 – How has Big Data London been?02:32 – From generative AI to agents to context04:33 – The biggest challenge: getting the plumbing right05:49 – The four C's (and a fifth)07:31 – The Advancing Analytics and Databricks partnership09:52 – Customer story: LakeForge and Genie Code11:22 – The shift CEOs aren't watching15:08 – ROI versus the cost of inaction16:44 – What the tech industry gets wrong about AI18:38 – If your AI agent had a personality20:02 – One takeaway for CDOs21:30 – Closing and where to find the guestsFor 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?Key TakeawaysAgents 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.Chapters03:13 Welcome and Alok Pareek's background06:17 What the agentic era demands of data pipelines14:34 Moving data versus intelligent pipelines18:40 Shift-left analytics20:49 Data and AI sovereignty25:05 Why zero-copy ETL falls short28:51 UPS Defense Delivery and real-time risk scoring31:26 The four CsMORE 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 #StriimAI 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.Why AI Projects Stall Without Trusted DataBöhm 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.”Why BW Migration Is an Opening for AI ReadinessBöhm 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.How to Keep Migrated Data Trusted After Go-LiveBöhm 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.TakeawaysThe 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.Also Read: OpenAI's self-service AI data agent, built on OpenMetadataWhat OpenAI's internal data agent shows about scaleSrinivas 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.Key TakeawaysMcKinsey: fewer than 10 per cent of enterprises have scaled AI agents to value80 per cent of enterprises cite data limitations as the scaling barrierOn 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 benchmarkThat's an 86% reduction in wrong answers from context alone, not a bigger modelToken spend fell 75 per cent once a context layer was addedDatabase queries per question dropped from 190 to 27, an 86 per cent reduction in computeOpenAI's Kepler agent spans 70,000 datasets and 600+ petabytes, built on OpenMetadataOpenAI's query performance improved from 22 minutes to 90 seconds with contextContext layer reduces to three primitives: data context, ontology/semantics, memoryOpenMetadata has 15,000+ community members and 4,000 production deploymentsCollate curates context per persona rather than serving the full context to every agentCollate's AI Governance Studio audits what agents access and the risk they poseChapters00:00 Introduction to AI and Data Context Challenges02:16 The Root Cause of Data Problems: Missing Context04:20 Evolving Definition of Context in AI and Data06:52 Why Data Limitations Still Hinder AI Scaling07:22 The Impact of Context on AI Model Accuracy and Cost09:21 How Providing Context Improves AI Performance11:02 Verifying Data Accuracy in a Constantly Changing Enterprise12:25 AI Agents and Continuous Data Quality Management14:25 OpenAI's Use of Context Layers for Better AI Performance16:03 Avoiding Noise and Cost in Context Management18:47 The Role of Persona-Based Context Curation19:45 Open Source as a Foundation for Context Layers21:37 Accountability for Incorrect or Stale Context22:24 Collaborative Role of Data Teams and AI Agents in Context Management24:01 Guardrails and Deterministic Answers in Enterprise AI25:53 Reducing Token Consumption with Context Layers28:47 Key Takeaway: Building a Robust Context Layer for AI30:37 Upcoming Industry Discussions and Challenges at Big Data London32:28 The Future of AI Agents and the Importance of Context in Data Strategy33:12 Closing Remarks and Next Steps for AI and Data Leaders @CollateData @enterprisemanagement360 #AIagents #EnterpriseAI #DataGovernance #OpenMetadata #ContextLayer #AIaccuracy #AgenticAI #CIO #DataStrategy #EM360Tech #DontPanicItsJustData #opensourcesoftware #opensourceaiAI agents, enterprise AI, CIO, CTO, IT leadership, data governance, AI governance, context layer, OpenMetadata, data strategy, agentic AI, digital transformation, tech podcast, enterprise technology