How to Write a Data Requirements Doc
A data requirements document defines what you're trying to achieve before building anything. It prevents the all-too-common scenario: spending months building something that doesn't actually solve the problem.
Why This Matters
Without clear requirements: - Teams build different things thinking they're aligned - Stakeholders expect features that were never discussed - Scope creeps because "just one more thing" never stops - Success is undefined, so you never know if you've achieved it
A good requirements doc creates alignment upfront. It's cheaper to iterate on a document than on code.
The Core Sections
1. Problem Statement What problem are we solving? Why does it matter? What's the cost of not solving it?
"Marketing spends 10 hours weekly manually compiling campaign reports from 5 different platforms. Data is often stale by the time it reaches leadership."
Not: "We need a marketing dashboard."
2. Goals and Success Criteria How will we know if we've solved the problem? Be specific and measurable.
"Reduce report compilation time from 10 hours to 1 hour weekly. Reports reflect data no more than 24 hours old."
Not: "Faster reporting" or "better visibility."
3. Scope What's included? More importantly, what's NOT included?
"In scope: Google Ads, Facebook Ads, LinkedIn Ads, organic social. Out of scope: Email marketing (Phase 2), offline campaigns (no data source)."
Scope creep is the project killer. Define boundaries early.
4. Data Sources Where will data come from? What systems? What format? How often is it updated?
"Google Ads via API (updated hourly), Facebook via Fivetran (daily sync), LinkedIn via manual export (weekly)."
This surfaces integration challenges before you start building.
5. Key Metrics and Calculations What exactly should be calculated, and how?
"Cost per Lead = Total Spend / Total Leads (where Lead = form submission with valid email)"
Definitions matter. "Revenue" can mean five different things. Spell it out.
6. Users and Access Who uses this? What do they need to see? What should they NOT see?
"Marketing team (full access), Leadership (summary view only), Sales (read-only, no spend data)"
Access control is easier to design upfront than retrofit.
7. Deliverables What exactly will be delivered? Be concrete.
"1. Automated daily data pipeline to Snowflake. 2. Looker dashboard with 5 specified views. 3. Weekly email report to marketing-leadership@company.com"
Not: "A marketing analytics solution."
8. Timeline and Milestones When is it needed? What are the checkpoints?
"Week 2: Data pipeline complete. Week 4: Dashboard draft for review. Week 6: Launch to full team."
9. Assumptions and Dependencies What must be true for this to work? What could block us?
"Assumes: Fivetran license approved by IT. API credentials available. Historical data back to January 2024 exists."
Surface risks early.
Common Mistakes
Too vague. "We need better analytics." Push for specifics.
Too detailed. Implementation details belong elsewhere. Focus on what, not how.
Missing success criteria. If you don't define success, everything and nothing qualifies.
Scope everything. Trying to solve all problems at once. Start small.
Skip stakeholder input. Requirements should come from users, not just the person writing the doc.
Treat it as final. Requirements evolve. The doc is a starting point, not a contract carved in stone.
The Process
1. Interview stakeholders. What problem are they trying to solve? How do they work today?
2. Draft the document. Start with problem and goals. Add detail iteratively.
3. Review with stakeholders. Does this match what they described? Anything missing?
4. Review with implementers. Is this feasible? What's unclear? What's missing?
5. Iterate. Revise based on feedback. Get sign-off.
6. Maintain it. As requirements change, update the doc. Keep it as source of truth.
Template Skeleton
``` # Project Name - Data Requirements
Problem Statement [What problem? Why does it matter? What's the cost of inaction?]
Goals & Success Criteria [Specific, measurable outcomes that define success]
Scope In scope: - [Item] Out of scope: - [Item]
Data Sources | Source | Access Method | Update Frequency | Notes | |--------|---------------|------------------|-------| | ... | ... | ... | ... |
Key Metrics & Definitions | Metric | Definition | Calculation | |--------|------------|-------------| | ... | ... | ... |
Users & Access | User Group | Access Level | Notes | |------------|--------------|-------| | ... | ... | ... |
Deliverables 1. [Specific deliverable] 2. [Specific deliverable]
Timeline | Milestone | Target Date | |-----------|-------------| | ... | ... |
Assumptions & Dependencies - [Assumption/dependency]
Open Questions - [Question to be resolved] ```
A Final Note
The document isn't the goal. Alignment is the goal. If stakeholders and implementers understand what they're building and why, the document did its job.
Don't let process overhead kill velocity. A one-page requirements doc for a small project is fine. Match the rigor to the stakes.
Requirements are the start. Learn about why data projects take longer than expected and Agile approaches to iteration.
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Sources: - Smartsheet: How to Write a Data Requirements Document - IBM: Business Requirements Document - Datamation: Data Requirements