Opening and Welcome
Andrew Delaney, President & Chief Content Officer, A-Team Group

Practitioner keynote: 

Panel: Delivering business value with data products

• Why is a data product mindset essential? What is the most compelling business case you’ve made to your board to move from a cost-centre mindset to a value-driven one?
• How do you pivot a large organisation from siloed data ownership to data stewardship? What are the most effective incentives or governance models you’ve used to encourage teams to create and share data products?
• What is the core technology and operating model for a data product? How do you define, build, and operationalise a data product framework at scale?
• How do you govern data products to ensure they are trustworthy and compliant? What role does a semantic layer or data catalogue play in ensuring discoverability and building a shared language?
• What KPIs and value frameworks are you using to measure the business impact and return on investment from your data product initiatives?

Moderator: Niresh Rajah, Chief Data & AI Officer, DLA Piper
Mridula Mutharaju, Head of Data & Analytics, Commercial & Institutional, NatWest
Peter Jackson, Chief Data Officer, Schroders

Keynote

Panel: Governing the unstructured frontier: AI, data and risk

• Why does the sheer volume and variety of unstructured data make it so difficult to govern, and what new risks does it introduce for AI initiatives?
• How are firms using AI as a governance tool to automatically discover, classify, and tag sensitive information within unstructured data at scale?
• How do you ensure data provenance, mitigate hidden biases, and achieve explainability for AI models trained on unstructured data?
• Beyond just compliance, how are firms linking the governance of unstructured data directly to key business outcomes and a source of competitive advantage?
• What does a governance framework look like that can effectively manage both structured and unstructured data for a holistic view of the enterprise?

Moderator: Joanne Biggadike, Head of Data Governance, Schroders
Paul Barker, Head of UK Data and Analytics Governance, HSBC

Keynote:

Panel: Beyond Silos – Building a unified data ecosystem for access, agility and ROI

• What are the drivers that make the breakdown of data silos a top priority today?
• What challenges do legacy systems and mainframes create for fragmentation and how can these be addressed? 
• How can firms shift the mindset from data ownership within a silo, to data stewardship for the enterprise? What incentives or governance models are effective in encouraging cross-functional collaboration?
• When considering new data platforms and operating models, how do external platforms and managed services fit in and what value can these provide over an in-house build?
• How can a data fabric or a semantic layer enable interoperability across legacy and modern systems without requiring a full rip-and-replace? Where are firms on their journey of implementing a semantic layer?
• Share an example of a new AI-driven capability or business value that became possible only after breaking down a critical data silo?

Case study: Unlocking alpha – mastering unstructured data

Panel: End-to-end data lineage in action: Case studies and discussion

• How has increased regulatory scrutiny and technical sophistication from auditors become the primary driver for automating end-to-end data lineage?
• What is the most compelling ROI for automating data lineage: is it reduced audit costs and faster regulatory response, or the increased trust required to deploy AI?
• Beyond just mapping data flows, how does automated lineage provide the essential foundation for AI governance?
• What are the biggest challenges in tracing data lineage across complex, hybrid environments that include both modern cloud platforms and legacy mainframe systems?
• The ultimate goal is a self-documenting, self-validating data ecosystem. How can AI be used to not only discover and map lineage but also to proactively identify quality breaks within those data flows?
• What is the single biggest organisational or cultural barrier to implementing end-to-end lineage and what is a practical first step to overcoming it?

Lynn Watts, Head of Data Management & Governance, Royal London Asset Management
Stephen Veasey, CEO, 3D Innovations

Panel: Beyond the Dashboard: AI-powered approaches for proactive data integrity

• What are the trends in the shift from traditional data quality monitoring to modern data observability, and where are firms on that journey?
• How can firms move beyond traditional, reactive checks to implement data observability and proactive controls that create a truly AI-ready pipeline?
• For unstructured data, where do AI/ML models provide more value: in the initial parsing and structuring, or in identifying contextual errors?
• Beyond just cleaning data, what is a powerful example of using AI to enrich or augment existing structured datasets to extract new value?
• What does a practical workflow for AI-assisted remediation look like, and how can you use AI to accelerate human validation without replacing it?
• How can firms build a business case that quantifies the ROI of preventing bad data versus cleaning it up after the fact?
• What is the biggest organisational or cultural barrier to trusting AI for data quality, and what is a practical first step to overcoming it?

Emma Beale, Head of Data Oversight, Universities Superannuation Scheme (Ltd)

Keynote:

Panel: Cultivating a data team for innovation and growth

A successful data strategy is as much about people as it is about technology.  How can we address the human capital challenge and reskill data professionals for new roles and foster a culture of innovation and continuous learning?

• Cultivating a growth mindset: How do you foster a culture of experimentation and continuous learning within your data team, encouraging them to move beyond their comfort zones and embrace new approaches?
• Identifying high-value use cases: What processes or frameworks are effective for helping your data team identify data innovation use cases that deliver tangible business value?
• Building the right skillset: Beyond technical skills, what are the most critical capabilities that you look for when building a data team for a modernisation journey?
• Overcoming cultural resistance: What’s the biggest cultural barrier to data innovation you’ve encountered, and what strategies have you used to overcome it?
• Measuring the impact of people: How do you measure the ROI of investing in your data team’s skills and culture, and how do you communicate that value to the rest of the C-suite?
• Leading through change: How have you championed the development of a more innovative and growth-oriented data team?

Joanne Biggadike, Head of Data Governance, Schroders

Event ends