BI Dashboards

What you’ll learn and why it matters

As a Data Analyst, BI dashboards are your most visible deliverables. They turn raw tables into decisions by showing the right metrics, filters, and context. Strong dashboarding skills help you align teams on KPIs, monitor performance in real time, spot anomalies quickly, and tell a credible story with data.

• Translate business questions into measurable KPIs and clear visuals.
• Model data so measures are fast, accurate, and consistent.
• Build filters, drilldowns, and hierarchies that invite exploration.
• Publish, refresh, secure, and maintain dashboards at scale.

Who this is for

• Data Analysts who need to deliver stakeholder-facing dashboards.
• Business Analysts and Ops leads who want reliable self-serve reporting.
• Engineers or scientists who must present insights to non-technical audiences.

Prerequisites

• Basic SQL (SELECT, WHERE, GROUP BY, JOIN).
• Comfort with spreadsheets (aggregations, pivots) and data types.
• Familiarity with at least one BI tool (e.g., Power BI, Tableau, Looker, Metabase). Tool specifics vary, but the concepts here are universal.

How BI dashboards fit a Data Analyst’s job

Dashboards are the operational heartbeat of analytics. You’ll gather requirements, define KPIs, model data, build visuals, secure access, schedule refreshes, and iterate through stakeholder feedback. Mastering these steps unlocks autonomy and trust: stakeholders rely on your dashboards to run the business.

Practical roadmap

1. Clarify the decision. Document the business goal, key users, and the actions they’ll take using the dashboard. Capture “north star” and supporting KPIs.
2. Define metrics precisely. Write metric formulas, time windows, filters, and grain. Create a living glossary.
3. Model the data. Design a star schema: facts for events/transactions; dimensions for entities (date, product, customer).
4. Prototype with sample visuals. Sketch layout: top-level KPIs, trendline, breakdowns, and a diagnostic table. Keep it minimal at first.
5. Build filters and drilldowns. Implement core slicers (date, region, product). Add hierarchies (e.g., Month → Week → Day) and drill-through pages for investigation.
6. Optimize performance. Pre-aggregate where possible, remove unused visuals, reduce queries, and validate cache/refresh strategy.
7. Secure and publish. Set row-level security, share with the right groups, certify datasets, and document assumptions.
8. Iterate with feedback. Timebox a review cycle; triage requests; version updates with release notes.

Worked examples

-- Grain: week. Users counted by user_id. Purchases within 30 days of signup.
WITH signups AS (
  SELECT user_id, DATE_TRUNC('week', signup_at) AS wk
  FROM users
  WHERE signup_at >= CURRENT_DATE - INTERVAL '180 days'
),
purchases AS (
  SELECT DISTINCT user_id
  FROM orders
  WHERE order_at >= CURRENT_DATE - INTERVAL '180 days'
),
cohort AS (
  SELECT s.wk,
         COUNT(DISTINCT s.user_id) AS signup_users,
         COUNT(DISTINCT CASE WHEN p.user_id IS NOT NULL THEN s.user_id END) AS converted_users
  FROM signups s
  LEFT JOIN purchases p ON p.user_id = s.user_id
  GROUP BY 1
)
SELECT wk,
       converted_users::decimal / NULLIF(signup_users, 0) AS conversion_rate
FROM cohort
ORDER BY wk;

-- SQL example for rolling average
SELECT d.date,
       AVG(d.dau) OVER (
         ORDER BY d.date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
       ) AS dau_7d_avg
FROM daily_dau d
ORDER BY d.date;

-- Example predicate for a BI tool that supports SQL-like RLS rules
-- Assume a user attribute current_region is provided by the identity provider
region = {{ user.current_region }}

Drills and exercises

• [ ] Write a one-sentence decision statement for a dashboard (what decision, by who, how often).
• [ ] Define three KPIs with exact formulas, filters, and time windows.
• [ ] Sketch a layout with 3 KPI tiles, 1 trend, 2 breakdowns, and a diagnostic table.
• [ ] Create a Date hierarchy and enable drilldown on at least one visual.
• [ ] Add two slicers (e.g., Region, Product) and verify they filter all relevant visuals.
• [ ] Measure load time; remove or optimize the slowest visual until render < 3–5 seconds.
• [ ] Set an incremental refresh or extract schedule; document expected data latency.
• [ ] Implement a simple RLS rule and test with two personas.
• [ ] Publish a draft and run a 15-minute stakeholder review; record top 3 changes.

Common mistakes and quick debugging tips

• Ambiguous metrics. Fix: Add grain, timeframe, and filters to every definition; publish a glossary.
• Too many visuals. Fix: Prioritize; start with KPIs and one narrative flow. Remove charts that don’t change decisions.
• Slow performance. Fix: Pre-aggregate, reduce high-cardinality fields in visuals, limit cross-filters, cache extracts.
• Broken drilldowns. Fix: Verify relationships, filter directions, and that hierarchies use the Date dimension rather than text fields.
• RLS leaks. Fix: Test with real personas, include all joins in the security path, deny-by-default when uncertain.
• Stale data. Fix: Check last refresh time on the dataset, credentials, gateway/connection health, and schedule frequency.
• Change chaos. Fix: Version releases with notes; test in a staging workspace; communicate changes in advance.

Mini project: Executive Revenue Pulse

Build an executive dashboard to monitor revenue health weekly.

Next steps

• Add anomaly detection (simple thresholds or moving-average bands) to key KPIs.
• Practice data storytelling: pair each KPI with a one-line insight.
• Extend governance: certify datasets, add data lineage notes, and ownership info.
• Prepare to take the skill exam to validate your knowledge and identify gaps.

Take the skill exam

This short exam checks your understanding of BI dashboard fundamentals. Anyone can take it; logged-in users will have their progress saved.