Join us to discover the data challenges AstraZeneca are facing and how they are tackling them using Experian’s Aperture Data Studio. Learn about the benefits they're experiencing, the positive impact it’s having on their patients and how data quality and governance will support their longer term data vision.

Lloyds Banking Group is on a bold mission to become the UK’s biggest FinTech. As the bank transforms, trusted data is key to driving innovation, compliance, and better customer outcomes. In this session, Kiran Bal, Head of Data Quality, shares how her team is embedding quality, ownership, and governance across the enterprise. Learn how they’re aligning with the CFO and business leadership through actionable metrics, stakeholder engagement, and a federated approach to trusted data at scale.

Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

Metadata, data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI initiatives. Metadata tools help discover, and inventory your data assets. Data quality tools help business users manage their data at sources by setting rules and policies. Data observability tools give organizations integrated visibility over the health of data, data pipeline and data landscape. Together the tools help organizations lay good foundation in data management for BI and AI initiatives.

Data observability helps to detect and diagnose unknown unknowns in data quality and pipeline performance—but it doesn't fix them. Learn how Bigeye is leveraging AI to enable data teams to resolve data quality and pipeline issues, and even prevent them occurring in the first place.

Accelerating AI use cases demands strong data governance and many organizations struggle to manage complex, growing data volumes effectively. This session explores essential strategies for building a solid data governance foundation. Learn how organizations are overcoming common data governance obstacles, like data silos and inconsistent rules, to achieve measurable gains in data quality and efficiency. Through real-world examples, discover how unified data platforms can simplify data discovery, classification, and policy enforcement, leading to faster, data-driven decisions and reduced risk.

As we enter 2025, the evolution of agentic architectures—AI agents capable of autonomous decision-making—will hinge on one critical factor: data quality. High-quality, reliable data is the foundation for AI readiness. This session explores the interplay between data quality, data observability & agentic AI, highlighting DQLabs’ approach to more autonomic platform. Discover how AI-driven automation enhances data accuracy & reliability, reduces cost & manual effort, prepares organizations for the agentic era with scalable, self-optimizing systems built for AI success.

Dive into the symbiotic relationship between data and Artificial Intelligence in this comprehensive session. Explore how robust data foundations are critical for developing effective AI systems and how AI, in turn, refines and enhances data quality. Gain actionable insights into transforming raw data into intelligent solutions and leveraging AI to drive business innovation.

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.