Translating Needs Into BI Specs

Why this matters

Translating stakeholder needs into BI specifications turns fuzzy requests into buildable dashboards, semantic models, and metrics. As a BI Analyst, you will often face requests like 'We need a dashboard for growth' or 'Show me churn'. Without a clear spec, teams misinterpret goals, ship the wrong metrics, or spend weeks reworking.

• Real tasks you will do: define KPIs and their formulas; map data sources; choose metric grain; document business rules; sketch visuals; write acceptance tests; set refresh SLAs; plan security and access.
• Outcome: a spec that engineers and analysts can build, test, and maintain without guesswork.

Concept explained simply

A BI spec is a bridge between intent and implementation. It translates business questions into unambiguous instructions a data team can build.

Mental model

Think in layers: Why → Who → What → How → Quality → Guardrails.

• Why: business objective and decisions supported
• Who: users, scenarios, and required drill paths
• What: KPIs, definitions, filters, and visuals
• How: sources, joins, grain, transformations
• Quality: data freshness, completeness, and validation checks
• Guardrails: security, edge cases, and acceptance criteria

1. Objective
- Problem/goal:
- Decisions enabled:

2. Users & scope
- Primary users:
- Use cases & frequency:
- Out of scope:

3. KPIs & definitions (one per metric)
- Name:
- Business meaning:
- Formula:
- Grain (per day, per account, etc.):
- Filters (included/excluded):
- Dimensions available (slice by):
- Edge cases & default values:
- Data lineage (table.field):

4. Data model & logic
- Sources:
- Joins & keys:
- Transformations & rules:
- Data freshness (SLA):

5. Visuals & interactions
- Views (charts/tables):
- Drill paths:
- Required filters:
- Layout notes:

6. Security & governance
- Row-level rules:
- PII handling:
- Access roles:

7. Acceptance criteria (tests)
- Sample calculations with expected results:
- Threshold checks (e.g., nulls & duplicates):

8. Rollout & maintenance
- Owner:
- Review cadence:
- Change log:

Worked examples

E-commerce conversion dashboard

• Objective: improve checkout conversion
• KPI: Conversion rate
• Definition: orders / sessions
• Grain: daily
• Filters: include paid and organic; exclude internal traffic
• Formula (pseudo): daily_cr = count_distinct(order_id where is_test=false) / count_distinct(session_id where is_internal=false)
• Data: web.sessions, sales.orders joined on visitor_id
• Acceptance test: on 2025-01-05, expected daily_cr ≈ 2.1% based on sample data

B2B sales pipeline health

• Objective: increase qualified opportunities
• KPI: SQL to Win Rate
• Definition: won_deals / sql_opportunities
• Grain: monthly, per sales team
• Filters: stage_date between month start/end; exclude partners
• Join: crm.opportunities join crm.accounts on account_id
• Edge case: opportunities with zero amount excluded
• Acceptance: month=2025-02, team=East, expected 21–23%

Customer support SLA

• Objective: meet first-response SLA
• KPI: % tickets responded within 4 hours
• Definition: tickets with first_response_mins ≤ 240 / all tickets
• Grain: weekly by queue
• Data: support.tickets; measure first reply from agent only
• Security: hide PII; row-level by queue owners
• Acceptance: Week 12, Queue Billing: 88–90%

How to derive formulas and grain

1. Start from the decision: what will change when the KPI moves?
2. Choose the unit of counting (denominator first), then the successful events (numerator).
3. Set the grain to match decision cadence (e.g., daily ops vs monthly strategy).
4. List exclusions and default handling (nulls, tests, refunds, duplicates).
5. Trace every field to its source table and key.

Metric: Net revenue
Fields: orders.amount, refunds.amount
Rule: net_revenue = sum(order_amount) - sum(refund_amount)
Grain: daily, per country
Edge: currency conversion at order_date FX rate

Handling ambiguity

• Ask for decisions, not just dashboards: 'What decisions will this inform?'
• Clarify user scenarios: 'Who needs this? How often?'
• Probe definitions: 'What exactly counts as a churned customer?'
• Confirm exclusions: 'Should internal traffic or test orders be excluded?'
• Set quality: 'How fresh must this be? What accuracy is acceptable?'

Edge cases and constraints

• Late-arriving data: define backfill windows and reprocessing rules.
• Time zones: set canonical time zone for grain (e.g., UTC vs local market).
• Identity stitching: document identity rules for user/account joins.
• Seasonality: prefer rolling windows to smooth volatility when relevant.

Exercises (do these before the quick test)

Progress saving note: the quick test is available to everyone; only logged-in users get saved progress.

1. Define a KPI properly
   Task: Stakeholder asks for a 'Signup Conversion' metric. Produce a spec snippet with business meaning, formula, grain, filters, and acceptance criteria.
   
   Checklist

   • Clear denominator and numerator
   • Explicit grain (daily/weekly etc.)
   • Inclusions/exclusions listed
   • Source tables and keys
   • One acceptance value or range
2. Choose the right grain and logic
   Task: Revenue by product category should match finance monthly numbers. Specify grain, currency handling, returns policy, and acceptance tests.
   
   Checklist

   • Monthly grain aligned with fiscal calendar
   • FX conversion rule with date
   • Refunds and cancellations treatment
   • Sample month reconciliation range
3. Rewrite a vague ask
   Task: 'We need a dashboard for growth'. Rewrite into objective, users, top 3 KPIs with definitions, visuals, and SLA.

Common mistakes and self-check

• Missing grain. Self-check: can someone compute it at the wrong level?
• Undefined exclusions. Self-check: list test data, staff usage, and edge events.
• Ambiguous joins. Self-check: write the join keys per source.
• No acceptance tests. Self-check: provide one date/team with expected value or range.
• Misaligned freshness. Self-check: confirm decision cadence vs refresh SLA.

Practical projects

• Spec and build: Marketing funnel dashboard with at least 5 KPIs, daily grain, and 6 acceptance checks.
• Reconciliation: Create a spec that reconciles BI revenue to finance GL with documented differences and tests.
• Support SLA: Draft a spec including row-level security and a privacy section for PII handling.

Who this is for

• BI Analysts and Analytics Engineers designing dashboards and semantic layers
• Product Analysts translating product questions into KPIs
• Data PMs coordinating measurement plans

Prerequisites

• Basic SQL and joins
• Understanding of dimensions, facts, and metric grain
• Ability to read simple data models

Learning path

1. Practice defining KPIs with clear numerator/denominator and grain
2. Map fields to sources and write transformation rules
3. Add acceptance tests and data quality checks
4. Draft visuals and interactions that match decisions
5. Review with stakeholders; iterate and finalize spec

Next steps

• Do the exercises above and compare to the provided solutions
• Take the quick test below to check understanding
• Apply the template to one real business request

Mini challenge

Given a churn analysis request, produce a 1-page spec that includes: churn definition, grain, cohort logic, exclusions, and one acceptance test using last quarter data.