> tpl_aim_012
Analytics and BI Requirements Document
Comprehensive Business Intelligence (BI) and analytics product requirements document detailing reporting user personas, dashboard wireframes, drill-down grains, data freshness SLOs, and query latency benchmarks.
Structured BI specification aligning executive, operational, and tactical stakeholder reporting needs with underlying dimensional fact tables, refresh schedules, and data filters.
Important Tech Document Template & Operational Notice
TinyCTO.tv Tech Document Template Notice: This template is a general educational and operational starting point. It is not legal, tax, accounting, investment, procurement, regulatory, security or certification advice. Requirements vary by jurisdiction, organization, contract and risk. Review and adapt it with qualified professionals before relying on it.
Problem Solved
Engineering teams build complex, costly BI dashboards without documented business requirements, resulting in slow query performance, misaligned metric definitions, and zero user adoption.
When to Use
- •Commissioning new executive, operational, or customer-facing analytical dashboards
- •Migrating legacy reporting platforms (e.g. Cognos, SSRS) to modern cloud BI (Looker, Tableau, Power BI)
- •Contracting external analytics engineering agencies for data warehouse delivery
When NOT to Use
- •For one-off ad-hoc SQL exploratory data queries that will never be scheduled as a production report
- •For real-time operational alerting scripts monitoring CPU and memory utilization (use TPL-OPS-001)
5 Template Sections & Structural Outline
Target audience (Executive, Regional Manager, Financial Auditor), key decisions enabled, and business ROI.
Listing all displayed metrics, calculations, benchmark targets, and comparison time windows (MoM, YoY).
Fact table grain (e.g. single transaction line-item), slice-and-dice dimensions, and hierarchical drill paths.
Query response latency (< 2.5 seconds p95), data cache duration, and scheduled refresh frequencies.
Role-based access permissions, tenant isolation, dynamic row-level security (RLS), and CSV export rules.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
Analytics and BI Requirements Document - Worked Case Study
Fictional Entity: SaaS Platform Executive ARR & Customer Health Dashboard
Real-world production case study demonstrating complete operational adoption for SaaS Platform Executive ARR & Customer Health Dashboard.
- •Delivered consolidated C-level dashboard replacing 14 disjointed spreadsheet reports
- •Achieved 1.4s p95 query latency across 80M underlying billing and usage transaction records
- •Implemented dynamic Row-Level Security (RLS) ensuring regional directors only access their designated territories
Frequently Asked Questions
What is the role of Row-Level Security (RLS) in enterprise BI?
Row-Level Security restricts data row visibility based on the logged-in user's credentials (e.g. a European sales director can only view EMEA revenue figures in the exact same Tableau workbook where an APAC director views Asia-Pacific data).
Why is separating Fact grain from Dimension hierarchy essential in BI requirements?
Specifying the exact grain of the underlying fact table (e.g. one row per basket transaction vs one row per scanned item) prevents double-counting and aggregation errors when users apply slicing filters across dimensional hierarchies.
How should query latency benchmarks be specified in a BI PRD?
Requirements should state quantitative percentiles under realistic concurrent user loads (e.g. "95% of dashboard filter interactions must render in under 2.5 seconds with 50 concurrent active users").
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Authoritative Sources
- BABOK Guide v3 - Requirements Analysis and Design DefinitionInternational Institute of Business Analysis • OFFICIAL REQUIREMENT
- TDWI Business Intelligence and Analytics Best PracticesTransforming Data With Intelligence • OFFICIAL REQUIREMENT