Programme overview
Introduction:
Many boards approve a data strategy that reads as a technology wish list: platforms are bought, pilots multiply and nobody can show which corporate goals the data spend advances. Leaders argue over central versus domain accountability, lake versus lakehouse and which use cases deserve money first. This Core Concept course on data strategy development equips senior leaders to connect data ambition to corporate priorities, locate where the payoff lies, choose target capabilities and the shape of the data organisation, and release funding in stages. Participants build a Data Strategy and Funded Investment Plan for their organisation.
Course Objectives:
- Translate corporate goals and competitive pressures into a small set of data ambitions that executives can endorse
- Evaluate data readiness and the gap between current data assets and strategic needs
- Prioritise a portfolio of data use cases on benefit, feasibility and time to payback with a quantified estimate for each
- Decide target data capabilities, architecture direction and sourcing options at executive level, including data mesh and lakehouse choices
- Design the data organisation with a chief data officer mandate, domain data product owners and stage-gated funding
- Produce a Data Strategy and Funded Investment Plan with payoff indicators and a data culture plan ready for board approval
Target Audience:
- Executives who set corporate strategy and must decide how much to invest in data and where
- Leaders of data, analytics or information functions accountable for data ambition and delivery
- Heads of transformation and strategy offices who shape change portfolios that depend on data
- Divisional heads who will be accountable for domain data products and the returns they generate
- Finance and investment leaders who approve and track funding for data programmes
- Technology leaders who must align platform and architecture decisions with corporate direction
Course Outline:
Day 1: Data Ambition and the Strategic Starting Point
- Corporate Goals to Data Ambition Mapping Canvas
- Data Strategy Pyramid: Vision, Objectives, Capabilities and Initiatives
- Offensive and Defensive Data Posture Choice
- Data Readiness Baseline Across Seven Capability Dimensions
- Executive Interviews and Data Pain Point Heatmap
Day 2: Benefit Estimates, Use-Case Portfolio and Data Monetisation
- Use-Case Discovery Workshops by Customer Journey and Process
- Benefit-Feasibility-Risk Scoring Grid for Use-Case Ranking
- Benefit Estimate Template: Payoff Drivers, Cost Lines and Payback Period
- Internal and External Data Monetisation Options
- Quick Wins Versus Lighthouse Initiatives Balance
Day 3: Target Data Capabilities and Architecture Choices
- Target Data Capability Map and Gap Heatmap
- Data Warehouse, Data Lake and Data Lakehouse Trade-Off Review
- Data Mesh Principles: Domain Accountability and Self-Serve Platform
- Build, Buy or Partner Decision Matrix for Data Platforms
- Cloud Spend Envelope and Vendor Lock-In Exposure Review
Day 4: Data Organisation Design, Funding and Culture Risks
- Chief Data Officer Mandate, Reporting Line and Decision Rights
- Data Product Owner Role and Data Product Lifecycle
- Federated Computational Governance Kept to One Enabling Layer
- Stage-Gated Funding Mechanism and Investment Committee Criteria
- Data Literacy Programme and Adoption Barrier Diagnosis
Day 5: Scenario Exercises and the Funded Data Strategy
- Insurance Group Scenario: Refocusing a Stalled Data Programme
- Logistics Operator Scenario: Funding Data Products by Domain
- Data Payoff Scorecard: Leading and Lagging Indicators
- Data Strategy and Funded Investment Plan Drafting
- Board Pitch Rehearsal and Peer Investment Challenge
Skills You Will Gain:
- Data Ambition Setting
- Data Readiness Diagnosis
- Benefit Estimation
- Use-Case Portfolio Prioritisation
- Data Architecture Direction
- Data Organisation Design
- Data Investment Planning
- Data Culture Leadership
Why Attend This Course:
- Return with a Data Strategy and Funded Investment Plan for your organisation, tested by a peer investment panel
- Explain to a board in plain commercial terms which data use cases come first, what they cost and when they pay back
- Settle recurring debates on central versus domain accountability and platform direction with a clear decision logic
- Compare data ambitions and funding approaches with leaders from insurance, logistics, public services and industry
Conclusion:
A data strategy earns support when every investment traces back to a corporate goal and every stage shows a measurable payoff. The course moves from setting data ambition and baselining readiness, through benefit estimates and a ranked use-case portfolio, to architecture, sourcing and data organisation decisions, stage-gated funding and data culture. The final day applies this work to cross-sector scenarios and produces a Data Strategy and Funded Investment Plan that participants present to their executive sponsors.