Why this matters
As a BI Developer, you will be asked for two kinds of outputs: official, repeatable business reports used by many stakeholders, and fast one-off answers that help teams decide quickly. Knowing when to build a certified report versus an ad hoc analysis protects data quality, prevents metric drift, and saves time.
- Reduce conflicting numbers by aligning on certified metrics.
- Speed up exploration with safe ad hoc guardrails.
- Promote successful ad hoc work to certified assets with confidence.
Concept explained simply
There are two modes of reporting work, each with different standards.
Certified reports in practice
- Purpose: Official, trusted, widely consumed; used for recurring decisions.
- Qualities: Documented metrics, data lineage, QA/UAT sign-off, versioning, refresh SLAs, access controls, owner.
- Distribution: Dashboards, schedules, wider audiences, often exec-facing.
- Change control: Requests, testing, and release notes.
Ad hoc reports in practice
- Purpose: Exploratory, one-off or short-lived; used for quick decisions.
- Qualities: Fast turnaround, limited audience, clear disclaimers, expiration date.
- Distribution: Shared in team channels or small meetings; not productionized.
- Upgrade path: Can be promoted to certified if usage grows.
Mental model: Factory vs Workshop
- Factory (Certified): Repeatable, standardized, safe, documented. Optimized for reliability.
- Workshop (Ad hoc): Flexible, quick, experimental. Optimized for speed.
Decision guide: Certified or Ad hoc?
Use this quick checklist. If most answers are “yes,” choose Certified; otherwise, Ad hoc.
- Will this be used by 10+ people or multiple teams?
- Will it inform recurring decisions (weekly/monthly/quarterly)?
- Does it include company-wide KPIs or sensitive data?
- Will it be shared with leadership or external stakeholders?
- Do we need refresh SLAs and change control?
Tip: Start ad hoc when uncertain. If usage grows or scope stabilizes, promote it.
Worked examples
Example 1: Monthly executive KPI pack
Classification: Certified. Reason: High visibility, recurring cadence, and company KPIs require documentation, QA, and SLAs.
Example 2: Product manager asks, “How many users tried Feature X last week?”
Classification: Ad hoc. Reason: Narrow scope, short-lived, immediate decision support.
Example 3: Sales operations wants a daily pipeline dashboard for all regions
Classification: Certified. Reason: Broad audience, operational use, daily refresh with RLS and definitions.
Example 4: Marketing wants a same-day split test read for a campaign
Classification: Ad hoc (initially). Reason: Quick exploratory check; promote later if it becomes a recurring campaign dashboard.
Promotion path: From ad hoc to certified
- Intake & scope: Confirm audience, decisions, cadence, and KPIs. Define owner and stakeholders.
- Align metrics: Map to your semantic layer or create/approve definitions. Resolve naming and calculation rules.
- Model & security: Build governed datasets, apply row-level/column-level security, and set refresh schedules.
- QA & UAT: Add tests (freshness, row counts, KPI tolerances). Run user acceptance testing with sign-off.
- Docs & catalog: Write a short description, metric glossary, data lineage, and usage notes. Add ownership and contact.
- Release: Publish to the Certified area, set access roles, schedule delivery, and announce changes.
- Operate: Monitor usage, track incidents, and maintain release notes and deprecation policy.
Guardrails and governance
Certified readiness checklist
- Metric definitions documented and approved.
- Data source lineage recorded.
- Row/column-level security in place (if needed).
- QA tests passed (freshness, volume, reconciliations).
- UAT completed with stakeholder sign-off.
- Refresh schedule and SLA defined.
- Owner and support contact assigned.
- Versioning and release notes set up.
- Accessibility and performance checked.
- Catalog entry with description and tags created.
Ad hoc safety checklist
- Use trusted, governed datasets where possible.
- Add clear disclaimers: preliminary, not production.
- Define owner and a review/expiry date.
- Limit audience to the requesting team.
- Avoid exposing PII; aggregate when possible.
- Keep calculations simple and transparent.
- Name/report tagged with “Ad hoc” and date.
- If reused >3 times or shared widely, start promotion.
Exercises
Complete these to build instincts. Your work won’t auto-save unless you’re logged in, but the exercises and the quick test are available to everyone.
Exercise 1 — Classify scenarios
Decide Certified vs Ad hoc and give a one-sentence justification.
- Board-ready quarterly performance deck.
- PM asks for user counts of a new beta feature over the last 10 days.
- Customer support needs a live queue dashboard visible to 100 agents.
- Finance wants to test a new gross margin formula on last month’s data only.
Show sample answers
1) Certified — executive use, recurring, needs SLAs and definitions. 2) Ad hoc — exploratory, narrow scope. 3) Certified — wide audience, operational, security required. 4) Ad hoc — trial of new metric; promote later if adopted.
Exercise 2 — Draft a certification checklist
For a “Global Sales Performance” dashboard, write an 8–10 item readiness checklist, propose a dataset/report name, and choose a folder placement.
Show sample answers
Checklist: metric glossary, lineage, RLS, QA tests (freshness, volume), UAT sign-off, SLA, owner, release notes, catalog entry, performance check. Names: Dataset: sales_performance_global; Report: Global Sales Performance (Certified). Folder: Certified/Sales.
Common mistakes and how to self-check
- Mistake: Treating an ad hoc analysis as a certified source. Self-check: Look for tags, disclaimers, and catalog entry. If missing, it’s not certified.
- Mistake: Skipping metric alignment. Self-check: Can you point to the metric definition and owner?
- Mistake: Publishing widely without security. Self-check: Verify RLS/CLS and access roles before sharing.
- Mistake: No SLA or monitoring. Self-check: Confirm refresh schedule, alerts, and on-call contact.
Practical projects
- Create an “Ad hoc vs Certified” decision rubric for your team and pilot it for two weeks.
- Take a frequently reused ad hoc query and promote it through the full certification steps.
- Set up a simple catalog entry template (title, owner, description, metrics, lineage) and apply it to three existing reports.
Who this is for
- BI Developers and Analysts who build dashboards, datasets, and metrics.
- Analytics Engineers who maintain semantic layers and data models.
- Team leads who approve metrics and reporting standards.
Prerequisites
- Basic BI tool skills (creating reports/dashboards, filters, permissions).
- Understanding of your core business metrics and data sources.
- Familiarity with data privacy and security basics.
Learning path
- Data Governance Basics: roles, ownership, and catalogs.
- Certified vs Ad hoc: this lesson.
- Semantic layer and metric definitions.
- Access control, RLS/CLS, and refresh SLAs.
- Change management and release notes.
Mini challenge
Pick one ad hoc report you shared recently. In 15 minutes, add: owner, expiration date, a disclaimer line, and a short note on metric sources. If more than 10 people used it or it was referenced twice in the last month, start the promotion path this week.
Next steps
- Adopt the decision guide for all new requests.
- Label current outputs into Certified or Ad hoc folders.
- Schedule a weekly 15-minute review to identify promotion candidates.
Quick Test
Ready to check your understanding? Take the quick test below. Everyone can take it for free; only logged-in users get saved progress.