business-intelligence

仓库: borghei/claude-skills
安装量: 490
排名: #2153

安装

npx skills add https://github.com/borghei/claude-skills --skill business-intelligence

Business Intelligence

Expert-level business intelligence for data-driven decisions.

Core Competencies Dashboard design Data visualization Reporting automation KPI development Executive reporting Self-service BI Data storytelling Tool administration BI Architecture Data Flow DATA SOURCES → ETL/ELT → DATA WAREHOUSE → SEMANTIC LAYER → DASHBOARDS │ │ │ │ │ ▼ ▼ ▼ ▼ ▼ CRM, ERP Transform Star Schema Metrics Def Tableau/PBI APIs, DBs Clean, Load Fact/Dims Calculations Looker/etc

BI Stack Components PRESENTATION LAYER ├── Executive dashboards ├── Operational reports ├── Self-service exploration └── Embedded analytics

SEMANTIC LAYER ├── Business metrics definitions ├── Calculated fields ├── Hierarchies └── Row-level security

DATA LAYER ├── Data warehouse (Snowflake/BigQuery/Redshift) ├── Data marts ├── Materialized views └── Cached datasets

Dashboard Design Dashboard Types

Executive Dashboard:

┌─────────────────────────────────────────────────────────────┐ │ EXECUTIVE SUMMARY │ ├─────────────────────────────────────────────────────────────┤ │ Revenue Pipeline Customers NPS │ │ $12.4M $45.2M 2,847 72 │ │ +15% YoY +22% QoQ +340 MTD +5 pts │ ├─────────────────────────────────────────────────────────────┤ │ REVENUE TREND │ REVENUE BY SEGMENT │ │ [Line chart: 12 months] │ [Pie chart: segments] │ ├────────────────────────────────┼─────────────────────────────┤ │ TOP ACCOUNTS │ KEY METRICS STATUS │ │ [Table: top 10] │ [KPI cards with RAG] │ └─────────────────────────────────────────────────────────────┘

Operational Dashboard:

┌─────────────────────────────────────────────────────────────┐ │ DAILY OPERATIONS │ ├─────────────────────────────────────────────────────────────┤ │ Orders Today Tickets Open Avg Response SLA Met │ │ 1,247 89 12 min 98.5% │ │ vs Avg: +8% vs Avg: -12% vs Target: ✓ vs Target: ✓ │ ├─────────────────────────────────────────────────────────────┤ │ HOURLY VOLUME │ QUEUE STATUS │ │ [Area chart: 24h] │ [Stacked bar by team] │ ├────────────────────────────────┼─────────────────────────────┤ │ ALERTS │ TEAM PERFORMANCE │ │ [Alert list with severity] │ [Table: agents + metrics] │ └─────────────────────────────────────────────────────────────┘

Design Principles

Visual Hierarchy:

Most important metrics at top-left Summary → Detail flow (top to bottom) Related metrics grouped together White space for readability

Color Usage:

STATUS COLORS ├── Green (#28A745): Good/On Track ├── Yellow (#FFC107): Warning/At Risk ├── Red (#DC3545): Critical/Off Track └── Gray (#6C757D): Neutral/No Status

BRAND COLORS ├── Primary: Use for emphasis ├── Secondary: Supporting elements └── Accent: Highlights only

DATA COLORS ├── Sequential: Light → Dark for ranges ├── Diverging: Different hues for pos/neg └── Categorical: Distinct colors per category

Chart Selection:

Data Type Best Charts Trend over time Line, Area Part of whole Pie, Donut, Treemap Comparison Bar, Column Distribution Histogram, Box Plot Relationship Scatter, Bubble Geographic Map, Choropleth KPI Framework KPI Development

KPI Definition: [Metric Name]

Business Context

  • Owner: [Department/Role]
  • Purpose: [Why this metric matters]
  • Strategic alignment: [Goal it supports]

Definition

  • Formula: [Calculation]
  • Data source: [System/Table]
  • Granularity: [Daily/Weekly/Monthly]

Targets

  • Target: [Value]
  • Threshold (Yellow): [Value]
  • Critical (Red): [Value]

Dimensions

  • Time: [Day/Week/Month/Quarter/Year]
  • Segments: [By region, product, etc.]

Caveats

  • [Known limitations]
  • [Data quality issues]

Metric Categories

Financial:

Metric Formula Frequency Revenue Sum of closed won Daily MRR Monthly recurring Monthly Gross Margin (Rev - COGS) / Rev Monthly CAC S&M Spend / New Customers Monthly LTV ARPU × Margin × Lifetime Quarterly

Customer:

Metric Formula Frequency Active Users DAU, WAU, MAU Daily Churn Rate Lost / Total Monthly NPS Promoters - Detractors Quarterly CSAT Satisfied / Responses Weekly

Operations:

Metric Formula Frequency Throughput Units / Time Hourly Error Rate Errors / Total Daily Cycle Time End - Start Daily Utilization Active / Capacity Daily Report Automation Report Types

Scheduled Reports:

report: name: Weekly Sales Report schedule: "0 8 * * MON" # Every Monday 8am recipients: - sales-team@company.com - leadership@company.com format: PDF pages: - Executive Summary - Pipeline Analysis - Rep Performance - Forecast

Threshold Alerts:

alert: name: Revenue Below Target metric: daily_revenue condition: actual < target * 0.9 frequency: daily channels: - email: finance@company.com - slack: #revenue-alerts message: | Daily revenue of ${actual} is ${pct_diff}% below target. Top contributing factors: ${top_factors}

Automation Patterns def generate_report(report_config): """ Automated report generation workflow """ # 1. Refresh data refresh_data_sources(report_config['sources'])

# 2. Calculate metrics
metrics = calculate_metrics(report_config['metrics'])

# 3. Generate visualizations
charts = create_visualizations(metrics, report_config['charts'])

# 4. Build report
report = compile_report(
    metrics=metrics,
    charts=charts,
    template=report_config['template']
)

# 5. Distribute
distribute_report(
    report=report,
    recipients=report_config['recipients'],
    format=report_config['format']
)

return report

Self-Service BI Enablement Framework SELF-SERVICE MATURITY MODEL

Level 1: Report Consumers ├── View existing dashboards ├── Apply filters └── Export data

Level 2: Data Explorers ├── Ad-hoc queries ├── Create simple charts └── Share findings

Level 3: Report Builders ├── Design dashboards ├── Combine data sources └── Create calculated fields

Level 4: Data Modelers ├── Create data models ├── Define metrics └── Optimize performance

Data Catalog

Data Catalog Entry

Dataset: sales_opportunities

Description

Contains all sales opportunities from CRM

Schema

| Column | Type | Description |

|--------|------|-------------|

| opp_id | STRING | Unique identifier |

| account_id | STRING | Related account |

| amount | DECIMAL | Deal value |

| stage | STRING | Pipeline stage |

| close_date | DATE | Expected close |

| owner_id | STRING | Sales rep |

Refresh

  • Frequency: Every 4 hours
  • Source: Salesforce API
  • Last refresh: 2024-01-15 08:00 UTC

Usage Notes

  • Filter by is_deleted = false
  • Amount is always in USD
  • Stage values: Prospect, Discovery, Demo, Proposal, Negotiation, Closed Won, Closed Lost
  • accounts
  • sales_reps
  • products

Data Storytelling Narrative Structure SITUATION → COMPLICATION → RESOLUTION

  1. SITUATION (Context) "Last quarter, we set a goal to increase customer retention by 10%"

  2. COMPLICATION (Problem/Opportunity) "However, churn increased by 5% in our enterprise segment"

  3. RESOLUTION (Insight + Action) "Analysis shows onboarding time correlates with churn. Reducing onboarding from 30 to 14 days could save $2M annually"

Insight Framework

Insight: [Title]

What happened?

[Describe the observation in data]

Why does it matter?

[Business impact and context]

Why did it happen?

[Root cause analysis]

What should we do?

[Recommended actions]

Supporting Data

[Charts and metrics]

Presentation Template EXECUTIVE PRESENTATION STRUCTURE

  1. Headlines First (2-3 key takeaways)
  2. Context (why we're looking at this)
  3. Key Findings (data + insights)
  4. Implications (what it means)
  5. Recommendations (what to do)
  6. Appendix (detailed data)

Tool Administration Performance Optimization

Dashboard Performance:

OPTIMIZATION CHECKLIST □ Limit visualizations per page (5-8 max) □ Use data extracts vs live connections □ Minimize calculated fields in viz □ Use context filters effectively □ Aggregate data at source when possible □ Schedule refreshes during off-peak □ Monitor query execution times

Query Optimization:

-- Bad: Full table scan SELECT * FROM large_table WHERE date >= '2024-01-01';

-- Good: Partitioned and filtered SELECT required_columns FROM large_table WHERE partition_date >= '2024-01-01' AND status = 'active' LIMIT 10000;

Governance

Access Control:

security_model: row_level_security: - rule: region_access filter: "region = user.region" - rule: team_access filter: "team_id IN user.teams"

object_permissions: - role: viewer permissions: [view, export] - role: editor permissions: [view, export, edit] - role: admin permissions: [view, export, edit, delete, publish]

Data Quality Monitoring:

DATA QUALITY CHECKS ├── Freshness: Is data current? ├── Completeness: Are all records present? ├── Accuracy: Do values make sense? ├── Consistency: Do related metrics align? └── Uniqueness: Are there duplicates?

Reference Materials references/dashboard_patterns.md - Dashboard design patterns references/visualization_guide.md - Chart selection guide references/kpi_library.md - Standard KPI definitions references/storytelling.md - Data storytelling techniques Scripts

Dashboard performance analyzer

python scripts/dashboard_analyzer.py --dashboard "Sales Overview"

KPI calculator

python scripts/kpi_calculator.py --config metrics.yaml --output report.json

Report generator

python scripts/report_generator.py --template weekly_sales --format pdf

Data quality checker

python scripts/data_quality.py --dataset sales_opportunities --checks all

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