Part VI
Operating the D3M System
Turning analyses into durable infrastructure
This closing part is about ownership rather than technique. Every earlier part produced something — a chart, a causal estimate, a churn score, an extraction pipeline — but analyses left lying around decay the moment their author moves on. It reframes every card, memo, dashboard, and case pack as a data product with a name, an owner, a version, a contract, and a refresh cadence, then routes them into the one-page decision memo a sponsor can sign, and scales monitoring from a single model to the whole portfolio. The part — and the book — lands on the Bean & Basket expansion case, which runs one strategic question down the full decision ladder so that data, visual evidence, causal effects, algorithms, and AI converge on a memo that ships.
1 chapter · 4 articles
What you’ll learn
- Repackage any analysis as a governed data product with an owner, version, contract, and refresh cadence
- Compress a body of evidence into a one-page decision memo a sponsor can read and sign
- Scale monitoring from a single model to a portfolio, catching drift and silent decay before stakeholders do
- Diagnose the learning-loop failures that let an analytics system censor its own training data
- Drive a real strategic question down the full decision ladder, integrating every prior part into one shippable recommendation
In this part
Turning a stack of analyses into an operating system that compounds instead of decays.