Is the universe the ultimate processor of information?

Professor Brian Cox explores how quantum mechanics, information theory, and the structure of reality converge—challenging how we think about data, computation, and intelligence itself. From entanglement to AI, Cox invites us to reimagine the future of technology through the lens of physics. A mind-expanding session for anyone working at the edge of data, AI, and innovation.

As enterprises scale their deployment of Generative AI (Gen AI), a central constraint has come into focus: the primary limitation is no longer model capability, but data infrastructure. Existing platforms, optimized for human interpretation and batch-oriented analytics, are misaligned with the operational realities of autonomous agents that consume, reason over, and act upon data continuously at machine scale. 

In this talk, Zhamak Dehghani — originator of the Data Mesh and a leading advocate for decentralized data architectures — presents a framework for data infrastructure designed explicitly for the AI-native era. She identifies the foundational capabilities required by Gen AI applications: embedded semantics, runtime computational policy enforcement, agent-centric, context-driven discovery.

The session contrasts the architectural demands of AI with the limitations of today’s fragmented, pipeline-driven systems—systems that rely heavily on human intervention and customized orchestration. Dehghani introduces autonomous data products as the next evolution: self-contained, self-governing services that continuously sense and respond to their environment. She offers an architectural deep dive and showcases their power with real-world use cases.  

Attendees will learn the architecture of “Data 3.0”, and how to both use GenAI to transform to this new architecture, and how this new architecture serves GenAI agents at scale.

Data and AI are now a priority at board level in terms of value opportunity, but many organisations are still struggling to realise the benefits. In a world where tech debt is forefront of mind and there is a graveyard of forgotten proof of concepts, what do organisations really need to do to drive effective decision making from data and AI at scale?

Join Anush and Caro for a fireside chat as they debate key questions around balancing technology implementation, with user centred design and business change to enable effective decision making through data and AI. Work through what this means in terms of developing and executing on a data and AI strategy grounded in delivering real business value. Gain learnings from their experience working with leaders across the private equity market and how that has influenced a pragmatic approach to value lead implementation balancing need for short term impact with long term goals.

Anush and Caro will showcase real life examples and highlight practical recommendations to bring to life the reality that effective decision-making is, and always will be, a people and process challenge, enabled by tech.

From software products to vacuum cleaners, nothing is safe from being labelled 'AI-enabled.' But is it all really AI? Clearly not.

In this talk, I'll break down what is, and what definitely isn’t, AI, share some amusing industry examples, and help you navigate the current AI boom with the right language and definitions, so your own 'AI' initiatives don’t end up looking ridiculous.

We’re stepping into a new era where we don’t just query data — we talk to it. Thanks to advances in AI and language models, it’s now possible to interact with data using natural language, making insights more accessible than ever.

In this session, we’ll explore how organisations are shifting away from static dashboards and complex query languages, towards tools that allow anyone — not just data experts — to ask questions and get meaningful answers directly from their data.

We’ll look at what it takes to build these conversational data experiences, how they work behind the scenes, and why getting governance, quality, and context right is more important than ever. You’ll also hear examples of how this is already being used to unlock faster, smarter decision-making.

Whether you’re a data leader, architect, or someone helping your organisation make sense of information, this session will offer practical ideas on how to make your data truly talk.

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.

Here's what everyone gets wrong about responsible AI: they see the current regulatory vacuum as permission to move fast and break things. But I'm here to tell you something counterintuitive, that regulatory gap isn't a license to be reckless. It's your once-in-a-generation opportunity to build an unassailable competitive moat. 

This session challenges conventional wisdom with data-driven insights, real failure stories, and actionable strategies. Prepare to see responsible AI not as overhead, but as the highest-ROI investment you can make in your AI future.

In a world where AI capabilities will be commoditised, trust becomes the ultimate differentiator. Build it now, or spend the next decade trying to buy it back.

In this talk, you will learn about some of the common challenges that you might encounter while developing meaningful applications with large language models.

Using non-deterministic systems as the basis for important applications is certainly an 'interesting' new frontier for software development, but hope is not lost. In this session, we will explore some of the well known (and less well known) issues in building applications on the APIs provided by LLM providers, and on 'open' LLMs, such as Mistral, Llama, or DeepSeek.

We will also (of course) dive into some of the approaches that you can take to address these challenges, and mitigate some of the inherent behaviors that are present within LLMs, enabling you to build more reliable and robust systems on top of LLMs, unlocking the potential of this new development paradigm.

This talk will introduce NVIDIA Inference Microservices (NIMs), a set of easy-to-use microservices designed to accelerate the deployment of generative AI models across various platforms, including clouds, data centers, and workstations. We will explore how NIMs simplify and speed up the deployment of AI applications to provide AI solutions for various industries.

This presentation provides an overview of how NVIDIA RAPIDS accelerates data science and data engineering workflows end-to-end. Key topics include leveraging RAPIDS for machine learning, large-scale graph analytics, real-time inference, hyperparameter optimization, and ETL processes. Case studies demonstrate significant performance improvements and cost savings across various industries using RAPIDS for Apache Spark, XGBoost, cuML, and other GPU-accelerated tools. The talk emphasizes the impact of accelerated computing on modern enterprise applications, including LLMs, recommenders, and complex data processing pipelines.

Energy flexibility is playing an increasingly fundamental role in the UK energy market. With the adoption of renewable energy sources such as EVs, solar panels and domestic and commercial batteries, the number of flexible assets is soaring - making aggregation and flexibility trading infinitely more complex and requiring vast amounts of data modelling and forecasting. To address this challenge, Flexitricity adopted MLOps best practices to tackle this complex real-world challenge and meet the needs of the scaling energy demand in the UK. 

The session will cover:

- The complex technical challenge of energy flexibility in 2025.

- The critical requirement to invest in technology and skillsets.

- A real-life view of how machine learning operations (MLOps) scaled Flexitricity’s data science model development.

- How innovations in technology can support and optimise delivering on energy flexibility. 

The audience will gain insight into:

- The challenge of building data science models to keep up with scaling demand.

- How MLOps best practices can be adopted to drive efficiency and increase data science experiments to 10000+ per year.

- Lessons learned from adopting MLOps pipelines.

AI is rapidly transforming organisations, automating complex tasks, enhancing decision-making, and reducing operational risks. However, emerging evidence suggests over-reliance on AI could erode domain expertise and problem-solving skills. Studies indicate that teams using AI without manual verification experience skill degradation, with practitioners losing diagnostic abilities when AI systems automate critical assessments.

This session explores how AI should be implemented as a skill enhancer rather than a substitute, ensuring data scientists, analysts, and business specialists maintain hands-on expertise while leveraging AI’s capabilities. We will discuss how to integrate AI while preserving fundamental domain knowledge, the risks of over-reliance leading to deskilling and decision-making dependency, and strategies for upskilling workers to collaborate effectively with AI rather than defer to it. Through real-world case studies, empirical research, and practical frameworks, this session will demonstrate how AI can complement, rather than replace, human expertise.

It’s no secret that AI is reliant on ‘rock solid’ data. However given the vast amounts of data that companies now have spread across a distributed SaaS, on-premises and multi-cloud data estate, many companies they are a million miles away from this. We are also well past the point where people can govern data on their own. They need help and a total rethink is now needed to conquer data complexity and create a high quality, compliant data foundation for AI Success.

In this watershed keynote, conference char Mike Ferguson details what needs to be done to govern data in the era of AI, how companies can conquer the complexity they face, by implementing an always on, active and unified approach to data governance to continuously detect, automate and consistently enforce multiple types of policies across a distributed data estate. The session will cover:

• Current problems with data governance today and why old approaches are broken

• Requirements to dramatically improve data governance using AI and AI automation

• The need for an integrated and unified data governance platform

• Why a data catalog, data intelligence, data observability, AI Agents and orchestration all need to be integrated for AI-Assisted active data governance

• Understanding the AI-assisted data governance services and AI-Agents you need

• Establishing health metrics to measure effectiveness of your data governance program

• Creating a Data Governance Action Framework for your enterprise

• Monitoring the health and security of your data using data governance observability

• Enabling continuous reporting and AI-Assisted data governance action automation

• Implementing data governance AI Agents for different data governance disciplines

Data remains one of the most valuable assets a company has to guide its decision making. How that data is processed, used and presented is changing rapidly and with it the role and skills of data engineers. 

In this fireside chat, Manuka will explore the future of data engineering and the ongoing challenges of overcoming legacy constrains and governance with the latest breakthroughs in AI.

Expect a grounded discussion on:

• What “AI-ready” really means for data engineers

• Engineering through legacy constraints in a highly regulated environment

• Designing ingestion, orchestration, and observability that scale

• Embedding governance and quality without slowing delivery

• What’s next for data engineering in the age of generative AI

Whether you’re building pipelines, managing platforms, or designing modern data infrastructure, this is a rare behind-the-scenes look at how data engineering is evolving to meet the AI moment.

AI can enable you to achieve a lot for your business in terms of increased revenue, more efficient operations, and reduced risk. However, most organisations are not getting the traction or the value. 

We’ll look at how you get traction, moving from concept to value and everything in between. Referring to Generative AI and Agentic AI. You’ll also understand that starting with a project is a mistake and will stop you scaling and growing your capabilities. 

You’ll get an understanding of a framework for identifying and aligning AI activities to your business strategy. Using a proven approach to enable you to identify and prioritise projects with the best impact and greatest chance of success, which in turn will generate most value for you

You’ll also gain an understanding of how you need to organise yourself and manage your data to ensure the success of AI.

In a fast-paced operational environment, processes are only as strong as the consistency with which they are followed. Compliance to process is not simply a matter of adherence - is it the foundation for efficiency, optimisation and improvement. This session will explore how operational teams can leverage data to measure and strengthen process compliance, ensuring daily practices align with, and deliver on organisational outcomes.

We will examine the practical approaches taken to collect, measure and turn process data into insight allowing opportunities to be identified and move towards a culture of data-informed decision making that fosters accountability, enhances customer experience and unlocks new avenues for operational excellence.

At SumUp, we’ve encountered challenges adopting data mesh and part of it is defining and serving a proper data product. Through our data decentralization journey – we’ve gone from reactive to proactive ,from scattered to centralized documentation and from heavy friction to self-serve platforms. And now, from a 1 star hotel to building our first 5 star hotel. Yes, you read that right. We’re treating our data product like a hotel. 

In this talk, you’ll hear how we’re treating data not only as a product but also as a service. 

Just like a hotel – majority of our data products are 3 stars, but we’re now aiming for building a 5 star hotel. This talk is for both technical and non-technical data folks as we will walk you through the different architecture components and processes we are building to make this a reality.

While 95% of enterprise AI pilots fail to deliver business value, Secoda's customers are seeing a different reality: with 76% of AI usage focused on core business intelligence workflows rather than isolated experiments. 

Join Etai Mizrahi, Co-Founder & CEO of Secoda, as he shares how companies like Dialpad achieved company-wide AI adoption by moving 200+ employees from traditional dashboards to natural language analytics. Learn how Secoda's multi-agent AI architecture transforms data governance from manual overhead into automated workflows, and learn practical strategies for scaling AI beyond pilots to become essential infrastructure that delivers measurable ROI.

Today’s shifting security dynamics call for data-driven insights that pre-empt dangers and guide swift, informed choices.

In this joint session led by commercial and public defence experts, we’ll unpack how cutting-edge AI solutions and flexible data platforms illuminate operations across land, sea, air, and cyber.

Join us to hear how these methods can be applied in any fast-paced setting to amplify adaptability, strengthen safeguards, and outpace new challenges.

Powered by: Women in Data®

Deepak has building AI systems since 2014 starting with a Logistic Regression based model to now building Gen AI based systems in 2025. His talk will feature both the technical, business and human aspects of the AI systems he has built and contrast and compare them over the years. The intention is to peek into what could be possible in the future, keeping the past in mind. 

AI agents have seen increasing adoption across multiple industries as the next wave of AI. 

Existing regulations such as the European Union AI Act also apply to AI agents.

The session is based on Sunil Soares' books that can be found here: https://yourdataconnect.com

This session will cover the following topics:

• Agentic AI governance framework

• Applications with embedded AI

• Associated AI Governance Regulations including EU AI Act

• Agentic AI Governance Platforms

The term 'agentic AI' is all the rage these days, but there's still not much clarity around what it means. We'll walk through the basic building blocks of these agentic AI systems - predictive AI, generative AI, and workflow automation - and discuss why it's harder (and more important) than ever to ensure a trusted, enterprise-grade, and secure data backbone to get the reliable and trusted solutions our end-users are looking for. We'll also touch on market trends where we see the technology and capabilities evolving in the coming months.

In this session, you will learn how to take vibe coding practices and embed them in a safe and secure way to build truly differentiated AI enabled sofware at enterprise scale.

Amid soaring expectations, where is AI currently delivering true impact and what should we be looking to next? This expert-level panel features leading voices from Capco, FTI Consulting, LexisNexis Risk Solutions and Women in Data® unpacking AI’s real-world cross-industry applications.

Our panel of data leaders will explore topics such as explainability, emergent regulation and scalable ethical frameworks by sharing their first-hand insights from real-world learnings. While acknowledging AI’s complexity, the session highlights where technology is delivering transformative results, and what still requires scrutiny.

Powered by: Women in Data®