Translating Data & AI Strategy Into Executable Operating Models

3rab partners with senior executives to design and stand up data & AI operating models that embed governance, capability, and measurable accountability into enterprise decision-making

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Our Core Advisory Offerings

Advisory mandates focused on designing, governing, and operationalising enterprise data & AI capability.

  • Establishes enterprise data leadership, clarifies decision rights, and delivers an executable strategy.

    • Target operating model: structure, roles, decision rights, accountability.

    • Data strategy: multi-year roadmap aligned to business priorities.

    • Domain ownership & accountability frameworks: clear governance across functions.

    • Integration: cross-functional alignment with Risk, Finance, Technology & Business.


    Deliverables: executive-ready materials for boards and regulators.


    Phases / Approach:

    1. Diagnostic assessment & gap analysis

    2. Operating model design and approval

    3. Implementation roadmap with milestones

    4. Board-ready reporting & KPIs

  • Right-sizes teams, develops capabilities, and ensures execution-ready delivery across global functions.

    • Current-state assessment: organizational structure, roles, and skills.

    • Target workforce model: roles, capabilities, and career paths.

    • Capability uplift roadmap: tailored learning, coaching, and mentoring.

    • Global delivery & sourcing strategy: optimize resources, locations, and vendor integration.


    Phases / Approach:

    1. Skills & structure assessment

    2. Target model design

    3. Capability roadmap & development programs

    4. Implementation & governance tracking

  • Aligns policies and frameworks to regulatory expectations, mitigates risk, and produces board-ready deliverables.

    • Review of policies, standards, and governance artifacts to ensure clarity and enforceability.

    • BCBS 239 alignment and regulatory compliance.

    • Gap analysis: against industry benchmarks and peers.

    • Policy redesign & remediation roadmap: prioritised with clear ownership.

    Phases / Approach:

    1. Assessment & diagnostic

    2. Gap analysis vs regulations / best practices

    3. Policy & framework redesign

    4. Delivery of board-ready, actionable recommendations

  • Bespoke and hands-on support across strategic data, AI, and operating model challenges — aligned to your organisation’s unique context and priorities.

    • Designed for bespoke projects: addressing needs outside standard mandates.

    • Strategic problem-solving: tailored to the client’s context and priorities.

    • Hands-on delivery: senior-led, direct engagement with executive teams.

    Phases / Approach:

    1. Define challenge & scope with client leadership

    2. Develop tailored methodology and plan

    3. Execute with embedded advisory support

    4. Deliver outcomes, dashboards, and board-ready insights

Engagement Approach


Diagnostic | Target Operating Model | Gap Assessment | Execution Roadmap

Selected Engagements

Context

A high-risk internal audit finding prompted Enterprise Data Governance to conduct a comprehensive evaluation of the bank’s data governance and data management policy environment. The matter was further reinforced through supervisory feedback. The institution needed to demonstrate enterprise-wide clarity over its data management requirements, alignment to recognised industry frameworks, and clearly defined governance roles, responsibilities, and authorities across business and control functions.

Mandate

Lead an independent assessment of the bank’s data governance and data management policy landscape against industry frameworks to define current-state coverage and identify structural gaps.

Approach

  • Reviewed and analysed approximately 70 data and technology-related policy artifacts, standards, and procedures.

  • Assessed coverage against DAMA DMBOK domains to evaluate alignment and completeness.

  • Identified inconsistencies, redundancies, and fragmentation across policy documentation.

  • Mapped governance roles and responsibilities embedded within policies to highlight accountability gaps.

  • Prioritised remediation themes and simplification opportunities to improve clarity and reduce operating complexity.

Outcome / Results

  • Delivered a structured current-state maturity assessment across enterprise data governance domains.

  • Identified priority gaps and simplification opportunities reducing policy operating overhead.

  • Strengthened enterprise alignment to recognised data management standards.

  • Provided executive clarity over governance roles, responsibilities, and authority structures.

Systemically Important US Banking Institution | Enterprise Data Governance

Data Policy and Governance Assessment

Context

Following the introduction of a new enterprise Chief Data Office, the institution sought to consolidate fragmented data capabilities and scale delivery capacity to support expanding enterprise data initiatives. Existing data resources were dispersed across multiple teams and geographies, limiting coordination, scalability, and efficient execution.

Mandate

Lead the design and buildout of a global data delivery capability aligned to the new enterprise CDO structure, enabling scalable execution of core data management and analytics functions while ensuring appropriate governance, risk oversight, and operational resilience.

Approach

  • Conducted executive stakeholder interviews across business and technology leadership to assess enterprise demand for data capabilities.

  • Designed a scalable global delivery model supporting core data management functions including metadata management, lineage, data quality, and analytics.

  • Led expansion of global delivery centres in Asia, working with regional leadership to establish talent pipelines, governance coverage, and operational controls.

  • Directed risk assessments for offshore transition of sensitive data processes to ensure appropriate regulatory and operational safeguards.

  • Managed third-party delivery partners and multi-million-dollar vendor engagements supporting capability buildout and workforce scaling.

Outcome / Results

  • Built and scaled a data delivery organisation of 500+ professionals, expanding offshore capability by ~400% to support enterprise data initiatives.

  • Achieved through optimisation of global delivery and capability distribution.

  • Enabled scalable delivery of critical data management capabilities including metadata, lineage, data quality, and advanced analytics.

Systemically Important US Banking Institution | Enterprise Chief Data Office

Global Data Operating Model Buildout

Context

The bank had recently appointed a Group Chief Data Officer to strengthen enterprise data management capabilities and improve coordination across historically decentralised data teams. Leadership required a clear enterprise data strategy to establish enterprise priorities, define core data capabilities, and guide the development of the newly formed CDO organisation.

Mandate

Support the development of the bank’s enterprise data strategy, defining strategic priorities and capability objectives to guide governance, operating model development, and future investment decisions.

Approach

  • Assessed enterprise data management capabilities and organisational structures across business divisions.

  • Engaged divisional leadership and control functions to define strategic priorities and enterprise data objectives.

  • Defined enterprise data strategy pillars covering governance, architecture, and delivery capabilities.

  • Identified priority capability gaps and improvement opportunities across the enterprise data landscape.

  • Developed a phased capability roadmap to guide implementation under the newly established CDO organisation.

Outcome / Results

  • Delivered enterprise data strategy adopted by the Group Chief Data Office.

  • Defined enterprise capability roadmap focused on business enablement, operational simplification, and regulatory alignment.

  • Aligned senior leadership around shared enterprise data management priorities guiding governance and operating model development.

Systemically Important UK Banking Institution | Group Chief Data Office

Enterprise Data Strategy Development

Context

Appointment of a new Group Chief Data Officer following years of decentralised data ownership across business divisions. The federated model had resulted in duplicated tooling, fragmented investment decisions, and limited enterprise visibility over total data spend.

Mandate

Contribute to the definition of the bank’s enterprise data strategy under the newly appointed Group CDO and establish enterprise-wide transparency over data investment — aligning capital allocation to strategic priorities and identifying opportunities to streamline spend.

Approach

  • Led dedicated workstream as part of broader enterprise data strategy programme.

  • Conducted structured interviews with divisional data leaders and senior executives to understand priorities and existing investments.

  • Developed and implemented a formal spend categorisation framework to identify data-related expenditure not captured by procurement processes.

  • Introduced enterprise “data spend” flag within procurement workflows to improve ongoing governance and visibility.

  • Identified overlapping initiatives, redundant tooling, and misaligned investment priorities across business units.

Outcome / Results

  • Consolidated and reprioritised enterprise data investments in line with the new strategy.

  • Delivered approximately 10% reduction in enterprise data investment (~$30M).

  • Strengthened governance, transparency, and executive oversight under the newly established Group CDO function.

Systemically Important UK Banking Group | Group Chief Data Office

Enterprise Data Investment Optimisation