0. Decision and Data Foundations
Define the decision, measurement system and reproducible evidence chain
0. Decision and Data Foundations
Analytics starts before data extraction. This module prevents three expensive errors: answering the wrong question, measuring the wrong unit and using information that was unavailable when the decision was made.
Learning outcomes
After this module, you can:
- write an analytics contract with owner, action, unit, horizon, objective and constraints;
- distinguish an entity, event, snapshot and outcome table;
- test whether each field existed at the decision timestamp;
- leave a reproducible trail from source data to recommendation.
Chapters
| Chapter | Question | Output |
|---|---|---|
| Decision Framing | Which action is genuinely under consideration? | decision table and loss function |
| Data Contracts | What does one row mean, and when was it knowable? | grain, key and timestamp audit |
| Reproducible Workflow | Can another analyst recreate and challenge the result? | analysis manifest and verification plan |
Prepare, work, follow up
- Prepare: choose one decision from work, study or public policy and name the person who acts.
- Workshop: rewrite “understand customers better” as a measurable action problem; then draw the minimum data lineage.
- Follow up: produce a one-page contract and exchange it with a peer who tries to find an undefined term.
Participation can be written rather than spoken. A spreadsheet is sufficient; Python is optional in this module.
Do not proceed with a dataset whose row grain, keys, event time or outcome-availability time cannot be stated. A polished dashboard cannot repair an unidentified population.