Don't Panic! It's Just Data

About Don't Panic! It's Just Data

Not only do many businesses have more data than they know what to do with, but they also often struggle to gain insights from some of the most valuable data in their possession, leading to many of their crucial data assets going unused. Whether it's issues with data quality, visualization, or management, getting lost in the sea of enterprise data at your possession can make it impossible to make smart, data-driven decisions that improve your business. The "Don't Panic! It's Just Data" podcast delves deep into the power of enterprise data. From groundbreaking vendor solutions to expert-backed best practices for making the most of your data assets, join us as we gather insights from leading tech vendors and professionals who depend on data daily.

Publisher

EM360Tech

Category

business · business · technology

Language

en

Explicit

No

First episode

20 Apr 2017

Latest episode

20 Oct 2026

Latest episodes

182 episodes in the feed.

1. 

   20 Oct 2026

   Can AI Agents Be Trusted With Enterprise Data? | Big Data LDN Special

   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 (https://de.linkedin.com/in/angelita-frozza-sanches), 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 (https://em360tech.com/podcasts/dont-panic-its-just-data) podcast (https://em360tech.com/podcasts/dont-panic-its-just-data), recorded live at Big Data London 2026 (https://em360tech.com/tech-articles/highlights-big-data-ldn-2026) in Kensington Olympia, host Mike Ferguson, Managing Director of Intelligent Business Strategies and Conference Chair of Big Data London (https://www.linkedin.com/in/mikeferguson3/), sits down with Sanches and Suresh Srinivas, CEO and Co-Founder of Collate and OpenMetadata (https://www.linkedin.com/in/sureshsri). 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 (https://em360tech.com/podcasts/ai-context-layer-collate-boosted-ai-accuracy) #AIAgents #DataGovernance #ContextLayer #AgenticAI #OpenMetadata #BigDataLondon #EM360Tech #AIAgents #DataGovernance #ContextLayer #AgenticAI #OpenMetadata #BigDataLondon #EM360Tech

2. 

   9 Oct 2026

   How SAP BW Migration Prepares Enterprises for AI

   Companies pouring money into AI are seeing only incremental gains, and One Data Chief Executive Officer Dr Andreas Böhm (http://linkedin.com/in/dr-andreas-b%C3%B6hm-90286693?originalSubdomain=de) says poor data is a major reason. In a recent episode of the Don't Panic. It's Just Data (https://em360tech.com/podcasts/dont-panic-its-just-data) podcast (https://em360tech.com/podcasts/dont-panic-its-just-data), Böhm joined the data whisperer Scott Taylor (https://www.linkedin.com/in/scottmztaylor/) 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 Data 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.” Why BW Migration Is an Opening for AI Readiness 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. How to Keep Migrated Data Trusted After Go-Live 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 (http://onedata.ai/) or follow Dr Andreas Böhm (http://linkedin.com/in/dr-andreas-b%C3%B6hm-90286693?originalSubdomain=de) on LinkedIn. Takeaways 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.

3. 

   22 Sept 2026

   How to Increase Agent Accuracy & Reduce Cost with Context

   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 (https://em360tech.com/podcasts/dont-panic-its-just-data) (https://em360tech.com/podcasts/dont-panic-its-just-data)podcast (https://em360tech.com/podcasts/dont-panic-its-just-data), host Shubhangi Dua, Podcast Producer and B2B journalist at (https://www.linkedin.com/in/shubhangi-dua-14a825154/)EM360Tech (https://www.linkedin.com/in/shubhangi-dua-14a825154/), sits down with Suresh Srinivas, the CEO and Co-Founder of Collate and also a Co-Founder of the open-source project OpenMetadata (https://www.linkedin.com/in/sureshsri/). 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 (https://open-metadata.org/) rather than a proprietary one. Also Read: OpenAI's self-service AI data agent, built on OpenMetadata (https://em360tech.com/whitepapers/openais-self-service-ai-data-agent-built-openmetadata) What OpenAI's internal data agent shows about scale 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 (https://community.openai.com/t/deep-dive-questions-on-keplers-rag-and-sql-generation-mechanisms/1373022), 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 (https://em360tech.com/whitepapers/openais-self-service-ai-data-agent-built-openmetadata) 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 Takeaways 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 Chapters 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 (https://studio.youtube.com/channel/UCZSvE7AOCvOTEGiANrXAIUg) @enterprisemanagement360 (https://studio.youtube.com/channel/UCNCS0CL4v38JWbNaqnn0G4w) #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

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