This page is part of the ForgeSDLC knowledge base — an AI-assisted, human-directed methodology for taking product work from concept to production. For the core operating model and vocabulary, see Forge SDLC overview and What is ForgeSDLC?.
Business Intelligence perspective
How business analysis adapts when the initiative centers on data, analytics, reporting, or data-driven decision making. BI initiatives require BA to address data requirements alongside — and often instead of — traditional functional requirements.
BABOK alignment: BABOK v3 Business Intelligence perspective.
Data requirements (what data exists, what quality, what insights)
User workflows
Data flows and transformations
System interactions
Data source integration and lineage
Acceptance criteria for features
Data quality criteria and validation rules
Solution performance (response time, availability)
Data accuracy, completeness, timeliness, freshness
2. Knowledge area shifts
Knowledge Area
BI Adaptation
BA Planning & Monitoring
Stakeholder analysis must include data owners, data stewards, and data consumers. Plan for data quality assessment as a BA activity.
Strategy Analysis
Current state analysis includes data landscape assessment — what data exists, where, quality, accessibility. Future state defines data-driven capabilities.
Elicitation & Collaboration
Elicit information needs (what questions do stakeholders need to answer?), not just process needs. Data profiling is an elicitation technique.
Requirements Life Cycle Management
Data requirements need version control alongside functional requirements. Data dictionaries and lineage documentation require maintenance.
Requirements Analysis & Design Definition
Data models, ETL specifications, report/dashboard specifications, KPI definitions replace or supplement use cases and process models.
Solution Evaluation
Evaluate data quality (accuracy, completeness, consistency), report adoption, decision impact — not just functional correctness.
3. BI-specific BA activities
3.1 Data requirements analysis
Activity
Description
Output
Information needs analysis
Identify what questions stakeholders need to answer with data
Information needs catalog
Data source identification
Inventory available data sources, assess accessibility and quality
Data source inventory
Data profiling
Statistical analysis of data to understand content, quality, and structure
Data quality assessment
Data modeling
Conceptual, logical, and physical data models for the analytics solution
Data models (dimensional, star schema, etc.)
KPI definition
Define measurable indicators with calculation formulas, data sources, and thresholds
KPI specifications
Report/dashboard specification
Define layout, filters, drill-down paths, data refresh frequency
Report specifications
3.2 Data quality management
Quality Dimension
Definition
Example Criteria
Accuracy
Data correctly represents the real-world entity or event
Customer email matches actual email address
Completeness
All required data is present
No null values in mandatory fields
Consistency
Same data represented the same way across sources
Date formats uniform across systems
Timeliness
Data is available when needed
Dashboard refreshes within 15 minutes of source update
Uniqueness
No duplicate records for the same entity
One customer record per customer
Validity
Data conforms to defined rules and formats
Phone numbers match expected patterns
4. BI-specific techniques
Technique
BI Usage
Data profiling
Assess quality, patterns, and anomalies in source data
Dimensional modeling
Design star/snowflake schemas for analytical workloads
KPI decomposition
Break high-level business metrics into measurable components
Dashboard wireframing
Visual prototype of report/dashboard layout and interactions
ETL specification
Define extraction, transformation, and loading rules for data pipelines
Data lineage mapping
Trace data from source through transformations to consumption
Data dictionary
Define entities, attributes, domains, and business meanings
Cohort analysis
Segment users/data by time period or attribute for trend analysis
5. BI BA artifacts
Artifact
Purpose
Where It Lives
Data dictionary
Canonical definitions for data entities and attributes
docs/product/data/
KPI specifications
Formal definitions of metrics with calculation formulas
docs/product/metrics/
Data quality rules
Validation criteria for each data element
docs/requirements/
Report/dashboard specs
Layout, filters, data sources, refresh rules
docs/product/features/
Data source inventory
Available data sources with quality assessment
docs/product/data/ or docs/product/integrations/
Data lineage diagrams
Visual representation of data flow from source to consumption
docs/architecture/
6. Common pitfalls in BI BA
Pitfall
Description
Remedy
Report factory
Building every report a stakeholder requests without understanding the decision it supports
Always ask: "What decision will this data support? What action will you take based on the answer?"
Data quality assumed
Assuming source data is clean; discovering quality issues during development or after launch
Profile data early; include data quality assessment in P1 discovery
Missing business context
Technical data model without business meaning; analysts can query but can't interpret
Maintain a data dictionary with business definitions, not just technical metadata
Vanity metrics
Tracking metrics that look good but don't drive decisions (page views, total users)
Use the metrics framework from PDLC.md §5 — focus on actionable outcome metrics