Executive summary
Small advisory teams rarely have the bandwidth for heavy engineering. This note shows a minimal pipeline — ingest → standardize → signal — that uses reproducible databooks and a weekly signal dashboard to cut time-to-insight by ~40% in pilot projects. The approach balances rigor with low build-cost and uses tools the team already has (spreadsheets, lightweight ETL scripts, and simple dashboards).
Key findings
- Standard schema matters: A shared row / column schema across projects reduced onboarding friction for new datasets.
- Databook as source of truth: A single, versioned workbook simplified reconciliations and reduced duplicate work.
- Weekly signal cadence: A short dashboard with 6–8 signals (coverage, anomalies, KPI deltas) gave teams a consistent agenda for decisions.
Practical pipeline (4 steps)
- Ingest: Use a simple ETL script (Python/R/Apps Script) to pull source spreadsheets or CSVs into a staging sheet with a known schema.
- Normalize: Transform date formats, canonicalize IDs, and apply business rules in a reproducible script or macro.
- Databook: Write cleaned tables into a single databook (tabbed workbook) with a manifest sheet describing sources and version.
- Signal dashboard: Produce a one-page dashboard (Excel / Google Sheets / simple BI tool) that shows the 6–8 signals relevant to weekly decisions.
Implementation checklist
- Create a schema manifest for each project (fields, format, example values).
- Deploy a staging script to validate new uploads (format checks + basic sanity tests).
- Publish a weekly signal dashboard template and automate refresh where possible.
- Assign a ‘databook owner’ for each project responsible for version control and documentation.
When this approach is not right
If you need real-time analytics, high-frequency transactions, or heavy data science experimentation, this lightweight approach will be insufficient — escalate to a proper data engineering build.
Resources
Included in the download: databook schema (xlsx), sample pivot examples, and a short implementation checklist.