analysis-documentation

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Original

English
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Translation

Chinese

Analysis Documentation

分析文档

When to use

使用场景

  • Finalising an analysis before sharing it with stakeholders
  • Handing off an analysis to another team member or to a future self
  • Archiving recurring analyses so they can be run again consistently
  • Preparing for peer review or a formal audit
  • Converting an exploratory notebook into a reference document
  • 在与利益相关方分享分析成果前完成最终整理
  • 将分析工作交接给其他团队成员或未来的自己
  • 存档周期性分析内容,以便后续能一致地重新运行
  • 准备同行评审或正式审计
  • 将探索性笔记本转换为参考文档

Process

流程

  1. Confirm audience and scope — determine whether the primary reader is technical (data team), business (stakeholders), or both. For mixed audiences, use a tiered structure. See
    references/audience_depth_guide.md
    for calibration.
  2. Write the business context section — state the business question, the stakeholders who requested the analysis, the decisions it informs, and the success criteria.
  3. Document data sources — for each source, record the table or file, date range, row count, key columns, and any known quality issues or exclusions applied.
  4. Write the methodology section — describe the analytical approach, tools and library versions, key assumptions, and important decisions made (and alternatives considered). Reference the assumptions log if one exists.
  5. Record results — include key metrics and statistics, embed or link visualisations with descriptive captions, and present findings in order of importance.
  6. Write the insights, recommendations, and reproducibility section — connect each finding to a business implication and a next action. Document the steps required to reproduce the analysis (data access, environment, execution order). Use
    assets/analysis_doc_template.md
    as the structure.
  1. 确认受众与范围 —— 确定主要读者是技术人员(数据团队)、业务人员(利益相关方)还是两者兼有。针对混合受众,采用分层结构。可参考
    references/audience_depth_guide.md
    进行校准。
  2. 撰写业务背景部分 —— 说明业务问题、请求分析的利益相关方、分析所支持的决策以及成功标准。
  3. 记录数据源 —— 针对每个数据源,记录对应的表或文件、日期范围、行数、关键列,以及任何已知的质量问题或已应用的排除规则。
  4. 撰写方法论部分 —— 描述分析方法、使用的工具和库版本、关键假设,以及做出的重要决策(和考虑过的替代方案)。如果存在假设日志,请参考该日志。
  5. 记录结果 —— 包含关键指标和统计数据,嵌入或链接带有说明性标题的可视化内容,并按重要性顺序呈现分析结果。
  6. 撰写洞察、建议与可复现性部分 —— 将每个分析结果与业务影响和下一步行动关联起来。记录复现分析所需的步骤(数据访问权限、环境、执行顺序)。使用
    assets/analysis_doc_template.md
    作为文档结构。

Inputs the skill needs

技能所需输入

  • Final code (SQL, Python, notebook) and outputs (charts, tables)
  • Business question and stakeholder context
  • Key findings and recommendations already identified
  • Data source details (tables, date ranges, sample sizes)
  • Library and tool versions used
  • 最终代码(SQL、Python、笔记本)和输出(图表、表格)
  • 业务问题和利益相关方背景
  • 已确定的关键发现与建议
  • 数据源详情(表、日期范围、样本量)
  • 使用的库和工具版本

Output

输出

  • assets/analysis_doc_template.md
    — completed analysis document covering context, data, methodology, results, and reproducibility
  • Linked or embedded visualisations and code references
  • assets/analysis_doc_template.md
    —— 完整的分析文档,涵盖背景、数据、方法论、结果和可复现性
  • 链接或嵌入的可视化内容与代码引用