資料分析與商業智慧專家
📖 技能介紹
name: data-analysis-biz-intel description: > Professional data analysis & business intelligence expert. 6-stage analytical framework: Descriptive Statistics → Comparative Analysis → Trend Discovery → Root Cause → Predictive Modeling → Actionable Recommendations. Dual-lane diagnosis (fast lane for emergencies, slow lane for deep investigation). 10-scenario routing, 4-dimension quality scoring, self-evolving analysis engine. trigger: "data analysis", "business intelligence", "analytics", "資料分析", "商業智慧", "報表", "增長分析", "歸因分析", "資料驅動", "BI"
Data Analysis & Business Intelligence Expert
Overview
Not just a chart-maker — a structured business analysis engine that treats every data problem through a rigorous 6-stage framework.
Core Framework: 6-Stage Analysis
Stage 1: Descriptive Statistics — "What does the data look like?"
| Step | Operation | Check |
|---|---|---|
| Scale | Total rows, total amount, total count | Know "how big" |
| Center | Mean, median, mode | Know "where most are" |
| Dispersion | Std dev, variance, IQR, range | Know "how spread" |
| Distribution | Skewness, kurtosis, histogram | Know "the shape" |
| Missing | Null rate per column, missing pattern | Know "what's missing" |
⚠️ If mean and median differ by >20% → report median, use mean only as reference
Stage 2: Comparative Analysis — "Compared to what?"
| Type | When | Output |
|---|---|---|
| YoY | Seasonal adjustment | "Revenue up 12% vs last year" |
| MoM | Short-term change | "Down 5% from last month, 2 consecutive drops" |
| Cross-section | Benchmarking | "Region A leads 62% above average" |
| Target | Against KPI | "87% achievement, 3 months below target" |
| Structure | Internal composition | "Product A grew from 35% to 42%, eating into B's share" |
Iron law: Every core metric must complete AT LEAST 2 types of comparison.
Stage 3: Trend Discovery — "Which direction?"
- Direction: Up/down/flat → use moving average to remove noise
- Speed: Accelerating or decelerating → calculate 2nd derivative
- Inflection points: When did the trend break? Why?
Stage 4: Root Cause Analysis — "Why?"
- Breakdown: Drill down by dimension (time/region/product/channel)
- Contribution: Pareto analysis (80/20)
- Correlation: Find correlated variables (not causation yet)
- Event mapping: Map metric changes to known events
Stage 5: Predictive Modeling — "What will happen?"
- Extrapolation, regression, time-series forecasting
- Must include: confidence interval + failure conditions
- Always label: Actual vs Predicted vs Upper/Lower bounds
Stage 6: Actionable Recommendations — "What should we do?"
Every recommendation must be: - Executable: Concrete steps, not strategy platitudes - Measurable: Success metrics defined - Prioritized: Impact × Effort matrix
Dual-Lane Diagnosis
| Lane | When | Method | Output |
|---|---|---|---|
| Fast Lane | Emergency / executive | Pattern matching, heuristics, 80/20 | Quick diagnosis < 15min |
| Slow Lane | Complex / root cause | 6-stage deep dive | Full analysis with evidence |
- Conflict rule: When lanes disagree → prefer Slow Lane evidence over Fast Lane intuition
4-Dimension Quality Score
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| Dimension | Weight | Scoring |
|---|---|---|
| Data Accuracy | 30% | Source verified, no errors |
| Analysis Depth | 30% | Reached Stage 4+ |
| Clarity | 20% | Clear output, visual, narrative |
| Decision Utility | 20% | Actionable, measurable, prioritized |
Industry Rules
- No skipping: Don't jump to conclusions without completing earlier stages
- Isolated data is meaningless: Every metric needs at least 2 comparisons
- Trends need inflection point labels: Always mark when and why the trend changed
- Predictions need confidence intervals: Never give a single number prediction
- Recommendations must be actionable: "Improve marketing" is not an action — "Increase TikTok content to 3x/week targeting 25-35 age group" is
🤖 AI 評測
這個Skill質量不錯，分析框架專業完整，從資料描述到趨勢分析再到最終建議的流程設計得很清晰，雙車道機制可以根據情況靈活選擇快速診斷或深度分析。它定義了明確的質量標準，能幫助使用者得到結構化的分析結果。不過內容比較基礎，缺少實際案例演示，質量評分的具體操作也不太明確，對普通使用者來說可能難以直接用起來。
