Documentation and Knowledge Management Questions

Requirements:

• Track assumptions (create, review, active, deprecated)
• Lightweight, low-friction for BI teams and stakeholders
• Integrates with dashboards (metadata) and sprint planning
• Searchable, auditable, and linkable to reports

High-level design:

• Small web app + Git-backed storage (Markdown/JSON) + dashboard metadata hooks
• Use existing tools where possible: Confluence/Notion or a lightweight repo (GitHub/GitLab) + simple UI (static site / lightweight app) + webhook integrations with BI tools

Assumption lifecycle & workflow:

• Create (Draft): BI analyst creates assumption with title, description, related reports, owner, rationale, date, evidence, expected impact, TTL (review date).
• Review (In review): Assigned stakeholder(s) and data steward review within N days. Review comment thread + approval vote.
• Active: Approved assumptions attached to dashboards; visible in metadata and tooltip.
• Deprecated: Marked when invalidated or superseded; archived but searchable; link to replacement assumption.

Ownership & roles:

• Owner: BI Analyst who created it — responsible for upkeep and presenting evidence at review.
• Reviewer(s): Data steward, product stakeholder, or domain SME — provides validation.
• Approver: Data governance lead for high-impact assumptions.
• Consumers: Any dashboard user (can file feedback).

Storage format:

• Single-source files in JSON or YAML per assumption stored in Git (example fields: id, title, status, owner, reviewers, related_dashboards[], created_at, review_due, evidence_links[], impact_estimate, notes).
• Rendered view in UI; Git history provides audit trail.

Integration:

• Sprint planning: Add "assumption tasks" as backlog items (review, validate, collect evidence). Link issue IDs to assumption id. Triage high-impact assumptions into sprint if review_due or linked to upcoming dashboard changes.
• Dashboard metadata: Add an assumptions panel/tooltip in dashboards (via BI tool metadata or embedded iframe). Each dashboard object includes assumption IDs and a short summary; clicking opens full assumption page.
• Automation: Scheduled jobs to notify owners before review_due; auto-tag dashboards when assumption deprecated.

Key benefits & trade-offs:

• Lightweight, uses familiar tools (Git/Confluence) to avoid heavy governance overhead.
• Trade-off: requires cultural adoption; mitigate with templates, short training, and automation for reminders and metadata syncing.

Situation/goal: Ensure a BI dashboard is fully maintainable and operable by SREs, data engineers, or new BI analysts.

Approach (what I’d deliver):

• Technical decision log: Single-page decision register (Confluence or repo Markdown) recording the problem, chosen option, alternatives considered, date, owner, and link to PR/design. Use a template: Problem | Options | Trade-offs | Decision | Owner | Date.
• Architecture diagrams: High-level and detailed diagrams in Lucidchart or diagrams.net stored versioned in the repo. High-level shows data sources → ingestion → warehouse → transformations → semantic layer → BI tool → consumers. Detailed includes table lineage, refresh schedules, SLAs.
• Runbook / operational playbook: A runnable runbook in Markdown with sections: purpose, prerequisites, normal run checks, common alerts & causes, step-by-step triage, mitigation steps, escalation matrix, rollback steps, and postmortem link. Include sample commands, queries, and screenshots.
• Ownership & on-call: Clear RACI matrix (Owner, Backup, Approver) in the doc; link to on-call rota (PagerDuty/Sheets). Assign SLAs for data freshness, dashboard availability, and incident response times.
• Rollback procedures: Explicit rollback steps per failure mode: revert dashboard version (BI tool revision history), revert ETL job/DB migration (use migrations with rollback scripts), restore snapshot (with retention policy). Include exact commands, expected side effects, and verification checks.

Tools & templates:

• Docs-as-code: Keep docs in the same Git repo as code; use Markdown + templates, reviewed via PRs.
• Confluence for long-form guides & decision register; diagrams.net / Lucidchart for visuals; GitHub/GitLab for versioning; CI hook to validate docs presence for new features.
• Monitoring/alerts: link Grafana/Datadog dashboards and query examples in the runbook.

Keeping docs current:

• Make documentation part of the definition of done for any change; require a docs PR with code changes.
• Quarterly doc reviews owned by the BI lead; automated reminders and a changelog on the decision register.
• Post-incident updates: update runbook and decision log immediately after blameless postmortems.
• Lightweight onboarding checklist for new analysts that walks through docs, sandbox, and a first-maintenance task.

Result: This structure provides clear, versioned, searchable guidance so SREs, data engineers, and new analysts can triage, operate, and safely roll back the BI dashboard with minimal ramp-up.

Decision Log: purpose — lightweight, single-source record of assumptions, stakeholders, decisions, and fallbacks for ambiguous analytics tasks so future readers know why things were built a certain way.

Suggested fields (one-line or small table per decision):

• ID: DEC-YYYYMMDD-N
• Title: short descriptive name
• Date / Author
• Context: one-sentence problem or request
• Decision: concise statement of what we chose to do
• Key Assumptions: numbered, testable assumptions (with owners & deadlines)
• Stakeholders / Approvers: names, teams, contact
• Data Sources & Gaps: datasets used and known quality issues
• Rationale: why this decision (trade-offs)
• Fallbacks / Next Steps: alternatives if assumption fails + trigger conditions
• Impact / Scope: dashboards/reports affected, downstream consumers
• Links: queries, dashboards, tickets, docs
• Status: Proposed / Accepted / Revisit date / Deprecated

Example entry 1:

• ID: DEC-20250701-01
• Title: Use order_event.timestamp for "order_time"
• Decision: Use order_event.timestamp because it exists for 99% of orders.
• Assumptions: (1) timestamp uses UTC; owner: Data Eng; verify by 2025-07-05. (2) late-arriving events < 2 hours.
• Stakeholders: Growth PM (approve), Reporting Lead
• Data gaps: missing for canceled orders
• Fallbacks: if timestamps are non-UTC, convert; if >5% missing, switch to order.created_at
• Impact: Real-time order funnel dashboard, alerts
• Links: /dashboards/order-funnel, GH-123

Example entry 2:

• ID: DEC-20250701-02
• Title: Retain only users with 2+ sessions for churn model
• Decision: Exclude single-session users from cohort.
• Assumptions: single-session users are bots/accidental; validate via session patterns by 2025-07-10.
• Fallbacks: if validation fails, include with different feature weighting.

How to use in sprint planning and handoffs

• Sprint planning: Add decision log review as acceptance criterion on tickets that depend on ambiguous choices. Use IDs in ticket descriptions so developers and analysts know which assumptions to validate in sprint. Prioritize assumptions with owners and deadlines into the sprint backlog (e.g., “verify timestamp timezone”).
• Handoffs: Deliver ticket with link to relevant decision log entry. New analysts run the checklist: confirm assumptions, check status, follow fallbacks if triggers met. During demos, call out decisions that materially affect interpretations.
  Benefits: reduces repeated discussions, speeds onboarding, creates clear validation paths and ownership, and makes retrospective fixes traceable.

1. Summary & objective — one-paragraph goal, key questions being answered, intended audience, and success criteria.
   Why: Aligns the analyst to the project purpose immediately so they make decisions consistent with stakeholder expectations instead of guessing.

2. Data sources & lineage — list of source tables/views, connection strings, refresh cadence, ETL transformations, and known data quality issues.
   Why: Prevents time lost tracing where metrics come from and avoids repeating broken assumptions; crucial for reproducing and troubleshooting metrics.

3. Definitions & metric logic — precise metric formulas (SQL/LookML/Power BI DAX snippets), filters, segment rules, and examples with sample records.
   Why: Ensures semantic consistency across reports and avoids “comparison-of-apples-to-oranges” problems that erode trust.

4. Current artifacts & access — links to dashboards, queries, notebooks, schema diagrams, saved extracts, credentials/permissions needed, and a “how to run” checklist.
   Why: Gives immediate runnable state so the analyst can view and iterate without waiting for access or hunting files.

5. Outstanding work & next steps — prioritized task list, blockers, stakeholder contacts, deadlines, and risks/assumptions.
   Why: Transfers context about what’s incomplete and what decisions remain, enabling focused execution and informed stakeholder communication.

Optional: add a 15–30 minute walk-through meeting to answer questions and transfer tribal knowledge.