What is Requirements Translation for BI Developers?
Requirements Translation is the BI developer’s skill of turning vague business asks into precise, testable, and buildable specifications. You take stakeholder goals (e.g., “track active customers”) and translate them into metrics, data rules, visuals, filters, refresh schedules, and acceptance criteria. This de-risks delivery, speeds up development, and ensures reports drive decisions—not confusion.
Why it matters in BI Developer work
- Reduces rework by aligning on definitions before building.
- Enables consistent metrics across dashboards and teams.
- Surfaces data gaps early, so you plan fixes instead of firefighting later.
- Builds trust: stakeholders know what they’ll get and how numbers are calculated.
Typical tasks this skill unlocks
- Define KPIs and calculation logic (grain, time windows, filters, edge cases).
- Map requirements to visuals and interactions that answer the business question.
- Specify refresh cadence, alerting windows, and data latency tolerances.
- Create acceptance criteria so QA and sign-off are fast and objective.
What you’ll be able to do
- Convert business questions into unambiguous metrics with formulae and examples.
- Design dashboards that reflect the job-to-be-done, not just pretty charts.
- Document filters, segments, and drill paths that match stakeholder decisions.
- Set realistic refresh schedules and communicate latency trade-offs.
- Write acceptance criteria that make testing and sign-off straightforward.
Who this is for
- BI Developers and Analytics Engineers delivering dashboards or semantic models.
- Data Analysts who frequently translate stakeholder asks into reports.
- Product/Data PMs who want cleaner handoffs to BI teams.
Prerequisites
- Basic SQL (SELECT, JOIN, GROUP BY, COUNT DISTINCT).
- Familiarity with at least one BI tool (e.g., Power BI, Tableau, Looker) concepts: measures, dimensions, filters, drilldowns.
- Comfort discussing business processes (sales funnel, user lifecycle, etc.).
Learning path (practical milestones)
Capture the decision, audience, and time horizon. Draft a one-sentence “decision statement.”
Write formulas, grain, windows, inclusions/exclusions, examples, and edge cases. Note default filters and segments.
Choose chart types suited to the question. Specify drill paths and required tooltips/annotations.
List source tables, join keys, data quality risks, and mitigation or placeholders.
Define schedule, expected data arrival, and acceptable staleness. Document SLAs.
Build a thin slice or wireframe. Validate with real data samples. Adjust rules quickly.
Write pass/fail checks for KPIs, visuals, filters, and performance. Confirm sign-off owners.
Worked examples (BI-focused)
Example 1 — Translate "We need more active users"
Business ask: “We need more active users.”
Translation:
- Primary KPI: 7-day Active Users (7DAU) = count of distinct user_id with at least 1 session in the last 7 days.
- Breakdowns: platform (iOS/Android/Web), country.
- Default filter: exclude test accounts (email domain contains “@test”).
- Time axis: last 12 weeks (daily).
- Goal: +15% MoM growth.
- Acceptance example: On 2025-06-01, 7DAU should equal distinct users with sessions between 2025-05-26 and 2025-06-01.
Example 2 — KPI rule with SQL
Goal: Daily purchase conversion rate = purchasing sessions / visiting sessions.
WITH daily AS ( SELECT event_date, COUNT(DISTINCT CASE WHEN event_type = 'visit' THEN session_id END) AS visits, COUNT(DISTINCT CASE WHEN event_type = 'purchase' THEN session_id END) AS purchasing_sessions FROM events GROUP BY event_date)SELECT event_date, purchasing_sessions * 1.0 / NULLIF(visits, 0) AS conversion_rateFROM daily; Notes: Use COUNT DISTINCT by session_id to avoid double counting multiple events within a session. Document whether cancellations/refunds affect numerator.
Example 3 — Map requirements to visuals
Ask: “Spot weekly revenue dips by market.”
- Visual 1: Weekly revenue trend (line) with reference band for target range.
- Visual 2: Heatmap of week vs. market to highlight dips.
- Interaction: Click a dip point to filter the heatmap to affected markets.
- Tooltip: Show revenue, % vs. last week, and top 3 product categories.
Example 4 — Filters and segments
Requirement: “New vs. Returning customers, last quarter.”
- Definition: New = first purchase within the quarter and no purchase in prior 90 days; Returning = otherwise.
- Default filters: Paid channels only; exclude employee purchases.
- Segment controls: Channel, Region, Device.
Example 5 — Refresh cadence and latency
- Data readiness: Orders table lands at 02:00 UTC; attribution table at 03:30 UTC.
- ETL duration: ~60 minutes.
- Report SLA: ready by 05:00 UTC on weekdays.
- Plan: Start refresh 03:45 UTC (buffer 15 min). If attribution is late, tag dashboard “Attribution delayed” and surface previous day.
Example 6 — Identify data gaps early
Scenario: Stakeholders want LTV by campaign, but historical campaign membership is missing before March.
- Gap: campaign_id null before 2025-03-01.
- Mitigation: provide LTV since acquisition only for cohorts after 2025-03-01; earlier cohorts show “insufficient attribution history.”
- Action: add backlog item to backfill campaign mappings from archive logs.
Drills (quick practice)
- Rewrite one vague ask into a specific KPI with formula, grain, time window, filters, and an example date check.
- List all default filters for a dashboard you maintain, and justify each.
- For one KPI, specify at least three edge cases and what to do (include/exclude).
- Sketch two different visual options that answer the same question; note trade-offs.
- Define a refresh plan that includes source arrival, buffer, duration, and SLA.
Common mistakes and debugging tips
Mistake: Ambiguous metric definitions
Fix: Write the formula with numerator/denominator, unit, grain, window, inclusions/exclusions, and one concrete example row/date.
Mistake: Double counting
Fix: Prefer COUNT DISTINCT at the intended grain (user_id, session_id, order_id). Validate totals with a small hand-check sample.
Mistake: Ignoring late-arriving data
Fix: Document lateness behavior (e.g., attribution data can lag by 24h). Use backfills or grace periods before locking daily numbers.
Mistake: Visuals don’t answer the question
Fix: Tie each visual to a specific decision. Remove or demote anything decorative.
Mistake: Unclear acceptance criteria
Fix: Write pass/fail tests with exact expected values or rules (e.g., totals match finance close file within 0.5%).
Mini project: KPI spec to prototype
Goal: Turn a stakeholder ask into a spec and a working prototype that can be validated.
- Pick an ask: “Understand weekly trial-to-paid conversion by channel.”
- Write the spec: define conversion, time window, grain, filters, segments, edge cases (free extensions, cancellations).
- Check sources: signups, subscriptions, payments; identify join keys; note any gaps.
- Create a thin-slice dataset (SQL) with 2–4 weeks of data and essential fields.
- Prototype visuals: line trend + bar by channel; add tooltips with counts and rates.
- Add acceptance checks: sample day calculations by hand; totals match known counts.
- Share for feedback; iterate once to finalize.
Mini project checklist
- Decision statement and audience captured
- KPI formula with example date
- Default filters and segments listed
- Data source map and gaps documented
- Refresh cadence and latency noted
- Prototype visuals created
- Acceptance criteria written
Practical projects
- Executive KPI one-pager: 3–5 metrics with tight definitions, weekly refresh, mobile-friendly visuals.
- Funnel quality dashboard: conversion at each step with drop-off reasons, segmented by campaign and device.
- Cohort retention report: monthly cohorts with retention curves, filters for market and product line.
Subskills
- Converting Business Questions Into Metrics — Turn decisions into metrics with clear purpose and units.
- Defining KPI Calculation Rules — Specify formula, grain, window, inclusions/exclusions, and examples.
- Mapping Requirements To Visuals — Choose charts and interactions that best answer the question.
- Clarifying Filters And Segments — Define defaults, user controls, and rationale.
- Defining Refresh Cadence And Latency Needs — Plan schedule, buffers, and SLAs that match decisions.
- Identifying Data Sources And Gaps — Map tables, joins, quality risks, and mitigations.
- Prototyping And Iteration With Stakeholders — Validate quickly with thin slices and adjust.
- Acceptance Criteria For Reports — Write objective pass/fail checks for sign-off.
Next steps
- Pick one live report you own. Rewrite the KPI specs and acceptance criteria; socialize with stakeholders.
- Complete the drills above for one new metric.
- When ready, take the skill exam below. The exam is available to everyone; logged-in users will have their progress saved.