Don't Panic! It's Just Data

Podcast: Don’t Panic! It’s Just Data Guest: Jignesh Patel, Director of Product Strategy at Stibo Systems and Elsebeth Gundersen Jensen, Product Owner at NetsHost: Dr Joe Perez, Data Analytics Expert and Amazon Bestselling AuthorWe’re living in times of an always-on digital economy where there’s no room for data errors. In the recent episode of the Don’t Panic It’s Just Data podcast, host Dr Joe Perez, Data Analytics Expert and Amazon Bestselling Author, sat down with Jignesh Patel, Director of Product Strategy at Stibo Systems and Stibo Systems’ customer, Elsebeth Gundersen Jensen, Product Owner at Nets. Perez pointed out that even the smallest inconsistency can "ripple completely across an entire operation, instantaneously." This reality is prompting enterprise tech leaders to rethink how they manage, govern, and use data, especially with the rapid growth of AI adoption.Overall, the guests send out a clear message – trusted, real-time data is now a crucial part of business infrastructure.Also Watch: From Chaos to Launch: Your Product is Ready, Your Data Isn'tWhat is the Hidden Cost of Untrusted Data?For large enterprises, especially those growing through mergers and acquisitions, fragmented data systems are almost unavoidable. Jensen noted that when combining multiple customer portfolios, inconsistencies often arise in even the simplest fields, like organisation numbers formatted differently in various systems.“When you bring in different customer portfolios, you will also get this scattered data picture that you don’t want in a master data management system,” she explained.According to Patel, the lack of trusted data impacts four key areas which includes customer experience, revenue growth, decision-making, and operational efficiency. Without a unified customer view, enterprises struggle to offer personalised experiences or spot cross-sell opportunities. Moreover, analytics based on unreliable data undermine executive confidence and increase compliance risks.These issues are made worse by speed. Alluding to her observations, Jensen told Perez and Patel that modern customers expect contract changes or service interactions to be updated almost instantly. “They don’t want to wait a day,” she stated. “Everything should be faster, better, and accurate.”Also Watch: Why is a Customer Data Strategy a Competitive Edge?How are Enterprises Mastering Intelligence?Traditionally, Master Data Management (MDM) has focused on creating the “golden record,” a single, reliable version of key business entities like customers or products. While this remains important, Patel believes this idea is changing quickly in the AI era.“MDM is moving beyond data correctness towards what I call mastering intelligence,” he said. “AI systems rely on trusted context—understanding what entities are, how they relate, and the business rules that apply.”This change is part of a larger transformation in enterprise architecture. Decision-making is no longer limited to human-driven dashboards; it is increasingly spreading across applications, analytics platforms, and AI agents acting in real time. In such a setup, inconsistent data does not just create errors but it can amplify it.“AI doesn’t eliminate the need for MDM or data governance. It emphasises it,” stated Patel. For enterprises heavily investing in AI, this insight is vital. Without a strong data foundation, AI models might provide insights but not dependable results.As enterprises move toward AI-driven and even agent-based business models, the need for trusted data will grow even more important. Patel highlights new questions from the C-suite – How will AI agents find my products? Why isn’t my business being recommended?The answer increasingly depends on structured, high-quality data. “AI success is dependent on trustworthy data,” Director of Product Strategy at Stibo Systems says. “MDM and governance are the foundation for the next generation of intelligent business systems.”For enterprise leaders, the key directive to note is in the race to implement AI, data trust is the competitive edge and not only the requirement. Key TakeawaysReal-time trusted data is essential for enterprise AI success and operational resilience.Poor data quality directly impacts customer experience, revenue growth, and compliance.Modern Master Data Management (MDM) is evolving from “golden records” to AI-ready data intelligence.Proactive data governance must replace reactive data cleanup to scale in real-time environments.A unified data model is the foundation for accurate, consistent, and AI-driven business insights.Chapters00:00 Introduction to Data Governance and MDM02:06 The Shift to Real-Time Data05:27 Business Risks of Lacking Trusted Data08:20 Growth Through Mergers and Acquisitions15:29 The Role of MDM in AI Initiatives20:02 Transitioning to Proactive Data Management22:01 Advice for CIOs on Managing Product DataFor more information, please visit em360tech.com and stibosystems.com. To learn more about AI in the MDM space and how they’re progressing enterprise analytics intelligently, follow:Stibo Systems LinkedIn: @StiboSystemsStibo Systems X: @StiboSystemsStibo Systems YouTube: @StiboSystemsGlobalEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFollow: @EM360Tech on YouTube, LinkedIn and X#MDM #DataGovernance #EnterpriseAI #DataQuality #TrustedData #AIStrategy #RealTimeData #DigitalTransformation #StiboSystems #TechPodcast

As AI becomes a central pillar of business decision-making, enterprises face a new challenge, and that is making their data AI-ready. It’s no longer enough to collect and digitise information. For organisations, data must be structured, contextualised, discoverable, and usable—both by humans and intelligent systems.AI can only deliver if your data is truly ready, but most enterprises are drowning in fragmented, incomplete, or slow-to-update data. In this episode of Don't Panic, It's Just Data, host Doug Laney and Sushant Rai, Vice President of Product of AI and Data Strategy at Relito, explore how modern data unification strategies are changing enterprises, enabling AI to deliver faster, more reliable insights. They focus on the shift from traditional Master Data Management (MDM) to next-generation AI-ready data cores, uncovering the risks of fragmented data and the strategies to overcome them.Why AI-Ready Data MattersAI, especially large language models (LLMs), is changing how people interact with data. Analysts, executives, and frontline teams now expect natural language queries and instant, actionable insights.Sushant explains:"AI performs at its best when it has full context, empowered with the right data. This allows AI agents to make decisions and take actions on behalf of your business."When you embed intelligence into your data layer, AI can help you manage and scale your data without drowning your teams in manual work. This will only work if your data is structured, clean, governed, and constantly updated, everything that makes it truly AI-ready.The Data Scale ChallengeThe volume of data being turned over daily is staggering. As Sushant notes:"The amount of data getting generated every single day is so massive that there’s no way to keep up without AI. Even the largest organizations, with massive data stewardship teams, can’t catch up manually."This gap is driving the change in the modern data platforms, where AI automates stewardship, enriches data continuously, detects anomalies, and maintains quality in real time.Want to learn more about modern data unification and AI-ready platforms? Visit Reltio.com for insights, resources, and case studies.TakeawaysData unification provides a trusted, real-time view of key business elements.Organizations must balance speed and trust in data management.Classic MDM is evolving into modern data unification platforms.Real-time data access is crucial for AI and analytics.AI can enhance data quality and governance processes.Successful data initiatives require clear business outcomes and ownership.Data unification should be viewed as a business platform, not just an IT project.AI agents will play a significant role in automating data governance.Organizations need to focus on both structured and unstructured data.The future of data management involves continuous unification and enrichment of data.Chapters00:00 Introduction to Data Unification and AI07:52 The Importance of Data Unification in Enterprises15:44 AI and Data Quality Management23:20 Organizational Success Factors for Data Initiatives25:16 Future Trends in Data and AIAbout ReltioAt Reltio, we believe data should fuel your success in the enterprise AI era. Reltio Data Cloud™ is the agentic data fabric for the enterprise—powering real-time data intelligence and AI transformation. Reltio’s cloud-native SaaS platform delivers unified, trusted, and context-rich data across domains in real time. With Reltio, organizations gain 360-degree views of customers, products, suppliers, and more—mobilized in milliseconds to any application, user, or AI agent. Trusted by the world’s largest enterprises across life sciences, financial services, healthcare, technology, and more, we help organizations fuel frictionless operations, drive innovation, and reduce risk.

The challenge all organisations, big and small, face is answering and implementing solutions to solve this key question: How can finance and accounting teams work faster, smarter and more accurately?In the recent episode of the Don’t Panic It’s Just Data podcast, host Scott Taylor, The Data Whisperer and Principal Consultant at MetaMeta Consulting, speaks with Kevin Gibson, CPA and Principal Solutions Engineer at insightsoftware. They talk about the constantly changing nature of financial reporting. Additionally, they discuss the pros and cons of modern financial reporting and the importance of connecting financial data with familiar tools like Excel. The conversation also touches on the future of financial reporting technology and the need for organisations to adapt to changing data access needs.Uncertainty in a Data-Driven World“With all this uncertainty, companies are being asked to look at their data in different ways. They want to pivot it, slice it, and dice it,” Gibson tells Taylor, encapsulating the theme of this episode. “They’re being told to do more with the data — what does it mean, how do we read it, how do we understand it, how do we analyse it?”The issue is that, as enterprises invest in digital transformation, finance teams struggle most with limited access to the data they need to support their analysis.“The ideal state,” Gibson adds, “would be: I can get what I want, when I want, and how I want it — without asking questions. But let’s be honest — that doesn’t exist today.”However, the good news is the data exists, Gibson says. The ugly part is that organisations can’t get to it. Many of the data accessibility issues have been attributed to cloud migration. “When you move your data to the cloud, you think: it’s cheaper, it’s more secure, it’s easier to maintain. But here’s the problem: you don’t control it anymore. Some cloud providers make access difficult or costly. So finance teams feel stuck,” he explains.Also Watch: Stop Fighting Excel: How to Turn Your Spreadsheets into a Real-Time Reporting Powerhouse?Real-Time Access on ExcelFor decades, finance professionals have relied on Excel, which Gibson refers to as the “largest data warehouse in the world.” “There are 1.1 billion users of Excel today,” he says. “And let’s be honest, I haven’t met an accountant yet who says they hate it.” Finance prospers in Excel, but IT often views it as a risk. This leads to a constant back-and-forth between usability and control. Gibson believes that the solution is to equip both sides- finance and IT with real-time, governed data inside Excel. That’s where insightsoftware comes in. “We can connect directly to these systems and give finance teams back their real-time access — not just to pieces of data, but all of it,” says Gibson. “Literally every piece of data can be accessed.” With tools like Spreadsheet Server, finance professionals can work in Excel — their “comfort food,” as Gibson calls it — while drawing directly from live ERP data in the cloud. “We give them insight — that’s what our software does. It gives them visibility into their data. Excel isn’t going away, and our job is to make it work even better.”To learn more, watch or listen to the podcast on EM360Tech.Also Watch: Struggling with ERP Data? How to Get Real-Time Reporting in ExcelTakeawaysFinance professionals are facing increased pressure to analyse data amidst uncertainty.The ideal state for finance teams is immediate access to data without barriers.Modern financial reporting has its good, bad, and ugly aspects, primarily revolving around data accessibility.Different industries have unique data needs and KPIs that impact financial reporting.Excel remains a critical tool for finance professionals despite the rise of cloud-based solutions.Organisations must find ways to connect their financial data with familiar reporting tools.The future of financial reporting will continue to evolve around Excel and data accessibility.Companies are beginning to realise the limitations of cloud-based systems and may revert to on-prem solutions.AI is emerging as a significant factor in data access and reporting.C-level executives should engage with finance teams to ensure they have the tools they need.Chapters00:00 Introduction to the Podcast and Guest00:59 Understanding the Role of Finance in Uncertain Times04:00 The Good, Bad, and Ugly of Financial Reporting08:13 Types of Data and Industry-Specific Challenges10:00 Connecting Financial Data with Reporting Tools13:59 The Future of Financial Reporting and Technology17:58 Key Takeaways for C-Level Executives

At Big Data LDN (BDL) 2025, Keboola CEO Pavel Dolezal presented a new data agent designed for all business users, not just engineers. With a mission to make AI, automation, and data easy to access, relevant, and useful across the organisation, Dolezal revealed that the data agent has been embedded with contextual intelligence and generative AI.“While we typically assist data engineers with building the pipeline, we took the same data agent and built a different environment for it — a chat-like environment. By default, the chat has context, knows what to do, knows where not to go,” the Keboola CEO unveiled on the Don’t Panic It’s Just Data podcast. In the EM360Tech podcast recorded live at BDL, Dolezal spoke to Christina Stathopoulos, the Founder of Dare to Data in the recent episode of the Don’t Panic It’s Just Data podcast. They talked about the new Keboola Data Agent and it plays a key role in AI-backed change and the growth of large language models (LLMs) in business.Context for AI-Backed Data Strategy Matters More“Anyone can be technical now. It’s context that matters,” stated Dolezal, also the co-founder of Keboola. He presented a strong argument for why enterprise data strategies are falling short and how a new wave of smart tools will change that.“The pipeline of what you can do is limitless if you build it for people in business,” he added. “You can't keep data and AI just in the hands of engineers anymore. That model doesn’t scale.”As businesses face a growing number of data sources — sometimes over 300 SaaS platforms and more than 80 departments — managing, governing, and activating that data has become a challenge. The appealing promise of AI often adds another layer of complexity.When AI Adds More ComplexityEnterprise leaders were told that AI would simplify data workflows. Instead, many found themselves managing disconnected tools and failed pilot projects.“We all read the MIT study. 95 per cent of AI proofs of concept don’t make it to production,” the CEO of Keboola highlights. “Why? Because large language models (LLMs) need context. And enterprise data environments are anything but simple.”At Keboola, context is crucial, he emphasised. It includes not just metadata but also event logs, debug trails, and orchestration details, including the complete story behind every data product.“LLMs thrive on context. The more relevant context you provide, the better the outcome. But in today’s data stack, where your context is spread across 15 tools, that's nearly impossible.”This is where Keboola’s new Data Agent comes in. It is a generative AI interface built directly into data workflows, capable of understanding and acting on both the structure and state of a company’s data.Watch the podcast for further insights on EM360Tech. Key TakeawaysFocus on context and domain knowledge rather than technical skills.Ease of acquiring technical skills compared to the importance of understanding business processes.Agents will run business processes both internally and externally.Need for infrastructure to support agents Challenges of provisioning ad hoc environmentsUse agents to automate business processes.Importance of data governance.ChaptersIntroduction & Keboola's Mission: 0:00Challenges in Modern Data Management: 2:23Impact of Complexity on Teams: 4:04LLMs in Data: Potential and Pitfalls: 5:50Keboola Data Agent: Bridging the Gap: 8:12Evolution of Keboola Data Agent: 13:00Future Vision for LLMs & Agents: 15:05Key Takeaway for Leaders: 17:12

Organizations are increasingly exploring new technologies to improve their operations, but adoption comes with real challenges. In the latest episode of Don’t Panic, It’s Just Data, host Trisha Pillay speaks with Kevin Petrie, VP of Research at BARC, about the practical realities of integrating these emerging technologies into business operations, particularly when it comes to data stewardship, strategy, and operational oversight.Real-World Applications and GovernancePetrie begins by noting that these technologies are already delivering real value today, particularly in areas like software development and customer service. The key, he emphasizes, is starting with strong oversight. “Governance starts with data,” he says, pointing out that reliable, well-managed data is the foundation for successful adoption.Human oversight is equally important; automation alone cannot replace careful monitoring and decision-making. Effective governance also needs to extend beyond structured data to include unstructured information such as text, images, and other content types. As organizations adopt new models, they must be aware of the risks these systems introduce and put controls in place to mitigate them.BARC DATA festival onlineBuilding on these themes, BARC will host the DATA festival online on October 21, a virtual gathering designed to help leaders turn insight into action. The free event will bring together data professionals, decision-makers, and industry experts to share real-world use cases, operational frameworks, and lessons learned from practical implementations. The festival provides a clear roadmap for organizations seeking to make technology adoption both effective and sustainable.TakeawaysEmerging technologies are already delivering measurable value in business operations.Governance begins with reliable data and extends to all types of information, including unstructured formats.Human oversight is essential to maintain accountability and manage risks.Practical, real-world use cases illustrate successful adoption strategies.Cost efficiency, culture, and cross-functional programs are critical for sustainable implementation.Responsible, accountable practices are a must-have for long-term success.Chapters00:01: Introduction to AI Adoption01:00: Real-World AI Applications03:03: Balancing Automation and Human Oversight05:19: Governance and Data Strategy07:02: BARC DATA festival online preview09:54: Audience and Takeaways12:13: Rapid Fire Questions14:02: Closing ThoughtsAbout BARCBARC is a leading analyst firm for data and analytics and enterprise software, recognized for providing trusted, unbiased insights. Its expert analysts deliver in-depth research, advisory services, and industry events to help organisations make informed decisions about technology, data strategy, and analytics.With over 25 years of experience in data strategy, governance, architecture, and software selection, BARC empowers clients to become truly data-driven organizations. Its research highlights market trends, evaluates software and vendors rigorously, and provides actionable guidance to help enterprises innovate with data, analytics, and AI.

With an erratic and fast business environment, finance teams are facing high pressure to process reports. The main challenge lies in how mid-market firms achieve digital transformation, not by abandoning familiar tools but by making them more effective. In this episode of the Don't Panic It's Just Data podcast, host Kevin Petrie, Vice President, Research and Head of Data Management Practice, BARC, speaks with Maeghan Carriere, Divisional Vice President, Software Sales NA, insightsoftware; and Nate Cook, Director of Product Marketing, insightsoftware. They discuss the importance of automation, the role of CFOs as strategic leaders, and, most importantly, how mid-market companies can leverage tools like Spreadsheet Server for digital transformation. The conversation also spotlights the need for finance professionals to upskill in AI and data analysis to remain competitive.All speakers agree that the key is in equipping finance professionals with direct, real-time access to their data in the environment they know and trust – Excel.CFOs as Strategic LeadersCook believes that the role of the Chief Financial Officer (CFO) and their team has changed massively. "We really expect them to look back only as it helps them find a path forward.”This new role requires finance teams to become effective data teams but how can they do that? A recent Gartner study found that 75 per cent of CFOs "said they own or co-own enterprise data and analytics at their organisations." Unfortunately, these teams "sometimes struggle to have access to the data that they need." The time wasted on tedious data entry and report generation is time lost for important analysis and strategic thinking."The short answer is Spreadsheet Server," Cook told Petrie, explaining that better decisions rely on better data, and Spreadsheet Server provides the latter.Also Watch: Struggling with ERP Data? How to Get Real-Time Reporting in ExcelExcel with AI Upskills Finance TeamsAutomating reporting doesn't just save time; it allows finance professionals to develop skills in areas like Excel, data analysis, and AI. Carriere points out that AI is an important tool that every professional needs to learn to use effectively."Once you understand how data flows and connects in real time, you're better positioned to use AI tools because you can quickly tell if the results make sense," she explains.The key message for IT decision-makers is clear, as per insightsoftware’s divisional VP, "Stop fighting Excel and make it more powerful instead." By embracing the tool that finance teams prefer, organisations can achieve quicker results, faster adoption, and ultimately free their financial experts from manual tasks. Cook also notes, Spreadsheet Server is "one way to help remove a lot of that toil and refocus the time that your folks are spending on the more strategic parts of analysis and decision-making that can help drive your organisation forward."TakeawaysFinance teams are facing scaling pressures and resource constraints.The need for speed in decision-making is critical for finance leaders.Automation can save finance teams significant time and improve productivity.CFOs are increasingly expected to be strategic leaders within organisations.Excel remains a preferred tool for finance teams and should be leveraged in digital transformation.Mid-market companies should identify specific areas for technology to deliver efficiencies.Real-time data access is essential for better decision-making.Upskilling in AI is necessary for finance professionals to remain competitive.Finance teams can develop analytical skills while automating reporting processes.Building trust in data is crucial for effective AI implementation.Chapters00:00 Introduction to the Podcast and Guests02:22 Current Challenges in Financial Reporting05:18 Transforming Finance Teams with Automation11:21 Digital Transformation for Mid-Market Companies13:43 Accessing ERP Data for Better Decision Making18:04 Upskilling Finance Teams for AI Integration20:45 Key Takeaways for CXOs

Generative AI has captured global attention, powering everything from chatbots to intelligent assistants. Yet in the enterprise, its promise often hits a dead end. According to Gartner, 80 per cent of enterprise data remains unused or “dark,” because conventional AI struggles to interpret complex, domain-specific information.In this episode of the Don't Panic It's Just Data podcast, EM360Tech host Trisha Pillay speaks with Andreas Blumauer, Senior Vice President at Graphwise, about how retrieval-augmented generation (RAG) and its advanced application, Graph RAG, are levelling up enterprise AI. Together, they explore the limitations of traditional AI, the critical role of knowledge graphs in improving data accuracy, and what it takes for organisations to successfully adopt these technologies.Why Graph RAG MattersWhile RAG enhances Generative AI by enabling it to retrieve relevant data from large knowledge bases, Graph RAG takes it further. By integrating knowledge graphs, Graph RAG preserves the relationships, sequences, and meaning inherent in enterprise data. This ensures AI outputs are not just collections of facts, but structured insights that reflect the logic of an organisation’s knowledge.These advantages include:Higher accuracy: Retrieval precision can increase from 80% to 95%, reducing errors in AI outputs.Trustworthy results: Outputs are explainable and traceable, providing transparency that enterprises require.Scalable integration: Connects data across silos and departments, making AI adoption enterprise-ready.“Graph RAG respects the structure of enterprise data instead of flattening it. That’s what makes it trustworthy,” explains Blumauer.Generative AI opened the door to possibilities. RAG made it actionable. Graph RAG takes it to the next level. By transforming dark, siloed data into structured, actionable knowledge, Graph RAG helps organisations achieve the accuracy, trust, and scalability essential for navigating the next frontier of enterprise intelligence.Takeaways80 per cent of enterprise data remains unused or dark.Traditional AI struggles to interpret complex enterprise data.RAG retrieves information from within the enterprise data landscape.Graph RAG improves the accuracy of AI outputs.Knowledge graphs link data points across different silos.Building a knowledge graph is a strategic investment.Incremental growth is possible with knowledge graphs.Graph RAG can increase accuracy from 80 per cent to 95 per cent.Data quality and governance are essential for AI success.The future of enterprise AI relies on effective knowledge management.Chapters00:00 Introduction to RAG and Graph RAG03:04 Understanding the Importance of Knowledge Graphs05:46 Adopting RAG: Organisational Readiness and Strategic Investment08:51 Real-World Applications and Benefits of Graph RAG11:56 The Evolution of Knowledge Graphs in AI14:46 Future of GraphRAG and Enterprise AI17:36 Rapid Fire Questions and Closing ThoughtsAbout GraphwiseGraphwise is a leading enterprise AI company specialising in knowledge graph technologies. By combining retrieval-augmented generation (RAG) with advanced graph-based approaches, Graphwise helps organisations turn siloed, complex data into accurate, actionable insights, enabling smarter decisions, scalable integration, and trustworthy AI outcomes.

Big Data LDN (BDL), the ultimate data event of the year, celebrates its 10th anniversary this year. This year’s event is scheduled to take place on September 24 and 25, with a brand-new deep-dive conference held on September 23. In this episode of the Don't Panic, It's Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, speaks with Michael Ferguson, CEO of Intelligent Business Strategies and BDL Conference Chair, and Palesa Amadi, BDL Conference Manager. The speakers provide a sneak peek of the upcoming Big Data LDN conference. They discuss how AI affects data and analytics, and spotlight the importance of data governance. Additionally, they talk about the different theatres and sessions available at the conference. The conversation also shines a light on keynotes and notable speakers, the changes in data engineering and architecture, and the learning opportunities for attendees. AI Agents, Data Products in the LimelightGet ready for "AI madness", says the Conference Chair. This year's event will feature AI in many forms, from AI agents walking the aisles to its integration into almost every software product on the exhibition floor."It's going to be hectic," Ferguson expresses. He expects a strong push toward AI-driven data governance and data management. Such changes aim to simplify complex tasks and make tools easier for more users to access. Instead of just seeing copilots, attendees will experience multi-agent capabilities and even "AI apps."With 16 theatres and numerous speakers, the conference focuses on learning. Conference Manager describes how the content is tailored for everyone—from engineers and analysts to students."You're going to come to this to learn and to add to your knowledge, and of course, network," says Amadi. The show has changed its focus to include new theatres like Data Products and Streaming Analytics, along with Decision Automation. Some theatres have been renamed to highlight the growing influence of AI, such as the Data and AI Strategy Theatre.A new "Data for Good" theatre will feature sessions on sustainability and using data for unconventional purposes, like tackling global food needs.Unmissable Keynotes and SpeakersThe speaker lineup is a big draw, featuring well-known experts and thought leaders. Amadi is particularly excited about keynote speaker Zhamak Dehghani, who will discuss the "apparent redundancy" of the term data mesh. Dehghani, who wrote the viral white paper on this topic, will advocate for her views, as Amadi puts it. Another highlight will be a session on the key role of big data and AI in the gaming industry, led by Carly Taylor.The event's closing keynote will be delivered by the world-renowned physicist Professor Brian Cox. He will talk about quantum computing and the universe as a quantum computer. Beyond the Main EventFor those looking for more, Big Data LDN is hosting a new paid pre-show conference on September 23rd called Data-Driven LDN. This event is mainly designed for attendees in a high-level enterprise role, like CDOs and CTOs, and will be led by Mike Ferguson. It’s a chance to explore topics such as agentic AI, data governance, and data products in depth.Whether you’re an industry expert or a curious newcomer, the 10th anniversary of Big Data London promises to be an engaging and transformative experience, demonstrating how quickly data and AI are changing our world.For further information, tune into the podcast.TakeawaysBig Data LDN is celebrating its 10th anniversary.AI will dominate this year's conference with new technologies.Data governance now includes privacy, security, and compliance.The conference will feature various theatres focusing on specific topics.Sustainability will be a key theme in the Data for Good theatre.Speaker quality is ensured through a rigorous selection process.The event aims to cater to all levels of expertise.Real-world case studies will provide practical guidance.Data architecture is evolving to lower latency and improve access.Networking opportunities will be abundant at the conference.Chapters00:00 Introduction to Big Data London 202301:48 AI's Dominance at Big Data London06:15 Navigating the Conference: Theatres and Content15:02 Data Governance Challenges for AI20:00 Improving Speaker Quality and Content32:35 Key Technical Lessons from the Conference