The massive interest in AI solutions has sparked a huge and pervasive wave of AI projects. We are now entering a second phase where the AI projects that have proven value are looking for operational landing places in enterprise environments. This is visible through the big hype for AI data systems like vector databases and feature stores. This phase of AI operationalizing is the hour of databases, which have proven already to be the battle-proof bedrock for data management enterprise environments. 

Postgres is naturally a front runner in this space. AI workloads are entirely tied to data, they start with data, they run on data and they produce data. Join this talk for a walkthrough on popular AI application flows, their strong ties to data and Postgres' strong operational qualities and demonstrate how they form the perfect environment for mission critical AI solutions in an enterprise.

AI is full of buzzwords, but what do they really mean for your business? In this 30-minute session, we’ll demystify key AI terms such as Artificial Intelligence, Machine Learning, Deep Learning, NLP, and MLOps. More importantly, we’ll demonstrate how these concepts can be applied to deliver tangible business value.

Through practical case studies, you’ll discover how organisations are using AI to optimise processes and achieve measurable outcomes. We’ll also discuss how to align AI initiatives with your business objectives to ensure success.

Join us for an insightful journey that simplifies AI and equips you with actionable strategies. Plus, stay for an interactive Q&A to explore how these ideas can be tailored to your needs.

Note: Visit Billigence at Stand Y239 for further insights.

Everything has changed in the last year with Generative AI entering onto the scene. This means a re-shuffling of priorities and budgets, putting AI-enabled Data & Analytics right back at the top of the agenda. In this session we will discuss: 

• That there is no Generative AI without data – but it has to be the right data 

• The importance of being able to bring together organised and trusted data 

• Why your data integration strategy is the foundation to successfully using AI

Gen AI entering the scene means a re-shuffling of priorities & budgets, putting AI-enabled Data & Analytics right back at the top of agenda.

Discover how D&G's diverse leadership has unified our data team by leveraging advanced tools and AI technologies. Learn how we foster a collaborative culture that drives innovation and impactful outcomes through state-of-the-art strategic analytics and AI-driven insights. Join us to explore strategies for integrating cutting-edge tech with diverse perspectives to enhance team performance and achieve impactful business and customer outcomes. 

As data continues to grow in complexity, the need for a unified data layer with rich semantic business meaning has become more critical than ever. This session examines the transformative impact of integrating generative AI with a well-structured, unified data layer, emphasizing how this combination unlocks new levels of intelligence and efficiency. By standardizing and contextualizing data across the organization, companies can fully leverage the power of generative AI to drive insights, automation, and decision-making. Explore practical strategies and case studies that highlight how a unified data layer is the key to harnessing generative AI, marking the moment when data management truly evolved. Don’t miss this opportunity to learn how to prepare your data infrastructure for the future.

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Join us at the Gen AI theater for an exclusive fireside chat with Matthew Thomson, Nokia’s head of data and digital, and Marianne Taudiere, Quid’s vice president of EMEA. Delve into how predictive analytics is revolutionizing strategic functions, explore the pivotal role of data in Nokia’s operations, and discover compelling case studies showcasing the transformative impact of Generative AI. Don’t miss this engaging session to learn how Gen AI is shaping the future of business!

Matthew Thomson (Nokia) and Marianne Taudiere (Quid) reveal how predictive analytics are revolutionizing strategic decision-making.

Improve your data infrastructure with governance and security, using proven methods and best practices. Break down data silos, foster collaboration, and optimise data accessibility, empowering your business units with the data and technologies they need. Learn how AI improves efficiency and streamlines data product development. And see how Microsoft Fabric simplifies data estate modernization with a focus on unifying your data in an open and governed foundation.

PrimaryBid, a UK-based technology business which connects retail investors to capital market transactions, is no stranger to working within a highly regulated, complex industry. As the team prepares for a global expansion beyond Europe and into the US and Middle East, they're planning for even higher stakes as they increase the scale of their operations and external data products.

Hear from PrimaryBid's Director of Data & AI, Andy Turner, as he shares PrimaryBid's data journey, including how they rebuilt their data stack from the ground up to ensure accurate, reliable data across global markets, the value (and challenges) of delivering external data products, and how data observability has played a crucial role throughout PrimaryBid's expansion.

In today’s data-driven world, whether you’re building your own data pipelines or relying on third-party vendors, understanding the fundamentals of great data movement systems is invaluable. It’s not just about making things work—it’s about ensuring your data operations are reliable, scalable, and cost-effective.

As an early employee and Airbyte’s Platform Architect, I’ve spent the last 3.5 years working through the challenges and intricacies of building a data movement platform. Along the way, I’ve learned some important lessons, often the hard way, that I believe could be helpful to others who are on a similar journey.

In this session, I’ll share these lessons in the hope that my experiences can offer some guidance, whether you’re just starting out or looking to refine what you’ve already built. I’ll also touch on how the rapid rise of generative AI is changing the landscape, and how we’re trying to adapt to these new challenges. My goal is to provide insights anyone can take back to their own projects, helping them avoid some of the pitfalls and navigate the complexities of modern data movement.

2 - 3 Main Actionable Takeaways:

• A general framework for designing a data movement system.

• Crucial fine print such as managing various destination memory types, the surprising need to re-import data and the shortcuts & pitfalls of artificial cursors.

• Adjusting data movement systems for an AI-first world.

Exploring the practical applications of generative AI in driving data-driven decision-making within our organisations. From simplifying marketing optimisation decisions to decentralising insights, this talk showcases how businesses leverage generative AI to innovate and adapt in today's dynamic landscape. So, join us to discover how this cutting-edge technology is revolutionising decision-making, driving growth, and providing a competitive edge in our business.

We are in an era of heavy learning when it comes to delivering successful AI implementations across our organizations. In fact, the best AI use cases and outcomes are around the corner and still to come. However, there are significant takeaways from first projects all around us that can guide, accelerate, and improve our own journeys. There are also key differences and considerations in how IT teams deploy AI projects and technologies. Attend this session to learn the four key data-centric must haves to increase AI initiative success based on current Quest and Sparkle client experiences from around the world and use them as a catalyst to effectively trailblaze your own efforts.

Attend this session to learn the four key data-centric must have's to increase AI initiative success based on our current client experiences

For decades, data modeling has been fragmented by use cases: applications, analytics, and machine learning/AI. This leads to data siloing and “throwing data over the wall.”

With the emergence of AI, streaming data, and “shifting left" are changing data modeling, these siloed approaches are insufficient for the diverse world of data use cases. Today's practitioners must possess an end-to-end understanding of the myriad techniques for modeling data throughout the data lifecycle. This presentation covers "mixed model arts," which advocates converging various data modeling methods and the innovations of new ones.

Prioritizing Product Roadmaps with Causal Inference Methods. In the talk, I'll be walking through an example of turning an ambiguous ask into insights that lead to an action plan for what to build in the coming year. Using Double ML on our workout data allowed us to tackle the tricky question of causality in a product landscape where we haven't tested many features.

Generative AI (GenAI) has garnered significant attention for its potential to revolutionize various industries, from creative arts to data analysis. However, organizations are realizing that implementing GenAI is not as easy as just asking ChatGPT a few questions. Providing the most relevant and accurate contextual data to the LLM is critical if organizations are going to realize the full benefits of GenAI. Retrieval Augmented Generation, or RAG, is a well understood and effective technique for augmenting the original user prompt with additional, contextual data. However, many examples of RAG grossly oversimplify the reality of enterprise data ecosystems. In this session, we will examine how a Logical Data Fabric can make RAG a practical reality in large, complex organizations and deliver AI-ready data that make RAG effective and accurate.