BI vs Business Analytics

• Distinguish BI (reporting) from business analytics (discovery)
• Map descriptive and diagnostic work to the right tooling and cadence
• Explain how both feed from the same modeled data layer

• A company needs weekly, trustworthy numbers at scale (BI) AND the ability to ask new questions quickly (analytics).
• Failing to separate them either buries analysts in report requests or ships inconsistent self-serve numbers.
• Clear modes make governance workable: certified reports get an SLA; ad-hoc analysis gets guardrails, not bureaucracy.
• Descriptive (BI) and diagnostic/predictive (analytics) answer different questions and need different cadences.

Your routine physical (BI) checks the same vitals on a schedule and catches trends early. When something looks off, the specialist runs deeper diagnostic tests (analytics) to find the cause — then the regular checkup resumes.

• BI mode: a semantic model is built once; dashboards and paginated reports read from it and refresh on schedule with alerts and SLAs.
• Analytics mode: analysts explore the warehouse with SQL and notebooks, combining datasets and testing hypotheses beyond the built dashboards.
• Both read the same modeled facts and dimensions, so a discovery insight can be promoted into a certified dashboard.
• Governance splits by purpose: BI reports are versioned and certified; analytics queries are scoped, reviewed and frequently discarded.
• Overlap tools (semantic layers) now serve both so 'what happened' and 'why' don't fork the numbers.

• Ops reviews a nightly SLA dashboard (BI) while a revenue analyst runs a diagnostic on why conversion dipped last Tuesday (analytics).
• A churn model flags at-risk accounts (analytics); the results feed an account-health report the CS team monitors weekly (BI).
• An analyst discovers that a new pricing page correlates with higher upsell; the finding becomes a certified report two sprints later.

• Keep the modeled layer shared; let BI and analytics differ in cadence, not in definitions.
• Give BI reports owners, SLAs and alerting; give analytics scoped access and documentation expectations.
• Have a promotion path: analytical insight lives in the report catalog only after it is certified.
• Keep the number of 'evergreen' dashboards small; the rest are projects, not infrastructure.

• Trying to make every analyst request a permanent dashboard — the report catalog becomes a graveyard.
• Treating ad-hoc analysis as disposable without capturing the good definitions.
• Letting analytics rebuild metric logic in notebooks, forking the numbers from BI.
• Buying an expensive BI suite and skipping the discovery capacity analysts still need.

• If a dashboard number and a notebook number disagree, trace both to the modeled layer — one of them isn't using it.
• When ad-hoc queries keep resurfacing, that's a signal to promote the question into a certified report.
• Audit usage: dashboards nobody opens every 90 days are analytics-by-another-name or dead.