In the age of hyper-personalisation, AI-powered tools are reshaping marketing by creating deeply tailored consumer experiences. While these technologies drive engagement and conversions, they also open the door to manipulative tactics, data exploitation, and algorithmic biases that challenge consumer trust and safety.

This talk explores the ethical dilemmas surrounding AI-driven marketing strategies, particularly how the line between persuasion and manipulation can blur. We’ll examine real-world examples of hyper-personalisation gone too far, the risks of algorithmic bias, and marketers' responsibilities in ensuring transparency, fairness, and consent.

Attendees will leave with actionable insights on fostering ethical AI practices in marketing—balancing innovation with governance to protect consumers and build trust in an increasingly AI-driven world.

Agentic AI—systems that autonomously set goals, make decisions, and execute multi-step business processes—is transforming the enterprise, unlocking new levels of productivity. But with greater autonomy comes greater risk, as agentic AI amplifies the challenges of traditional and generative AI by increasing agency.

In this session, attendees will learn how to govern agentic AI with trust and transparency, enabling innovation without compromising safety. The speaker will discuss how targeted controls—enabled by the right tools and frameworks at the right time—can keep pace with fast-moving technology. Real-world case studies will illustrate how leading organizations are successfully managing agentic AI to transform workflows, boost productivity, and scale responsibly.

DfE are providing schools and local authorities with world-leading MI and intelligent reporting to take early action and help pupils thrive. With a national view of data, and through collaboration, system integration, smart analytics and responsible AI, we make it easier to spot issues, understand context and drive down absence.

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Many Data Engineers play very reactive roles. Because of that they struggle with cross-org collaboration, getting a seat at the table, creating alignment, and making their data producers produce high-quality data for them.

But there's another way – Data Engineers can LEAD!

Data Engineers sit at the intersection of data and their business, and if they execute well, they can play a very pivotal role!

In this talk, Orr and Shachar will explain what it looks like when you lead as a Data Engineer, and give practical suggestions for ways to apply that in your job.

Are AI code generators delivering SQL that "looks right but works wrong" for your data engineering challenges? Is your AI generating brilliant-sounding but functionally flawed results? 

The critical bottleneck isn't the AI's intelligence; it's the missing context.

In this talk, we will put thing in context and reveal how providing AI with structured, deep understanding—from data semantics and lineage to user intent and external knowledge—is the true paradigm shift. 

We'll explore how this context engineering powers the rise of dependable AI agents and leverages techniques like Retrieval-Augmented Generation (RAG) to move beyond mere text generation towards trustworthy, intelligent automation across all domains. 

This limitation highlights a broader challenge across AI applications: the need for systems to possess a deep understanding of all relevant signals, ranging from environmental cues and user history to explicit intent, to achieve reliable and meaningful operation.

Join us for real-world, practical case studies directly from data engineers that demonstrate precisely how to unlock this transformative power and achieve truly reliable AI.

With the pace of change of AI being experienced across the industry and the constant bombardment of contradictory advice it is easy to become overwhelmed and not know where to start. 

The promise of LLMs have been undermined by vendor and journalistic hype and an inability to rely on quantitative answers being accurate. Afterall, what good would a colleague be (artificial or not) if you already need to know the answer to validate any question that you ask of them?

The promise of neuro-symbolic AI that combines two well established technologies (semantic knowledge graphs with machine learning) enable you to get more accurate LLM powered analytics and most importantly faster time to greater data value.

In this practical, engaging and fun talk, Ben will equip you with the principles and fundamentals that never change but often go under-utilised, as well as discussing and demonstrating the latest techniques, platforms and tools so that you can get started with confidence.

Ben will show that far from taking months, data products can take minutes or hours to prepare, publish and start gaining value from, all in a sustainable and maintainable manner.

For nearly 200 years, Dun & Bradstreet has supported clients in achieving growth through the transformative power of data and analytics. As the world has rapidly evolved, so have we—serving more industries in more places than ever. We combine global data with local expertise to provide deep insights, intelligent tools, and tailored solutions. From navigating economic challenges to capitalising on new opportunities, we help businesses make smarter decisions and unlock value throughout every phase of the business lifecycle.

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When Virgin Media and O2 merged, they faced the challenge of unifying thousands of pipelines and platforms while keeping 25 million customers connected. Victor Rivero, Head of Data Governance & Quality, shares how his team is transforming his data estate into a trusted source of truth by embedding Monte Carlo’s Data + AI Observability across BigQuery, Atlan, dbt, and Tableau. Learn how they've begun their journey to cut data downtime, enforced reliability dimensions, and measured success while creating a scalable blueprint for enterprise observability.

75% of GenAI projects fail to scale—not because the models lack sophistication, but because they’re built on fragmented data. If your systems don’t know who they're talking about, how can your AI deliver reliable insights?

This talk unveils how real-time Entity Resolution (ER) is becoming the silent engine behind trusted, AI-ready data architecture. We will discuss how organizations across financial services, public safety, and digital platforms are embedding ER into modern data stacks—delivering identity clarity, regulatory confidence, and faster outcomes without the drag of legacy MDM.

You’ll learn:

• Why ER is foundational for AI trust, governance, and analytics
• Patterns for embedding ER into streaming and event-driven architectures
• How ecosystem partners and data platforms are amplifying ER value
• How to build trust at the entity level—without slowing down innovation

Whether you’re modernizing architecture, launching AI programs, or tightening compliance, this session will equip you to embed trust from the ground up.

What does it really mean to be a data-driven organisation? In this session, Hazal Muhtar, Senior Director of Analytics at Wise, will share real-life examples of how customer data fuels product innovation, marketing and growth at one of the world’s leading fintech companies.

Hazal will explore how data is driving change across industries and reshaping the way businesses operate. She’ll also discuss the importance of data democratization—how winning teams across an organisation access to insights can spark innovation, accelerate decision-making and build a culture where data is a shared language.

Join this session to discover how Wise embraces data at scale, and leave with practical takeaways on how customer data can become your most powerful asset in unlocking transformation and growth.

We are entering an Age of Artificial Intelligence with unprecedented opportunities. Companies are integrating AI-driven solutions to enhance efficiency, drive innovation, and maintain a competitive edge. However, prevailing myths about AI create uncertainty in strategic decision-making and adoption. We will discuss four foundational myths in our AI centric world: 1) regulation is an innovation killer; 2) scaling current models will lead to Artificial General Intelligence (AGI); 3) general models create maximum value; and 4) the value of data is unlimited. We will show these myths are delaying AI progress and provide research in overcoming their challenges.

"“If you shoot for the moon, you often don’t land among the stars.”

Delivering change in highly governed and hierarchical organisations can be slow and energy-sapping. DX4D—sponsored by the Second Permanent Secretary—set out to break the traditional model.

Come and hear about the programme’s successes and where it struggled. We’ll share what we believe are the key ingredients for success—and why getting off the launch pad matters more than reaching the final destination.

As AI systems become increasingly agentic - acting with more autonomy, initiative, and influence - the need for a new kind of human leadership has never been greater. Technology may accelerate decisions, but it cannot set direction, build trust, or inspire people to change. That’s the role of agentic leadership: intentional, visible, and human-centred. 

Drawing on both current research and her experience leading global data programmes and coaching senior executives, Helen Mannion will show why organisations must evolve their leadership as much as their technology. 

Attendees will leave with clear insight into how they can accelerate AI adoption in organisations, the specific leadership behaviours that make the biggest difference, and practical steps they can take to start building the conditions for AI to succeed.

Join Amperity’s Marcus Owens, Lead Solution Consultant, to learn more about the rapid innovations in data architecture brought by the new wave of AI agents. This session will start with a quick overview of what makes a good AI Agent – and then focus on how Agentic strategies can accelerate two key needs in customer data: 

Make Customer Data Usable – How AI Agents accelerate customer data engineering with Amperity’s Stitch and Chuck Data – saving data engineering teams hundreds of hours of effort. 

Make Use of Customer Data – How AmpAI allows Marketers to build outcome-driven customer journeys, going from intent to results faster than ever before.

Arch Capital Group, a $34 billion S&P 500 specialty insurance leader managing $21.5 billion in gross premiums across 60+ global offices, faced a critical challenge: ensuring data quality and consistency across their complex risk assessment operations. With 25+ predictive models supporting AI-driven underwriting for specialty lines—the industry's most complex and unusual risks—incomplete or inaccurate data inputs threatened the accuracy of critical business decisions spanning property & casualty, reinsurance, and mortgage insurance operations. 

In this session, Sam from Arch Capital shares how the organization partnered with DQLabs to transform their data trust framework, implementing automated quality checks across their global data ecosystem. Learn how this transformation enabled Arch to maintain their disciplined underwriting approach while scaling operations, improve regulatory compliance across multiple jurisdictions, and enhance their ability to respond rapidly to emerging risks while supporting the data accuracy essential for their leadership position in specialty insurance markets.

This talk, presented by Dan Keeley, Principal Data Engineer and Jonathan Conn, Digital Technology Director from England Rugby, delves into the real-world challenges and triumphs of a complex cloud-to-cloud migration.

So this is pretty cool. You have just created a set of data products. The pipelines are running and the data is ready ... but no-one is using them. Why not? Taking the consumer's perspective, I will show you 10 top tips on how to attract more people to your data.

Abstract:AI promises transformation, but most enterprises are still held back by a blind spot: unstructured data. The documents, conversations, and content that hold the richest business insights remain locked away—unusable, ungoverned, and invisible to AI. This untapped treasure delays projects, derails accuracy, and creates governance risks.

In this exclusive launch session, Teresa Rojas, Senior Director of Product Management at Collibra, and Reece Griffiths, co-founder of Deasy Labs (now part of Collibra), will unveil Collibra Unstructured AI. They’ll show how breakthrough technology, combined with Collibra’s unified governance platform, enables organizations to finally move beyond the black box and unlock the hidden wealth in their data to power trustworthy, scalable AI.

LLMs and MCP have fundamentally changed my work as a solo data practitioner. I spend less time writing code, but throughput and durability have improved dramatically. MCP servers now act as core tooling for LLMs in my workflows, but have also started to become data products in their own right. 

This is a practical talk that will focus on my experience of shifting to this way of thinking and working, including what has worked for me and the realities of where there are still rough edges.

Data Stacks have traditionally been comprised of all-in-one, monolithic solutions. Powerful, but often rigid, expensive, and slow to adapt. In this session, we’ll explore how a composable architecture flips that model on its head, giving teams the flexibility to integrate best-in-class tools, scale on demand, and move faster. We’ll walk through a practical use case comparing a traditional CDP with a composable approach, highlighting the trade-offs and the tangible benefits in agility, cost, and business impact.

AI is no longer a lab experiment; it’s reshaping how business gets done. This session explores how Rackspace is integrating AI agents as teammates, rather than technology tools. Learn how to cut through industry groupthink, redesign workflows, and build a new operating model where humans and AI collaborate to drive trust, growth, and resilience.

On average, one woman is killed by an abusive partner or ex every five days in England and Wales (Refuge).  

Clare’s Law, introduced in 2014, was intended to help prevent such tragedies, allowing Police to proactively disclose domestic violence history to victims potentially at risk. Yet while the law gave the Police new powers, officers’ capacity could inhibit efficient response to these requests - with siloed data across systems making proactively identifying safeguarding risks a challenge.

Bedfordshire Police, in partnership with Palantir, has built a solution to address these challenges - harnessing AI and big data to transform their force’s safeguarding approach from reactive to proactive, enabling faster identification and safeguarding of those at risk.

Emily Fitzsimons (Deployment Strategist, Palantir) alongside Bedfordshire Police will explore the challenges created for Police Public Protection Units by an increasingly complex data landscape - and how they're using latest technologies like AI to overcome them.

Join us to see how we're transforming safeguarding to help reduce domestic violence by empowering potential victims with vital information before it's too late.

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AI may be the enabler, but people are the driving force. From ethical design and cultural transformation to trust-building and team diversity, the session examines how people power progress.

This 30-minute discussion curated by Women in Data® brings together senior data leaders from Legal & General, M&G and Faculty, to explore how a human-centric mindset is essential for delivering responsible, impactful AI in financial services.

Speakers will share real-world insights into leading change, embedding inclusive practices, and turning complex data into meaningful outcomes. Expect a fast-paced, thought-provoking dialogue that reframes AI as not just smart tech, but a tool that must be shaped by empathy, purpose, and collaboration.

Powered by: Women in Data®

Sound AI outcomes start with trusted, high-quality data and delivering it efficiently is now a core part of every data and AI strategy. In this session, we’ll discuss how AI-supportive capabilities such as autonomous data catalogs, unstructured metadata ingestion and automated data trust scoring are transforming how organizations deliver AI-ready data products at scale with less hands-on staff involvement.

You’ll see how GenAI and agentic AI can accelerate reliable data delivery at every stage, from identifying and fixing data issues to building semantic business layers that give your AI models the context-rich inputs needed for success. We’ll also explore how agentic AI enables self-updating catalogs, proactive data quality monitoring, and automated remediation to free your teams to focus on innovation instead of maintenance.

If you’re shaping your organization’s data and AI strategy as a CDO, CDAIO, CIO, or data leader, this is your blueprint to operationalizing trusted, governed, and AI-ready data for every initiative, faster and smarter.

I’m passionate about building high-throughput distributed systems and making complex data platforms simple, resilient, and scalable. Today, I’m the Co-Founder and CTO of Ryft, building an Iceberg management platform.