Abstract decision infrastructure map of connected data controls

Independent board consultant / data & AI governance / transformation

Quentin Casares

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

Evidence, control, and momentum for CEOs, CFOs, boards, PE operating partners, and regulated executives.

Discuss a confidential mandate See evidence of impact

Proof signals

Critical Data Element governance

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

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.

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.

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.

Evidence

Proof that connects engineering, governance, and executive outcomes.

Short case studies and the senior roles behind them: operating context, intervention, and measurable result — not a list of logos.

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

Independent practice

Independent board consultant · 2026 to present

Confidential board advisory on data trust, reporting, and AI adoption.
Fractional and transformation leadership for regulated and PE-backed businesses.
Independent of vendor and platform interests — judgement, not resale.

Alpha Bank London

Head of Data · 2024 to 2026

Chaired 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

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

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

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.

Operating model

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

Advisory

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

CEOs, CFOs, boards, and PE operating partners

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

Executive decision map
Risk and opportunity heatmap
90-day sequencing plan
Board-ready findings pack

Fractional data and AI leadership

Scale-ups, regulated firms, and transformation leaders

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

Operating model design
Team and vendor leadership
Platform and tooling decisions
Governance forum setup

Regulatory data confidence programme

Banks, insurers, fintechs, and regulated enterprises

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

BCBS239 and regulatory gap assessment
Critical Data Element framework
Lineage and metadata plan
Audit evidence model

Commercial data platform acceleration

Commercial leaders, CTOs, CDIOs, and transformation sponsors

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

Target architecture
Data product roadmap
Integration and remediation plan
Delivery governance cadence

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.

2026-06-09 / 1 min

AI at the Speed of Light

How AI capability is compounding on a months-long clock while institutions and enterprises move in years — and what boards should actually do about the gap.

Read article

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.

Read article

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.

Read article

Speaking & media

Built 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.

Reach

Grounded in regulated, commercially demanding environments.

Not tags for decoration: terms and sectors that map to real 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

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

Confidential by default · NDA-ready · GDPR-aligned