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Data Analytics Business Intelligence

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chendongqi
data-analytics-business-intelligence

商业智能助手 - 专业的BI系统设计与业务决策支持专家。适用场景: (1) BI报表与仪表板设计 (2) KPI指标体系构建与监控 (3) 业务数据可视化方案 (4) 管理驾驶舱设计 (5) 自助分析平台规划 (6) BI工具选型与实施 (7) 数据驱动决策支持 触发关键词:商业智能、BI报表、仪表板、Dashboard、KPI、数据可视化、管理驾驶舱、自助分析、决策支持、数据看板、经营分析

Overview

Publisherchendongqi
RepositoryOPB-Skills
Skill namedata-analytics-business-intelligence
Stars
133
Forks
25
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by chendongqi on GitHub. Read the source before you install it.

Installation

Install the Data Analytics Business Intelligence AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chendongqi/OPB-Skills/tree/main/skills/data-analytics-business-intelligence
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chendongqi/OPB-Skills.git /tmp/OPB-Skills
mkdir -p .claude/skills
cp -r /tmp/OPB-Skills/skills/data-analytics-business-intelligence .claude/skills/data-analytics-business-intelligence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Analytics Business Intelligence in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Analytics Business Intelligence on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Analytics Business Intelligence is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

商业智能助手

核心工作流程

1. BI系统规划

BI系统架构:

数据层
├── 数据源:业务系统/ERP/CRM/日志
├── 数据仓库:ODS/DWD/DWS/ADS
├── 数据集市:主题数据集
└── 元数据管理:数据字典/血缘

分析层
├── OLAP引擎:多维分析
├── 报表引擎:定时报表
├── 即席查询:自助分析
└── 数据挖掘:高级分析

展现层
├── 管理驾驶舱:高层决策
├── 业务看板:部门运营
├── 自助分析:灵活探索
└── 移动端:随时随地

BI建设路径:

阶段目标核心能力典型产出
报表阶段看得到固定报表日报/周报/月报
分析阶段看得清多维分析业务看板
洞察阶段看得懂智能分析预警/预测
决策阶段做得对决策支持智能推荐

2. 仪表板设计

Dashboard设计原则:

信息架构
├── 核心指标:3-5个最重要指标
├── 趋势变化:同比/环比/目标达成
├── 维度下钻:支持多级下钻
└── 异常预警:自动标记异常

视觉设计
├── 布局:Z字型/F字型阅读
├── 颜色:统一配色、语义一致
├── 图表:选择合适的图表类型
└── 留白:避免信息过载

交互设计
├── 筛选器:全局/局部筛选
├── 联动:图表间联动
├── 下钻:层层深入
└── 导出:支持数据导出

Dashboard类型与设计要点:

类型受众设计要点更新频率
战略看板高管简洁、聚焦KPI、趋势日/周
运营看板中层详细、可下钻、预警实时/日
分析看板分析师灵活、多维、可探索按需
监控看板运维实时、阈值、告警实时

3. KPI指标体系

KPI体系构建方法:

战略分解法(BSC)
├── 财务维度:收入/利润/成本
├── 客户维度:满意度/留存/NPS
├── 流程维度:效率/质量/周期
└── 学习维度:能力/创新/文化

价值链分析法
├── 获客环节:流量/转化/CAC
├── 服务环节:体验/满意度/投诉
├── 复购环节:留存/复购/LTV
└── 传播环节:分享/推荐/NPS

杜邦分析法(财务)
├── ROE = 净利率 × 周转率 × 杠杆
├── 净利率 = 净利润 / 收入
├── 周转率 = 收入 / 总资产
└── 杠杆 = 总资产 / 净资产

KPI设计SMART原则:

原则含义示例
Specific具体明确"月活用户数"而非"用户活跃"
Measurable可衡量有明确的计算公式
Achievable可达成基于历史设定合理目标
Relevant相关性与业务目标紧密关联
Time-bound时限性明确统计周期

4. 数据可视化

图表选择矩阵:

比较类
├── 柱状图:类别对比
├── 条形图:排名展示
├── 雷达图:多维对比
└── 子弹图:目标达成

趋势类
├── 折线图:时间趋势
├── 面积图:累计趋势
├── 瀑布图:增减变化
└── 斜率图:前后对比

构成类
├── 饼图:占比(<7项)
├── 环形图:占比+中心信息
├── 树图:层级占比
└── 桑基图:流向构成

关系类
├── 散点图:相关性
├── 气泡图:三维关系
├── 网络图:关联关系
└── 热力图:矩阵关系

可视化最佳实践:

场景推荐图表注意事项
KPI卡片数字+趋势箭头突出变化幅度
时间趋势折线图时间轴连续
排名对比条形图排序展示
目标达成仪表盘/子弹图显示目标线
占比分析饼图/环形图不超过7项
地理分布地图配合热力

5. 管理驾驶舱

驾驶舱设计框架:

CEO驾驶舱
├── 核心指标:收入/利润/现金流
├── 业务概览:各业务线表现
├── 预警信息:异常指标提醒
└── 趋势预测:关键指标预测

销售驾驶舱
├── 业绩达成:目标/实际/缺口
├── 漏斗分析:商机/转化/成交
├── 团队排名:销售人员业绩
└── 客户分析:客户分布/贡献

运营驾驶舱
├── 流量监控:UV/PV/来源
├── 转化分析:注册/激活/付费
├── 用户分析:活跃/留存/流失
└── 内容分析:曝光/点击/互动

驾驶舱布局模板:

+------------------------------------------+
|  [Logo]  [标题]  [时间筛选]  [刷新时间]   |
+------------------------------------------+
| +--------+ +--------+ +--------+ +------+ |
| | KPI 1  | | KPI 2  | | KPI 3  | | KPI 4| |
| +--------+ +--------+ +--------+ +------+ |
+------------------------------------------+
| +------------------+ +------------------+ |
| |                  | |                  | |
| |   趋势图表       | |   构成/排名      | |
| |                  | |                  | |
| +------------------+ +------------------+ |
+------------------------------------------+
| +--------------------------------------+ |
| |                                      | |
| |          详细数据表格                | |
| |                                      | |
| +--------------------------------------+ |
+------------------------------------------+

6. BI工具选型

主流BI工具对比:

工具定位优势适用场景
Tableau企业级可视化强、交互好大中型企业
Power BI微软生态Office集成、性价比微软体系企业
Metabase开源简单易用、免费初创/小团队
Superset开源功能全面、可定制技术团队
FineBI国产本土化、服务好国内企业
QuickBI阿里云云原生、AI能力阿里云用户

选型考虑因素:

业务需求
├── 用户规模:并发用户数
├── 数据量级:数据规模
├── 分析复杂度:自助/固定
└── 移动需求:移动端支持

技术要求
├── 数据源支持:连接能力
├── 部署方式:云/本地
├── 安全要求:权限/加密
└── 扩展能力:API/二次开发

成本预算
├── 许可费用:按用户/数据量
├── 实施成本:部署/培训
├── 运维成本:维护/升级
└── 隐性成本:学习/迁移

输出模板

BI需求分析文档

【项目名称】___________
【业务背景】___________
【目标用户】___________

【核心需求】
| 需求类型 | 需求描述 | 优先级 |
|----------|----------|--------|

【KPI指标清单】
| 指标名称 | 计算公式 | 数据来源 | 更新频率 |
|----------|----------|----------|----------|

【Dashboard规划】
| 看板名称 | 目标用户 | 核心指标 | 展示方式 |
|----------|----------|----------|----------|

【数据源清单】
| 系统名称 | 数据内容 | 接入方式 |
|----------|----------|----------|

【实施计划】
- 阶段1:
- 阶段2:
- 阶段3:

Dashboard设计规范

【看板名称】___________
【目标用户】___________
【更新频率】___________

【布局设计】
[布局草图]

【指标定义】
| 指标 | 定义 | 公式 | 数据源 |
|------|------|------|--------|

【图表设计】
| 区域 | 图表类型 | 数据维度 | 交互 |
|------|----------|----------|------|

【筛选器】
| 筛选项 | 类型 | 默认值 | 联动 |
|--------|------|--------|------|

【预警规则】
| 指标 | 预警条件 | 提醒方式 |
|------|----------|----------|

最佳实践

  • 用户导向:从用户需求出发,而非技术可行性
  • 简洁优先:少即是多,聚焦关键指标
  • 数据质量:垃圾进垃圾出,确保数据准确
  • 迭代优化:持续收集反馈,不断改进
  • 培训赋能:让用户真正用起来才有价值

Frequently asked questions

What does the Data Analytics Business Intelligence AI skill do?

商业智能助手 - 专业的BI系统设计与业务决策支持专家。适用场景: (1) BI报表与仪表板设计 (2) KPI指标体系构建与监控 (3) 业务数据可视化方案 (4) 管理驾驶舱设计 (5) 自助分析平台规划 (6) BI工具选型与实施 (7) 数据驱动决策支持 触发关键词:商业智能、BI报表、仪表板、Dashboard、KPI、数据可视化、管理驾驶舱、自助分析、决策支持、数据看板、经营分析

Why use Data Analytics Business Intelligence on TypingMind?

Because you install it once and use it with any model. Data Analytics Business Intelligence is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Analytics Business Intelligence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chendongqi/OPB-Skills/tree/main/skills/data-analytics-business-intelligence. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Analytics Business Intelligence?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Analytics Business Intelligence?

As many as you like. As long as a model supports skills, you can use Data Analytics Business Intelligence with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Analytics Business Intelligence AI skill free?

It is published on GitHub by chendongqi. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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