A series of 11 articles telling one story about data production — from defining target groups and shaping data portfolios to building production engines, steering content, and connecting to commercial outcomes.
Why the differentiator isn't data depth — it's use case coverage. And why that's what separates you from any AI-only solution.
Defining target groups, mapping their use cases, and making the hard call on which ones to serve.
An evaluation framework across strategic, financial, and production attractiveness. Managing data types as a portfolio, not a backlog.
Leading dimensions: industries, use cases, functional excellence, or data types? How to shape the org and hire the right talent.
How to actually produce data — modeling, surveying, collecting. AI-enabled tooling. Where humans stay in the loop.
The ongoing prioritization within your portfolio. Industries, countries, topics. The monthly mechanism that keeps production aligned to customer value.
How to measure and improve production quality. Why you can't QA your way out of a production design problem.
How to collect the right signals to continuously inform production steering. Feedback loops that actually work.
The strategic choice that shapes everything downstream — and how it changes your relationship with Product and Sales.
Customer-facing products (API, ecosystem, platform), pricing models, packaging, and the Sales collaboration.