Quentin CasaresData and AI leadership for regulated growth

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Abstract decision infrastructure map of connected data controls

Executive data / AI governance / regulated transformation

Quentin Casares

Turns fragmented enterprise data into trusted commercial, regulatory, and executive decision infrastructure.

A commercially grounded data and AI leader for CEOs, CFOs, boards, PE operating partners, and regulated executives who need evidence, control, and momentum.

Discuss a confidential mandate See evidence of impact

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Critical Data Element governance

Purview-aligned governance planning across more than 500 Critical Data Elements and finance reporting views.

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Data quality remediation uplift

Data quality improvement achieved through remediation, controls, ownership, and governance redesign.

£0M+

Documented efficiency delivery

Documented efficiency contribution through operating model redesign, automation, reporting simplification, and vendor governance.

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EMEA data and analytics remit

Regional data, reporting, analytics, and governance accountability across more than 30 EMEA markets.

Figures from curated executive record · methodology on request.

01 —Evidence

Selected proof from regulated data, AI governance, and transformation work.

Short case studies connect the leadership claim to operating context, intervention, measurable result, and board-level lesson.

2026-05-01 / case study

BCBS239 data confidence programme

A regulatory data confidence case study on turning weak evidence, inconsistent definitions, and fragmented ownership into board-readable control.

Read the evidence

2026-04-15 / case study

AI governance working group operating model

A case study on creating practical AI governance rhythms for use-case intake, executive oversight, and regulated adoption.

Read the evidence

02 —Executive thesis

The data function only matters when it changes the quality of decisions.

Fragmented data is not a technology problem. It is a board-level decision problem, a regulatory control problem, and a commercial performance problem.

His work sits where commercial performance, regulatory control, and executive decision quality meet: building data functions, modernising platforms, improving data quality, and creating governance that gives senior leaders confidence in the numbers.

He combines board-level credibility with hands-on technical fluency in Python, SQL, cloud data platforms, applied AI, RAG patterns, knowledge graphs, and agentic systems. That mix matters because the hardest executive data decisions are rarely solved by a platform selection alone.

The consistent thread is pragmatic transformation: turning fragmented data estates into decision infrastructure that can be governed, audited, scaled, and used by commercial teams.

03 —Signature blog themes

A compact operating model for decision infrastructure.

The site is structured around the problems Quentin is most useful for: decision-grade data, regulated AI, commercial platforms, and governance that can withstand scrutiny.

Decision-grade data

Designing the data products, metric definitions, quality controls, and ownership models executives can use when the decision matters.

MI governance and decision catalogues
Commercial analytics and performance reporting
Single source of truth design across CRM, ERP, core banking, and operations

Regulated AI governance

Making AI adoption practical in environments where model risk, auditability, accountability, and human control are non-negotiable.

Use-case intake and model risk classification
Human-in-the-loop controls and evaluation criteria
FCA, PRA, BCBS239, GDPR, and AI Act readiness alignment

Commercial data platforms

Building pragmatic cloud data platforms that connect engineering choices directly to revenue, margin, cost, and regulatory outcomes.

Azure, Databricks, Microsoft Fabric, Snowflake, BigQuery, and SQL ecosystems
Build vs buy strategy and vendor governance
Data products for underwriting, pricing, workforce, finance, and risk

Governance that earns trust

Replacing theatre with stewardship, evidence, metadata, lineage, and controls that boards, auditors, and business leaders can inspect.

BCBS239 remediation and regulatory evidence packs
Data catalogues, CDEs, lineage, glossaries, and ownership
Data governance forums with clear charters and decision rights

04 —Selected operating proof

Proof that connects engineering, governance, and executive outcomes.

Quantified signals from data leadership roles where reporting trust, regulatory evidence, operating model design, and commercial analytics had executive consequences.

Alpha Bank London

Head of Data / 2024 to present. Sole senior data leader for a boutique private bank, accountable for enterprise data strategy, governance, analytics, regulatory reporting data quality, and applied AI adoption.

Chairs the Data and AI Governance Working Group.
Led BCBS239 remediation across finance and risk reporting data.
Deployed Microsoft Purview plans across 500+ Critical Data Elements and 18 finance database views.

Volante Global

Head of Data Engineering and Analytics / 2022 to 2023. Built the data function for a high-growth specialty insurance technology business with full ownership of engineering, analytics, BI, governance, and commercial data products.

Stood up the cloud data platform on Azure and Databricks.
Created commercial analytics for underwriting, pricing, exposure, customer, and margin decisions.
Recruited and led a multi-disciplinary data team.

HSBC

Head of EMEA Data, Reporting and Analytics / 2013 to 2018. Accountable to the HR Executive Committee for data, reporting, analytics, vendor delivery, and governance across a 30+ country EMEA portfolio.

Led a blended 40+ person team across employees, contractors, and offshore delivery.
Delivered major cost efficiencies through re-engineering, automation, and operating model change.
Directed Accenture and Deloitte partnerships and GDPR readiness work.

Dufrain, MTC, RBS, Barclays, Walgreens Boots Alliance

Transformation, BI, data quality, and governance roles / Earlier leadership. Delivered advisory, public sector analytics, enterprise BI, investment banking data management, and healthcare data quality programmes.

Improved data quality by up to 40% in transformation contexts.
Built reporting estates and governance models for high-stakes decisions.
Operated across commercial, risk, operational, workforce, and regulatory datasets.

05 —Advisory areas

For moments where data has become a leadership issue.

The advisory model is deliberately narrow: confidential, executive-level work where the problem requires commercial judgement, regulatory literacy, and enough technical depth to make credible calls.

Executive data and AI strategy review

A focused review of whether the data estate, governance model, AI agenda, and reporting stack can support the next commercial or regulatory objective.

CEOs, CFOs, boards, and PE operating partners

Fractional data and AI leadership

Hands-on senior leadership for organisations that need credible data direction before, during, or after hiring a permanent executive.

Scale-ups, regulated firms, and transformation leaders

Regulatory data confidence programme

A structured path from weak evidence and inconsistent metrics to inspectable controls, stewardship, lineage, and data quality management.

Banks, insurers, fintechs, and regulated enterprises

Commercial data platform acceleration

A pragmatic build vs buy and delivery plan for data products that support growth, margin, retention, pricing, risk, and operating efficiency.

Commercial leaders, CTOs, CDIOs, and transformation sponsors

06 —Featured insights

Featured insights for executives evaluating data and AI maturity.

Short, board-facing pieces written for retrieval as much as reading: clear language, explicit expertise, and no generic AI consultant positioning.

2026-05-01 / 1 min

Decision-grade data is built around consequences

Why executive reporting fails when it is organised around systems instead of decisions, accountability, and commercial consequence.

2026-04-18 / 1 min

Regulated AI governance should accelerate the right use cases

A practical model for making AI adoption useful, inspectable, and controlled in financial services and other regulated enterprises.

07 —Speaking and media

Positioned for boardrooms, executive forums, and regulated AI conversations.

Topics are built around decision quality, board trust, AI governance, and commercial platform choices rather than trend-led AI commentary.

Decision-grade data: why correct reports still fail executives

How organisations move from technical reporting to data products designed around the decisions CEOs, CFOs, boards, and regulators actually make.

AI governance without theatre

A practical operating model for use-case intake, model risk, human control, evaluation, and executive confidence in regulated environments.

Board trust in data is earned, not declared

What it takes to make metrics, lineage, CDEs, stewardship, and audit evidence believable at senior levels.

08 —Featured appearances

Recorded talks and panels, watchable in place.

Selected keynotes and executive briefings on decision-grade data, AI governance, and commercial transformation.

09 —On the record

Podcast conversations for the longer argument.

Operating judgement on data trust, regulated AI adoption, and the commercial consequences of decision quality.

The Decision Infrastructure Podcast artwork

The Decision Infrastructure Podcast · 2025-10-08 · 54 min

When the board stops trusting the numbers

A conversation on rebuilding data confidence after trust has broken: critical data elements, provenance, and the evidence model senior leaders can inspect.

Regulated Futures artwork

Regulated Futures · 2025-07-30 · 47 min

Adopting AI without losing control

How regulated organisations can move on AI adoption while keeping accountability, auditability, and executive oversight intact rather than bolted on later.

Source-grounded expertise

Terms the site is designed to be retrieved for.

These are not tags for decoration. They map to actual leadership experience across banking, insurance, public sector operations, and applied AI engineering.

Enterprise data strategyAI governanceData governanceBCBS239 remediationGDPR data controlsFCA and PRA regulatory expectationsCritical Data ElementsData lineageMicrosoft PurviewDatabricksAzure data platformsCommercial analyticsBoard reportingData quality remediationRAG and knowledge graphsAgentic systems governance

Organisations

Experience across complex, regulated, and commercially demanding environments.

Qualified mandate

When the numbers need to become trusted decision infrastructure.

Use the contact route for confidential advisory, transformation review, fractional leadership, board-level data strategy, or speaking enquiries.

Start a confidential conversation