菜单
Structured requirements elicitation for analysis requests. Use when scoping new analysis projects, clarifying ambiguous business questions, or documenting analysis acceptance criteria with stakeholders.
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基于 SOC 职业分类
| name | stakeholder-requirements-gathering |
| description | Structured requirements elicitation for analysis requests. Use when scoping new analysis projects, clarifying ambiguous business questions, or documenting analysis acceptance criteria with stakeholders. |
At the start of any non-trivial analysis request, especially when the ask is vague ("can you look into X?"), when multiple stakeholders have a stake in the outcome, or when the result will drive an important decision. Spending 30 minutes on requirements prevents days of rework.
assets/interview_guide.md to surface: the business decision being made, who the audience is, what "done" looks like, and what constraints exist.references/decision_maker_framework.md to classify the decision (strategic / operational / tactical) and calibrate the required rigour and format.assets/requirements_doc_template.md covering: business question, success criteria, scope inclusions/exclusions, data sources, and timeline.references/elicitation_techniques.md for any requirement still unclear after the interview (5-whys, scenario walkthrough, MoSCoW prioritisation).assets/analysis_brief_template.md, which becomes the authoritative scope document for the project.requirements_doc_template.md)analysis_brief_template.md)Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
Trace and resolve discrepancies when the same metric shows different values in two or more sources. Use before reporting, after pipeline changes, or when stakeholders question a number.
Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.
Translate SQL queries into plain language business logic. Use when documenting queries, explaining analysis to non-technical stakeholders, code reviewing for correctness, or building a query catalog.