The world has never been more connected. Today, customers demand near-perfect uptime, responsive networks, and personalized digital experiences from their telecommunications providers. 

The industry has reached an inflection point. Legacy architectures, fragmented customer data, and batch-based analytics are no longer sufficient. Now is the time for Telcos to embrace real-time, when the speed of insights and the ability to remain agile determine competitive advantage.

In this session, leaders from Orange Belgium, Google Cloud, and Striim explore how telcos can rethink their data foundations to become real-time, intelligence-driven enterprises. From centralizing data in BigQuery and Spanner to enabling dynamic customer engagement and scalable operations, Orange Belgium shares how its cloud-first strategy is enabling agility, trust, and innovation.

This isn’t just a story of technology migration—it’s about building a data culture that prioritizes immediacy, empathy, and evolution. Join us for a forward-looking conversation on how telcos can align infrastructure, intelligence, and customer intent.

In an era where sustainability is no longer optional but essential, data-driven organisations are leveraging advanced analytics to unlock new pathways for innovation and growth.  

This 30-minute session brings together leading Women in Data® experts with deep experience in applying sustainability analytics across sectors. Through real-world insights and strategic frameworks, they will explore how environmental, social, and governance (ESG) data can be harnessed to inform smarter decision-making, uncover hidden opportunities, and drive long-term value creation and innovation whist remaining rooted in responsible data use.

Powered by: Women in Data®

Discover how to build a powerful AI Lakehouse and unified data fabric natively on Google Cloud. Leverage BigQuery's serverless scale and robust analytics capabilities as the core, seamlessly integrating open data formats with Apache Iceberg and efficient processing using managed Spark environments like Dataproc. Explore the essential components of this modern data environment, including data architecture best practices, robust integration strategies, high data quality assurance, and efficient metadata management with Google Cloud Data Catalog. Learn how Google Cloud's comprehensive ecosystem accelerates advanced analytics, preparing your data for sophisticated machine learning initiatives and enabling direct connection to services like Vertex AI. 

 Learn how Tableau helps organisations manage uncertainties and go from data to decisions faster. Join us on this agentic journey, empowering every data professional to deliver limitless success.

Session brought to you by Salesforce

In this session, Omni CEO Colin Zima and VP of Product Arielle Strong will share how early experiments led to AI features our customers actually use and love: from natural language chat, to embeddable AI products, to APIs and an MCP server. 

They’ll walk through what worked, what didn’t, and how AI has reshaped our product roadmap. Expect real-world examples of AI analytics in production, along with best practices for getting your data AI-ready.

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.

We’re excited to be back at Big Data LDN this year—huge thanks to the organisers for hosting Databricks London once more!

Join us for an evening of insights, networking, and community with the Databricks Team and Advancing Analytics!

🎤 Agenda:

6:00 PM – 6:10 PM | Kickoff & Warm Welcome

Grab a drink, say hi, and get the lowdown on what’s coming up. We’ll set the scene for an evening of learning and laughs.

6:10 PM – 6:50 PM | The Metadata Marathon: How three projects are racing forward – Holly Smith (Staff Developer Advocate, Databricks)

With the enormous amount of discussion about open storage formats between nerds and even not-nerds, it can be hard to keep track of who’s doing what and how this actually makes any impact on day to day data projects.

Holly will take a closer look at the three big projects in this space; Delta, Hudi and Iceberg. They’re all trying to solve for similar data problems and have tackled the various challenges in different ways. Her talk will start with the very basics of how we got here, what the history is before diving deep into the underlying tech, their roadmaps, and their impacts on the data landscape as a whole.

6:50 PM – 7:10 PM | What’s New in Databricks & Databricks AI – Simon Whiteley & Gavi Regunath

Hot off the press! Simon and Gavi will walk you through the latest and greatest from Databricks, including shiny new AI features and platform updates you’ll want to try ASAP.

7:10 PM onwards | Q&A Panel + Networking

Your chance to ask the experts anything—then stick around for drinks, snacks, and some good old-fashioned data geekery.

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.

Data leaders today face a familiar challenge: complex pipelines, duplicated systems, and spiraling infrastructure costs. Standardizing around Kafka for real-time and Iceberg for large-scale analytics has gone some way towards addressing this but still requires separate stacks, leaving teams to stitch them together at high expense and risk.

This talk will explore how Kafka and Iceberg together form a new foundation for data infrastructure. One that unifies streaming and analytics into a single, cost-efficient layer. By standardizing on these open technologies, organizations can reduce data duplication, simplify governance, and unlock both instant insights and long-term value from the same platform.

You will come away with a clear understanding of why this convergence is reshaping the industry, how it lowers operational risk, and advantages it offers for building durable, future-proof data capabilities.

As organizations increasingly adopt data lake architectures, analytics databases face significant integration challenges beyond simple data ingestion. This talk explores the complex technical hurdles encountered when building robust connections between analytics engines and modern data lake formats.

We'll examine critical implementation challenges, including the absence of native library support for formats like Delta Lake, which necessitates expansion into new programming languages such as Rust to achieve optimal performance. The session explores the complexities of managing stateful systems, addressing caching inconsistencies, and reconciling state across distributed environments.

A key focus will be on integrating with external catalogs while maintaining data consistency and performance - a challenge that requires careful architectural decisions around metadata management and query optimization. We'll explore how these technical constraints impact system design and the trade-offs involved in different implementation approaches.

Attendees will gain a practical understanding of the engineering complexity behind seamless data lake integration and actionable approaches to common implementation obstacles.

In a world where AI agents, complex workflows, and accelerating data demands are reshaping every enterprise, the challenge isn’t just managing data, it’s creating trusted context that connects people, processes, and technology. 

Join Rebecca O’Kill, Chief Data & Analytics Officer at Axis Capital, for an Honest No-BS conversation about how her team is transforming governance from a compliance checkbox into a strategic enabler of business value. 

Together, we’ll unpack: 

• Minimal Valuable Governance (MVG): why the old ivory tower “govern everything” mindset fails, and how focusing on just enough governance creates immediate business impact. 

• The ACTIVE framework, a practical approach for governance built on: Alignment, Clarity, Trust, Iterative, Value, Enablement 

• How Axis Capital is embedding governance across the organization by uniting the “front office” (what and why) with the “back office” (how). 

• Why context and knowledge are critical for the next era of agentic AI and multi-agent workflows, and how Axis is preparing for it today. 

By the end, you’ll see how Axis Capital is turning governance into a competitive advantage and why this approach is essential for any organization looking to thrive in a world of AI-driven automation and connected workflows. 

Analytical Data Product success is traditionally measured with classic reliability metrics. If we were ambitious, we might track user engagement by dashboard views or self-serve activity; they are blunt, woolly indicators at best. The real goal was always to enable better decisions, but we often struggle to measure whether our data products actually help. Conversational BI changes this equation. Now we can see the exact questions users are asking, what follow-ups they need, and where the data model delights or frustrates them. This creates a richer feedback loop than ever before, but it also puts our data model front and centre, exposed directly to business users in a way that makes design quality impossible to hide.

This session will recap the foundations of good data product design, then dive into what conversational BI means for analytics teams. How do we design models that give the best foundation? How can we capture and interpret this new stream of usage feedback? What does success look like? We'll answer all of these questions and more.

Practicing analytics well takes more than just tools and tech. It requires data modeling practices that unify and empower all teams within analytics, from engineers to analysts. This is especially true as AI becomes a part of analytics. Without a governed data model that provides consistent data interpretation, AI tools are left to guess. Join panelists Joe Reis, Sarah Levy, Harry Gollop, Rob Hulme, Shachar Meir, and Guy Fighel, as they share battle-tested advice on overcoming conflicting definitions and accurately mapping business intent to data, reports and dashboards at scale. This panel is for data & analytics engineers seeking a clear framework to capture business logic across layers, and for data leaders focused on building a reliable foundation for Gen AI.

Open Banking data offers transaction-level granularity at scale. This is ideal for building models that power credit underwriting, customer segmentation, and market trend analysis. This session covers practical techniques for transforming raw transaction data into structured features using entity resolution, feature engineering, and aggregation pipelines.

Maximize the value of your SAP investments by harnessing the power of Data and AI on Azure. In this session, you’ll learn proven strategies to leverage SAP landscapes, unify data across SAP and non-SAP systems, and unlock advanced analytics with Azure’s Agentic AI capabilities. We’ll showcase industry-ready accelerators that accelerate transformation, enable governed data access, and turn insights into action to fuel innovation, agility, and measurable business outcomes.

Data governance often begins with Data Defense — centralized stewardship focused on compliance and regulatory needs, built on passive metadata, manual documentation, and heavy SME reliance. While effective for audits, this top-down approach offers limited business value. 

Data Governance has moved to a Data Offense model to drive Data Monetization of Critical Data Assets in focusing on analytics and data science outcomes for improved decision-making, customer and associate experiences. This involves the integration of data quality and observability with a shift-left based on tangible impact to business outcomes, improved governance maturity, and accelerated resolution of business-impacting issues.

The next iteration is to move to the next phase of Data Stewardship in advancing to AI-Augmented and Autonomous Stewardship — embedding SME knowledge into automated workflows, managing critical assets autonomously, and delivering actionable context through proactive, shift-left observability, producer–consumer contracts, and SLAs that are built into data product development.

You’ve rolled out self-service analytics. Everyone’s answering their own questions. Job done, right? Not quite.

Together with ThoughtSpot, Huel shares what comes next when your organisation has fully embraced self-service analytics. From redefining the role of the data team, to unlocking a culture of empowerment for business users to go from reactive insights to proactive decisions, discover how Huel is going beyond dashboards and into a new era of analytics. If you’ve nailed self-service, or even if you haven't, this is your roadmap to what’s next.

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.

Businesses spend countless hours wrangling data: extracting information from messy PDFs, building dashboards that nobody uses, and attempting to extract insights that simply don’t exist. Surely there’s a better way?

In this session, Vishal Soni and Owen Coyle will show how AI and Alteryx can work together to completely transform how you handle data. Starting with one of the toughest challenges: extracting structured information from unstructured PDFs. Instead of complex regex, manual OCR, or hours of cleanup, you’ll see how LLMs inside Alteryx can instantly convert complex documents into clean, tabular data that’s ready for analysis.

Once this data is processed: Alteryx Auto Insights can be leveraged, which produces AI-powered analysis of your data, and jumps straight to the “why” behind the numbers. You’ll quickly see how Auto Insights surfaces the most important trends, patterns, anomalies and actionable insights. All this, while generating personalized, presentation-ready reports to drive action.

Whether you’re new to Alteryx or already an experienced user, you’ll leave this session with a clear understanding of how AI is changing analytics – turning hours of manual work into instant, actional insight – and how Alteryx is that the forefront of this change.

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.

What happens when cutting-edge data meets disruption? In this half-hour session, leading Women in Data® voices from some of the biggest names in the insurance sector will explore how innovative data and analytics are reshaping finance for a more resilient future.  

From climate risk and regulatory shifts to customer behaviour and operational efficiency, this session will unlock how insurers are using their data to adapt and stay ahead of the curve in our rapidly changing world. 

Powered by: Women in Data®

Business challenges that were once sporadic are now persistent and widespread—impacting everyone across the organization, from business users and analysts to data engineers and scientists.

To keep pace, BI platforms have steadily evolved, embracing technologies that empower every user to tackle growing data complexity with confidence.

Now, with sophisticated Gen AI and Agentic AI capabilities built into these platforms, we’re stepping into a new era of analytics—one that redefines what data democratization means for modern businesses.

Join us for an exclusive session where we’ll explore how the latest innovations in Gen AI are reshaping the BI landscape and unlocking powerful, actionable insights for every user.

In this session, you’ll learn:

- What defines a truly Gen AI-powered BI platform

- How businesses can empower every user with cutting-edge Gen AI

- How Agentic AI is shaping BI

- Live demos showcasing Gen AI and Agentic AI capabilities in BI

- Discover how a Gen BI platform can drive smarter decisions, boost productivity, and deliver transformative business outcomes.

AI is changing the game across industries, but one of its most powerful and urgent applications is in combating climate change. From optimizing renewable energy grids to improving carbon capture and making supply chains more sustainable, AI is playing a crucial role in driving real-world impact.

In this talk, Russell Dalgleish—entrepreneur, investor, and a leading voice in Scotland’s sustainability and innovation scene—dips into how AI-driven solutions like predictive analytics, and intelligent automation are reshaping the path to net zero. He’ll share lessons from Scotland’s thriving Greentech sector, offering practical insights on how businesses can harness AI to meet sustainability goals while staying ethical and responsible.

If you’re a business leader, policymaker, or innovator looking to turn AI into a force for good, this session is for you.

AI is changing every aspect of how we do our work and analytics engineering is no different. However, using AI to achieve 10x productivity improvements requires a totally new approach to how we do analytics. In this session, we will demystify how to use context engineering, and prompting techniques based on practical experience we've had at rolling our Paradime to some of the most innovative and AI-forward startups, scale-ups and enterprises.

In this joint session, GoodData and Purple will share a field guide to turning analytics into revenue. 

Together, we’ll show you how governed, context-rich intelligence becomes paid products that customers adopt. Using Purple’s guest Wi-Fi data product as an example, we’ll unpack what’s sold, how it’s priced, and how GoodData powers it in a step-by-step runbook you can use today. 

We’ll close with what’s next: operationalizing and monetizing AI Assistants and Agents so you can charge for outcomes, not charts.