Data strategy is the roadmap that connects organizational ambition to data reality. A great data strategy answers three questions: Where are we today? Where do we need to be? How do we get there?
Most data strategies fail not because they're wrong about the destination, but because they underestimate the gap between current state and target state, and overestimate organizational capacity for change.
A complete data strategy has six components:
1. Business alignment: What are the top 3-5 business priorities for the next 3 years? How does data enable each of them? This is the foundation, a data strategy that doesn't explicitly connect to business priorities gets deprioritized when resources are constrained.
2. Current state assessment: Data maturity across the five dimensions (strategy, architecture, governance, talent, culture). What exists, what works, what doesn't.
3. Target state definition: What does the data organization look like in 3 years? What capabilities exist? What level of self-serve analytics? What AI use cases are in production?
4. Gap analysis: Specific gaps between current and target state, each with estimated effort and impact.
5. Roadmap: Phased plan to close the gaps. Year 1: foundations. Year 2: capabilities. Year 3: scale and differentiation. Each phase has specific deliverables and milestones.
6. Governance model:
Knowledge check
1. According to the lesson, why do most data strategies fail?
2. Why is business alignment described as the foundation of a data strategy?
3. When communicating the strategy to the CEO, what should take priority on the slides?
4. Select ALL statements that correctly describe components of a complete data strategy.
Select all the correct answers.
5. Select ALL correct statements about communicating a data strategy to different executives.
Select all the correct answers.
A data strategy that isn't understood and supported by the executive team exists only on paper. Communication is as important as content.
For the CEO: Business outcomes first. Revenue impact, cost reduction, competitive advantagecompetitive advantageA lasting edge over competitors: a resource, capability or position they cannot easily replicate, letting a firm earn above-average returns over time.View full definition →, risk mitigation. Technical details are appendix material, not slide material.
For the CFO: Investment required, ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → timeline, risk-adjusted returns. The CFO needs to justify the investment to the board, give them the language to do so.
For the board: Two slides maximum. What does winning look like in 3 years (data-enabled revenue, competitive positioningpositioningThe mental space you want your brand to occupy in your target customer's mind relative to alternatives.View full definition →)? What are we investing in year 1 to get there?
For business unit leaders: What specific capability gets built for their function, by when, and what does it enable them to do that they can't do today?
For the data team: The full technical roadmap. Architecture decisions, platform investments, talent plan, governance framework.
The CDO who can present the same strategy at multiple levels of abstraction, from board summary to technical deep dive, demonstrates the leadership range the role requires.
An effective 3-year roadmap is organized around capabilities, not projects:
Year 1, Foundation: Data catalogData catalogA centralized inventory of an organization's data assets, enriched with metadata, that helps people find, understand, and trust the data they need.View full definition → launched, data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.View full definition → monitoring in place, core data warehousedata warehouseA central repository that consolidates data from many source systems into a structured, query-optimized store designed for analytics, reporting, and business intelligence.View full definition →/ operational, foundational governance framework documented and communicated, key data definitions standardized, 2-3 high-value analytics use cases delivered.
Year 2, Capability: Self-serve analytics platform available to business users, 5-7 predictive models in production, MLOpsMLOpsMachine Learning Operations: combining ML and DevOps practices to industrialise, deploy, monitor, and retrain models reliably in production.View full definition → framework operational, data literacy program launched, embedded analyst model piloted in 2 business units.
Year 3, Scale: Advanced AI capabilities in production, data productdata productA data asset managed like a product, with an owner, defined users, guaranteed quality, and measurable business value.View full definition → program launched, external data monetization first revenue, full self-serve analytics, data literacy program company-wide.
This structure is deliberately conservative. CDOs who promise Year 3 capabilities in Year 1 consistently damage their credibility.
Data strategy is not industry-agnostic. Key variations:
Financial services: Regulatory compliance dominates. BCBS 239 (banking data risk management), GDPR, and sector-specific reporting requirements shape the governance architecture before the analytics strategy.
Healthcare: Patient data sensitivity and HIPAA constraints shape every architectural decision. Real World Data as a strategic asset. AI in clinical decision supportdecision supportTechnologies and processes that turn raw data into actionable insights via reporting, dashboards and analysis, so teams can decide based on facts rather than intuition.View full definition → as an emerging priority.
Retail: Customer data is the core strategic asset. Personalization, demand forecasting, and increasingly retail media networks as data revenue streams.
Manufacturing: Operational data (IoT, production systems) combined with supply chain data. Predictive maintenance, quality control, and supply chain optimization as primary use cases.
1. Quelle est la composante d'une stratégie data qui est le plus souvent sous-estimée par les CDOs qui échouent à livrer ?
A) L'alignement business
B) La définition de l'état cible
C) L'analyse des gaps, sous-estimer la distance entre l'état actuel et l'état cible, et la capacité d'absorption organisationnelle
D) La gouvernance
Réponse: C
2. Comment un CDO doit-il adapter sa communication de la stratégie data pour le CEO ?
A) Présenter tous les détails techniques pour démontrer la rigueur
B) Se concentrer sur les outcomes business (revenus, coûts, avantage compétitifavantage compétitifA lasting edge over competitors: a resource, capability or position they cannot easily replicate, letting a firm earn above-average returns over time.View full definition →), les détails techniques en annexe
C) Utiliser le même niveau de détail qu'avec l'équipe data
D) Présenter uniquement les risques et la compliance
Réponse: B
3. Quelle est la structure recommandée pour un roadmap data 3 ans ?
A) Déployer toutes les capacités avancées en année 1 pour démontrer la valeur rapidement
B) Se concentrer uniquement sur la gouvernance les 3 premières années
C) Année 1 : fondations → Année 2 : capacités → Année 3 : scale et différenciation
D) Adapter le roadmap chaque trimestre selon les priorités immédiates
Réponse: C