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

Practitioner keynote: Leading in the Age of AI

• What does it take to scale AI from pilot to production at speed,  without breaking trust and embedding governance as a catalyst rather than a constraint?
• How should firms define ROI for AI, creating a transparent and repeatable value framework that captures both hard financial returns and the strategic value of risk reduction?

Ari Cohen, Chief Data and AI Officer, EMEA and Americas, Macquarie Group

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
Effie Kilmer, Chief Data & AI Advisor, Microsoft EMEA
Mridula Mutharaju, Head of Data & Analytics, Commercial & Institutional, NatWest
Peter Jackson, Chief Data Officer, Schroders

Keynote: When your data works, everything works

• What does “data as a business multiplier” look like across infrastructure, apps, security, people, and AI?
• Which 90-day wins should a CDO prioritize to prove value fast and change perceptions from cost center to growth engine?
• How do you tie governance and metadata to the KPIs your peers care about so every function sees its own gains?
• What belongs on an Enterprise Data Health scorecard, and how do you baseline and track it?
• Which metrics best demonstrate ROI—cost, speed, risk reduction, and adoption—and how do you report them credibly?

Ray Sullivan, Vice President, Product Management, Data Modernisation, Rocket Software

A-Team Research Report Update: AI Adoption in Financial Services
Strategic Implications for the Office of the Chief Data Officer

Andrew Delaney, President and Chief Content Officer, A-Team Group
Mark Davies, Partner, Element22

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, Chief Control Office, Enterprise Technology, HSBC Group
Michael Dimopoulos, Driving Global Markets Client Reporting Solutions and Digital Innovation, BNP Paribas

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?

Moderator: Duncan Cooper, Former CDO, Northern Trust
James Hope-Lang, Program Lead – Data Management, Danske Bank
Junaid Arshad, Custody Data Product Development, State Street,

Keynote fireside chat: Failures of Chief Data Officers; how to reboot: Data strategy, AI readiness, organisational alignment, and leadership lessons

• Why do so many CDO roles fail to deliver lasting impact, and what are the most common strategic and leadership missteps that derail data transformations
• When data and AI programmes fall short of expectations, how should CDOs diagnose whether the root cause is data foundations, operating model, governance, or stakeholder alignment?
• How can a CDO rebuild credibility and trust with the business after early failures, turning sceptical stakeholders into active sponsors and co-owners
• What practical frameworks can CDOs use to reboot their data strategy — resetting priorities across governance, technology, talent and value delivery without starting from scratch?
• How must the CDO role evolve in the age of AI to avoid repeating past mistakes and ensure data functions are positioned as enablers of business outcomes rather than cost centres?

Interview with: Jennifer Courant, Chief Data Officer, DWS Group

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?

Moderator: Naomi Clarke, Data Innovation & Strategy, Independent Expert
Lynn Watts, Head of Data Management & Governance, Royal London Asset Management
Pablo Kotey, Head of Data Enablement, Schroders
Janelle Veasey, CEO, 3D Innovations
Ray Sullivan, Vice President, Product Management, Data Modernization, Rocket Software

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?

Moderator: Chad Giussani, Independent Advisor
Ruby Paramanathan, Head of Data Strategy, Columbia Threadneedle Investments EMEA APAC
Joe Dimambro, Data Quality Lead, Hargreaves Lansdown

Afternoon break and sponsor networking

Champagne roundtables

Join a roundtable discussion for a deep-dive, interactive discussion with your peers on some of the day’s most important themes.  Address common problems, benchmark your progress and come away with practical solutions and takeaways!

1: Operationalising data products at scale

• Moving from data ownership to stewardship and product thinking
• Designing scalable data product frameworks and operating models
• Using semantic layers & catalogues for discoverability and trust
• Measuring ROI and aligning data products to business value

User host: Mridula Mutharaju, Head of Data & Analytics, Commercial & Institutional, NatWest

2: Building a unified data ecosystem across legacy & cloud

• Strategies for integrating legacy systems with cloud platforms
• Data fabric vs semantic layer vs virtualization approaches
• Enabling AI and analytics through interoperability
• Balancing build vs buy vs managed services

User host: Duncan Cooper, Former CDO, Northern Trust

3: Data Management for Private Markets

• Improving completeness, consistency and timeliness of private markets data across GPs, fund admins, custodians and internal teams
• Strengthening control and reconciliation of cashflows, valuations, fees, exposures and look-through reporting
• Automation opportunities across onboarding, validation and investor reporting workflows
• Managing the document layer and building clearer audit trails and governance for private markets data

4: How to ensure high quality and trusted data for AI

• Moving from reactive data quality to proactive observability
• Applying ML for anomaly detection and contextual validation
• Designing workflows for AI-assisted remediation
• Quantifying ROI for proactive integrity programmes

5: Managing and governing unstructured data

• Classifying, tagging & governing text, voice and documents
• Extracting intelligence & alpha from unstructured sources
• Making unstructured data usable for AI and analytics
• Managing privacy, risk & explainability at scale

User host: Paul Barker, Chief Control Office, Enterprise Technology, HSBC Group

6: Cultivating a data team for innovation & growth

• Developing a culture of experimentation & continuous learning
• Building modern skillsets: governance, engineering, AI, product
• Overcoming cultural resistance to transformation
• Measuring the impact of people, capability uplift & leadership

User host:  Joanne Biggadike, Head of Data Governance, Schroders

Event ends