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Domain · Data
Turn data into an advantage.
Data is only worth the decisions it enables. The real challenge isn't technical: it's governance, trust and scaling.
This domain covers architecture, analytics, governance and AI strategy — from pipeline plumbing to the trade-offs you present to the board.
Analyst, data engineer, analytics lead or future CDO: you'll build a vision that connects the technical and the business.
The learning path
CDO Track
Progressive modules, each closed by a checkpoint. Your progress is saved.
Data strategy & the CDO role
The foundation every CDO needs: your mandate, your org chart, your 100-day plan, your data vision, how to make the business case, and how the CDO role differs across industries.
BLOCK 02Data governance & compliance
The non-negotiable foundation of every CDO's agenda: governance frameworks, data quality, Master Data Management, lineage, GDPR and global privacy regulation, data contracts, and data risk management.
BLOCK 03Modern data architecture
Design data warehouses, lakes, lakehouses, data mesh, pipelines and a modern data stack
BLOCK 04Analytics, BI & decision intelligence
Deliver dashboards, self-serve and advanced analytics to build a data-driven culture
BLOCK 05AI & machine learning strategy
Build AI, GenAI, NLP, recommendation systems and responsible AI strategies
BLOCK 06Data products & monetization
Monetize data through productization, data partnerships and infonomics
BLOCK 07Data culture & organization
Build data literacy, structure the data function, and drive data maturity
BLOCK 08CDO leadership & executive presence
Master the CDO role, executive communication, crisis management and future vision
The feed
What's moving, right now.
lag_tolerance is a budget decision now, and most teams have not made it
dbt State went generally available in September 2026, turning "rebuild everything on a schedule" into "rebuild only what changed." The savings are real, but they only land if you decide, model by model, how stale your data is allowed to be.
DataSB 947 makes human review of AI firing decisions a data problem
California now bars employers from firing or disciplining workers on the say-so of an automated system alone, and from July 2027 a human has to corroborate the output in writing. The compliance work lands on data teams, who have to prove which model touched a decision, what personal data fed it, and who reviewed it.
DataHow did Telefónica build a churn model that actually moved retention numbers?
Telefónica's data teams spent years accumulating subscriber signals before their churn models started producing revenue-grade predictions. The mechanics of what they built, and where other telcos consistently fall short, carry direct lessons for any CDO running a retention program in 2026.
Everything to actually do.
Every action from the lessons, deduplicated and organized by phase. A checklist you tick off.
Open the playbook →Test my levelWhere do you really stand?
A per-skill diagnosis, delivered as a radar. Spot your blind spots in ten minutes.
Take the test →GlossaryThe vocabulary, no fog.
The terms of the trade, clearly defined, with concrete examples.
Open the glossary →The tools
From theory to a real decision.
The daily podcast
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