Future CDO
Become a Chief Data Officer: The Full Training Path
Stepping into a CDO role means mastering data governance, architecture, analytics strategy and the political side of getting teams to actually use data. Here you get the full curriculum built for that exact job: dozens of lessons organized into clear blocks, from building a data catalog to running a data quality program to pitching a data strategy to your board. Nothing generic, nothing borrowed from a data analyst course and relabeled.
Alongside the lessons, you get interactive tools to model your own roadmap, an action playbook with templates you can apply the same week, and a glossary so you stop nodding along in meetings when someone says "data mesh" or "MDM." Start with the level assessment: it tells you exactly which blocks to prioritize instead of forcing you through content you already know.
Lessons for this profile
- From IT asset to business weapon: the CDO's evolution
- Defining your data vision and north star
- Calculating the ROI of data initiatives
- Using data for competitive intelligence at scale
- CDO in financial services: when regulation is your architecture
- The CDO org chart: where you sit changes everything
- DAMA-DMBOK demystified: what actually matters for CDOs
- Data quality dimensions: why 'good enough' destroys trust
- GDPR in practice: the 10 mistakes CDOs make most often
- Data contracts: the new standard for quality agreements between teams
- Data breach playbook: what to do in the first 72 hours
- Setting up a Data Governance Council that doesn't become theater
- Data warehouse, lake & lakehouse: choosing the right architecture
- Data mesh: principles, success conditions & criticisms
- dbt (data build tool): industrialized SQL transformation
- Batch vs streaming: choosing the right paradigm
- Active metadata and the data catalog
- Cloud data infrastructure: services, costs & migration
- Modern BI: tools, maturity & the semantic layer
- Decision intelligence: decision architecture & embedded analytics
- Experimentation at scale: A/B testing, causality & platforms
- From dashboards to decisions
- The metrics & semantic layer
- Dashboard design: principles and KPIs that drive decisions
- CDO AI strategy: prioritization, build/buy & value chain
- NLP à l'échelle : Voice of Customer, analyse de sentiment et text mining
- Enterprise GenAI use cases and value
- AI risk & the EU AI Act
- Closing the POC-to-production gap
- Generative AI in the enterprise: RAG, risks & governance
- Data monetization: three modes & the data flywheel
- Partenariats Data : types, due diligence et privacy-preserving technologies
- The data-as-a-product mindset
- Value metrics and adoption
- Data productization: pricing, distribution & business case
- Product management for data
- Data maturity & organizational design: assessment and sequencing
- Data literacy: building analytical capability across the organization
- Driving cultural change to data-driven
- Hiring data talent and the skills map
- Embedded analytics, Data Council & data career ladder
- Decision rituals: getting data into the room
- The CDO role: four archetypes, first 100 days & C-suite relationships
- Gestion de crise data : incidents, communication et résilience
- Communicating with the board and c-suite
- The CDO's first 90 days
- Data strategy: build, communicate and sustain the roadmap
- Durabilité de la fonction data : succession, développement personnel et legacy