『Don't Panic! It's Just Data』のカバーアート

Don't Panic! It's Just Data

Don't Panic! It's Just Data

著者： EM360Tech
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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.Copyright 2026 EM360Tech マネジメント マネジメント・リーダーシップ 経済学
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  • How SAP BW Migration Prepares Enterprises for AI

    How SAP BW Migration Prepares Enterprises for AI
    2026/10/09
    Companies pouring money into AI are seeing only incremental gains, and One Data Chief Executive 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 ...
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    26 分
  • How to Increase Agent Accuracy & Reduce Cost with Context

    How to Increase Agent Accuracy & Reduce Cost with Context
    2026/09/22
    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, ...
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    32 分
  • Can AI Agents Make Decisions Without Real-Time Enterprise Data?

    Can AI Agents Make Decisions Without Real-Time Enterprise Data?
    2026/09/21
    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 DeliveryRead Use Case: The Hyper-Responsive Payments OrganisationTakeawaysThe 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.Chapters00:00 - Why AI advantage is moving from models to real-time data02:06 - Sundar’s path from memory-based learning to simplifying complex tech04:10 - Why “everyone is renting the same brain”06:52 - Why impressive pilots fail in regulated production workflows08:30 - The four audit questions that block deployment10:43 - Why 15-minute refreshes are not real time for fraud13:25 - Why enterprises need a nervous system, not a warehouse scan15:52 - How consumable data turns raw events into agent-ready context18:29 - What Stream’s intelligent pipe does in practice21:12 - Governance for non-human users and mission-critical guarantees22:26 - Stop benchmarking models and start benchmarking data staleness25:45 - Final takeaway: it really is just data, but it must move faster
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    27 分
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