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.

Don't Panic! It's Just Data is a technology podcast hosted by EM360Tech, with 182 episodes on record and a Required Pod Score of 80.

EM360Tech hosts Don't Panic! It's Just Data, a technology show with 182 episodes published.

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  Episode #74

  How to Increase Agent Accuracy & Reduce Cost with Context

  Sep 22, 202632 minS1

  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 It’s because it didn’t know which definition of the term 'churn' the business had in mind, which table was out of date, or which join resulted in customers being counted twice.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 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 , 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 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 @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

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  Episode #73

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

  Sep 21, 202627 minS1

  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. 'Everybody Is Renting The Same Brain' 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 Takeaways 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. Chapters 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

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  Episode #72

  What Happens When Your AI Agent Finds Two “Correct” Answers?

  Sep 2, 202622 minS1

  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. Takeaways 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. Chapters 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 Guest Ananya Devraju — Business Intelligence Developer, Premier Inn Host 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

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  Episode #71

  What Is "Agent Debt" and Why Is It Breaking Your AI Code?

  Aug 20, 202627 minS1

  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. Takeaways 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. Chapters 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. #AgentDebt #AICodingAgents #AIGeneratedCode #AIObservability #SREAgents agent debt, agent debt explained, agent debt in software development, AI-generated code failures, AI-generated code incidents, AI coding agents, AI coding agents in production, autonomous coding agents, AI code reliability, AI software reliability, AI technical debt, AI-generated software, AI-generated software observability, observability for AI-generated code, AI observability, software observability, production reliability, production incidents, AI incident management, AI-assisted incident response, SRE agents, AI SRE agents, site reliability engineering, AI code review, AI production readiness, AI developer tools, enterprise software development, LLM-generated code, large language model code, New Relic, Nic Benders

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  Episode #70

  How Does a Unified Data Platform Improve Financial Crime Compliance?

  Jul 28, 202625 minS1

  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. Also Watch: What is AI Hyper-Personalisation in CX and Why Does it Matter? Takeaways 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. Chapters 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 #Teradata #FinancialCrime #AML #Compliance #DataStrategy #AIinBanking #FinTech #DataSilos #DataManagement #DonTPanicItsJustData #EM360Tech #B2BMarketing #B2BPodcasts

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