By structuring a strategy into foundational, consolidated, and finished layers, businesses can unlock data product capabilities the organisation has never had before, quickly, and compliantly. This use-case based presentation by Adrian Pinder, Head of Digital and Data at DS Smith, will outline his team’s thought process when integrating Gen AI into their internal procurement data product. Their implementation strategy relied on:
As AI becomes embedded in business decisions, many organisations struggle to explain, govern, and stand behind the outcomes. The issue is rarely the model itself, but fragmented data, unclear ownership, and hidden quality risks.
In this session, Jean-Georges “jgp” Perrin outlines practical frameworks for turning fragmented data into governed data products that give organisations a lasting advantage as AI scales. Using open standards such as ODCS and ODPS, data products define ownership, quality, and usage boundaries early — making AI governable, auditable, and defensible without slowing innovation or forcing centralised rebuilds. You’ll learn:
You don’t govern AI. You govern the data products behind it.
AI and Machine Learning have become the go-to buzzwords in boardrooms, but not every data product needs a neural network to deliver value. This panel will explore examples of enterprise leaders separating genuine opportunity from costly overengineering, helping teams identify where AI/ML truly enhances functionality and adds value. Dive deeper into:
Session to be Announced
In this insightful case study, Ana Sotelo, Technical Data Product Director at PepsiCo, shares how the company is transforming its legacy data estate by prioritising quality and governance while migrating to the cloud. Over the past three years, PepsiCo has experienced great ROI, by:
Despite its growing importance, the term “data product” still lacks a universal definition across industries which creates confusion, misalignment, and missed opportunities. This session explores how enterprises can clarify the relationship between data products and business outcomes, and why internal roles like platform architects, product managers, and data engineers are essential to building and sustaining them. Gain a more in-depth understanding of:
Session to be Announced
As platform engineering evolves, the focus is shifting from simply reducing technical overhead to delivering strategic, measurable value across the organisation. This session will highlight a compelling case study on how adopting a Product Operating Model can reposition internal platforms from a cost centre to a business-enabling product. Learn more about how companies are:
Adopting a data mesh model isn’t just a technical shift—it’s a cultural one. This session explores how enterprises can tailor mesh principles to their unique platform architecture by aligning business goals, fostering cross-functional collaboration, and building platform teams that ask the right questions. Learn how to balance:
This panel will focus on how to manage the inevitable changes in enterprise data, from growth in volume to shifts in structure and use. Attendees will gain insight from a range of perspectives regarding maintaining data consistency and reliability in the face of continuous change, including: