In data integration and data management, the focus is often on technology—databases, ETL processes, automation, reporting tools. But in the process, the true objective is easily overlooked: generating business value. 

Why does this happen? What organizational and technical barriers contribute to the disconnect? And most importantly: what strategies can we adopt to better align data initiatives with the goals of business stakeholders? 

This session explores the root causes and presents practical approaches to building a data-driven culture—with a clear focus on business impact.

Are you struggling to gain leadership support, craving stakeholder engagement, and begging for proper funding? Even though you may create Agentic AI wonders with your data, it won’t matter unless you explain the value in practical business terms. Join The Data Whisperer’s rollicking and riotous review of current buzzwords and some practical tips including:

• Differentiating between a data management narrative and other data storytelling and data literacy efforts

• Developing strategies to secure sponsorship and funding

• The 3Vs of Data Storytelling for Data Management

As AI reshapes every aspect of data management, organizations worldwide are witnessing a fundamental transformation in how data governance operates. This panel discussion, hosted by DataHub, brings together two forward-thinking customers to explore the revolutionary journey from traditional governance models to AI-autonomous systems. Our expert panelists will share real-world experiences navigating the four critical stages of this evolution: AI-assisted governance, where machine learning augments human decision-making; AI-driven governance, where algorithms actively guide policy enforcement; AI-run governance, where systems independently execute complex workflows; and ultimately, AI-autonomous governance, where intelligent systems self-manage and continuously optimize data stewardship processes. Through candid discussions of implementation challenges, measurable outcomes, and strategic insights, attendees will gain practical understanding of how leading organizations are preparing for this transformative shift. The session will address key questions around trust, accountability, and the changing role of data professionals in an increasingly automated governance landscape, providing actionable guidance for organizations at any stage of their AI governance journey.

Legacy data tools weren’t built for the AI era. Agentic Data Management replaces static rules and siloed platforms with intelligent agents that monitor, reason, and act—automating quality, governance, and lineage at scale. Discover how data leaders are shifting from manual firefighting to autonomous control, powering faster, trusted, and scalable data for AI and analytics.

- See a live demo of an agentic system in action

- Learn how probabilistic and deterministic approaches work in concert

- Explore how to build intelligent data products using the MCP protocol

Your AI is only as good as your data. Downtime, pipeline failures, and blind spots threaten revenue, compliance, and trust. Join Acceldata at Big Data London to explore Agentic Data Management (ADM), where AI agents autonomously resolve issues, optimize pipelines, and ensure governance. Powered by xLake Reasoning Engine, ADM delivers trusted, AI-ready data with self-healing operations. Hear how enterprises like Dun & Bradstreet boosted reliability and compliance. Ideal for data leaders, engineers, architects, analysts, product managers, and governance heads seeking autonomous data excellence. Visit Booth M70 for live demos

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.

For years, data engineering was a story of predictable pipelines: move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs.

This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

In this, the 10th year of Big Data LDN, in its flagship Great Dat Debate keynote panel, conference chair and leading industry analyst Mike Ferguson welcomes executives from leading software vendors to discuss key topics in data management and analytics. Panellists will debate the challenges and success factors in building an agentic enterprise, the importance of unified data and AI governance, the implications of key industry trends in data management, how best to deal with real-world customer challenges, how to build a modern data and analytics (D&A) architecture, and issues on-the-horizon that companies should be planning for today.

Attendees will learn best practices for data and analytics implementation in a modern data and AI -driven enterprise from seasoned executives and an experienced industry analyst in a packed, unscripted, candid discussion.

For years, data engineering was a story of predictable pipelines: move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs.

This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

Ten years ago, I began advocating for **DataOps**, a framework designed to improve collaboration, efficiency, and agility in data management. The industry was still grappling with fragmented workflows, slow delivery cycles, and a disconnect between data teams and business needs. Fast forward to today, and the landscape has transformed, but have we truly embraced the future of leveraging data at scale? This session will reflect on the evolution of DataOps, examining what’s changed, what challenges persist, and where we're headed next.

**Key Takeaways:**

✅ The biggest wins and ongoing struggles in implementing DataOps over the last decade. 

✅ Practical strategies for improving automation, governance, and data quality in modern workflows. 

✅ How emerging trends like AI-driven automation and real-time analytics are reshaping the way we approach data management. 

✅ Actionable insights on how data teams can stay agile and align better with business objectives. 

**Why Attend?**

If you're a data professional, architect, or leader striving for operational excellence, this talk will equip you with the knowledge to future-proof your data strategies.

The future of healthcare depends not only on breakthroughs in science, but also on how we harness the power of data, technology, and AI. To realise this future, we must challenge long-held assumptions about how data products are delivered. What once took months of complex engineering now happens in days—or even hours—by re-imagining the way we work. At AstraZeneca, we shifted from a traditional IT-centric model to one where business teams take ownership, rapid prototyping drives innovation, and automation ensures quality, compliance, and trust.

 This change is more than a process improvement; it is a cultural transformation. By aligning every step to business value, embracing bold goals, and learning from failure, we have built a system that empowers people to innovate at speed and at scale. Data products are no longer the end goal but the enablers of something greater: a knowledge fabric ready for AI, where enterprise context unlocks smarter decisions and accelerates the delivery of life-changing medicines.

Our journey proves that when ambition meets courage, and technology meets purpose, we can transform the way data serves science—and, ultimately, transform the lives of patients around the world.

Discover how Dun & Bradstreet and other global enterprises use Data Observability to ensure Quality & Efficiency, and enforce compliance across on-prem and cloud environments. Learn proven strategies to operationalize governance, accelerate cloud migrations, and deliver trusted data for AI and analytics at scale. Join us to learn how Data Observability and Agentic Data Management empowers leaders, engineers, and business teams to drive efficiency and savings at petabyte scale.

In an era where data complexity and scale challenge every organization, manual intervention can no longer keep pace. Prizm by DQLabs redefines the paradigm—offering a no-touch, agentic data platform that seamlessly integrates Data Quality, Observability, and Semantic Intelligence into one self-learning, self-optimizing ecosystem.

Unlike legacy systems Prizm is AI native, it is Agentic by Design, built from the ground up around a network of intelligent, role-driven agents that observe, recommend, act, and learn in concert to deliver continuous, autonomous data trust.

Join us at Big Data London to Discover how Prizm’s agent-driven anomaly detection, data quality enforcement, and deep semantic analysis set a new industry standard—shifting data and AI trust from an operational burden to a competitive advantage that powers actionable, insight-driven outcomes.

Federated data management approaches like data mesh promise to reduce complexity by organizing data into domain-owned, reusable products. But managing data as a product alone isn't enough. In many organizations, true reuse and cross-domain collaboration remain limited, while redundant data products continue to grow driving up costs without delivering efficiency. To make federated data strategies work, organizations also need a platform where supply and demand can meet, and where valuable products can be easily discovered, understood, accessed, and combined. They need a data product marketplace. In this talk, using real-world examples, we will explore how a data product marketplace: Drives reuse and composability of data products, reducing integration costs and helping stabilize maintenance over time. Aligns data supply with real business demand, highlighting high-value products and preventing the unchecked growth of low-impact ones. Engages the full ecosystem, from producers to consumers, in shaping governance policies and a shared language that support collaboration and trust. A well-designed data product marketplace is not just a nice to have. It is the necessary link that makes federated data management strategies both sustainable and effective.

In the age of agentic AI, competitive advantage lies not only in AI models, but in the quality of the data agents reason on and the agility of the tools that feed them. To fully realize the ROI of agentic AI, organizations need a platform that enables high-quality data pipelines and provides scalable, enterprise-grade tools. In this session, discover how a unified platform for integration, data management, MCP server management, API management, and agent orchestration can help you to bring cohesion and control to how data and agents are used across your organization.

In this short presentation, Big Data LDN Conference Chairman and Europe’s leading IT Industry Analyst in Data Management and Analytics, Mike Ferguson, will welcome everyone to Big Data LDN 2025. He will also summarise where companies are in data, analytics and AI in 2025, what the key challenges and trends are, how are these trends impacting on how companies build a data-driven enterprise and where you can find out more about these at the show.