BI Fundamentals Skill

Master the core concepts of Business Intelligence including KPI design, metrics frameworks, data literacy, and analytical decision-making.

Quick Start (5 minutes)

1. Understand what makes a good KPI (SMART criteria)
2. Learn the difference between metrics and KPIs
3. Explore common BI frameworks (Balanced Scorecard, OKRs)
4. Apply data-driven decision making

Core Concepts

What is Business Intelligence?

Business Intelligence (BI) transforms raw data into actionable insights for better decision-making.

DATA → INFORMATION → INSIGHT → ACTION → VALUE
  ↑         ↑           ↑         ↑
  |         |           |         └─ Business outcome
  |         |           └─ Understanding "so what?"
  |         └─ Context + meaning
  └─ Raw facts/numbers

KPIs vs Metrics

Aspect	Metric	KPI
Purpose	Measure activity	Measure success
Scope	Operational	Strategic
Target	Optional	Required
Audience	Teams	Leadership
Example	Page views	Conversion rate

SMART KPI Framework

S - Specific    : Clear and unambiguous
M - Measurable  : Quantifiable with data
A - Achievable  : Realistic given resources
R - Relevant    : Aligned with business goals
T - Time-bound  : Has a deadline or frequency

Example Application:

Bad:  "Improve customer satisfaction"
Good: "Increase NPS score from 45 to 55 by Q4 2025"
      S: NPS score (specific metric)
      M: 45 → 55 (quantifiable)
      A: 10-point increase (realistic)
      R: Customer satisfaction (business goal)
      T: By Q4 2025 (deadline)

Code Examples

KPI Definition Template (YAML)

kpi:
  name: "Customer Acquisition Cost (CAC)"
  category: "Growth"
  owner: "Marketing Director"

  formula:
    numerator: "Total Sales & Marketing Spend"
    denominator: "Number of New Customers Acquired"
    calculation: "SUM(marketing_spend + sales_spend) / COUNT(new_customers)"

  target:
    value: 50
    unit: "USD"
    direction: "lower_is_better"
    threshold_warning: 60
    threshold_critical: 75

  measurement:
    frequency: "monthly"
    data_source: "finance_db.marketing_costs, crm.customers"
    lag_days: 5

  context:
    benchmark_industry: 45
    benchmark_company_historical: 55
    related_kpis: ["LTV", "LTV:CAC Ratio", "Payback Period"]

Metrics Framework (Python-style pseudocode)

# Balanced Scorecard Framework
balanced_scorecard = {
    "financial": {
        "objective": "Increase shareholder value",
        "kpis": [
            {"name": "Revenue Growth", "target": "15% YoY"},
            {"name": "Operating Margin", "target": "25%"},
            {"name": "ROI", "target": "18%"}
        ]
    },
    "customer": {
        "objective": "Improve customer satisfaction",
        "kpis": [
            {"name": "NPS", "target": ">50"},
            {"name": "Customer Retention", "target": ">90%"},
            {"name": "Customer Lifetime Value", "target": ">$500"}
        ]
    },
    "internal_process": {
        "objective": "Optimize operations",
        "kpis": [
            {"name": "Cycle Time", "target": "<3 days"},
            {"name": "Defect Rate", "target": "<1%"},
            {"name": "On-Time Delivery", "target": ">98%"}
        ]
    },
    "learning_growth": {
        "objective": "Build organizational capability",
        "kpis": [
            {"name": "Employee Engagement", "target": ">80%"},
            {"name": "Training Hours per Employee", "target": ">40/year"},
            {"name": "Internal Promotion Rate", "target": ">30%"}
        ]
    }
}

OKR Structure

objective: "Become the market leader in customer experience"

key_results:
  - kr: "Increase NPS from 45 to 65"
    progress: 0
    confidence: "medium"

  - kr: "Reduce average response time from 4 hours to 1 hour"
    progress: 0
    confidence: "high"

  - kr: "Achieve 95% first-contact resolution rate"
    progress: 0
    confidence: "low"

initiatives:
  - "Implement AI chatbot for 24/7 support"
  - "Train all support staff on empathy communication"
  - "Create self-service knowledge base"

Best Practices

KPI Design Principles

1. Less is More: 5-7 KPIs per area maximum
2. Balanced View: Include leading and lagging indicators
3. Actionable: Each KPI should drive specific actions
4. Owned: Every KPI has a single accountable owner
5. Reviewed: Regular cadence for KPI review and adjustment

Common Anti-Patterns to Avoid

❌ Vanity Metrics: Metrics that look good but don't drive action
   Example: Total page views (without context)

❌ Metric Overload: Too many KPIs diluting focus
   Example: 50+ KPIs on a dashboard

❌ Lagging Only: All backward-looking, no predictive indicators
   Example: Only measuring revenue, not pipeline

❌ Misaligned Incentives: KPIs that encourage wrong behavior
   Example: Call center measured only on calls/hour

❌ Black Box Metrics: Complex calculations no one understands
   Example: "Engagement Score" with undocumented formula

Data Quality Dimensions

ACCURACY     → Data correctly reflects reality
COMPLETENESS → No missing values or records
TIMELINESS   → Data is current and up-to-date
CONSISTENCY  → Same data across different systems
VALIDITY     → Data conforms to business rules
UNIQUENESS   → No duplicate records

Common Patterns

Industry-Specific KPIs

SaaS / Subscription Business

Growth: MRR, ARR, Net Revenue Retention
Acquisition: CAC, LTV, LTV:CAC Ratio
Engagement: DAU/MAU, Feature Adoption, Time in App
Churn: Logo Churn, Revenue Churn, Expansion Revenue

E-Commerce / Retail

Sales: GMV, AOV, Conversion Rate
Customer: Repeat Purchase Rate, CLV, Cart Abandonment
Operations: Inventory Turnover, Order Fulfillment Time
Marketing: ROAS, Organic vs Paid Traffic %

Manufacturing

Efficiency: OEE, Cycle Time, Throughput
Quality: Defect Rate, First Pass Yield, Scrap Rate
Delivery: On-Time Delivery, Lead Time, Fill Rate
Cost: Cost per Unit, Labor Productivity, Waste %

Metric Hierarchy Template

COMPANY LEVEL (CEO/Board)
├── Revenue Growth (+15% YoY)
├── Profitability (25% EBITDA)
└── Customer Satisfaction (NPS >50)
    │
    ├── DIVISION LEVEL (VP)
    │   ├── Sales Revenue
    │   ├── Marketing Efficiency (CAC)
    │   └── Product Adoption Rate
    │       │
    │       └── TEAM LEVEL (Manager)
    │           ├── Leads Generated
    │           ├── Conversion Rate
    │           ├── Feature Usage
    │           └── Support Tickets
    │               │
    │               └── INDIVIDUAL LEVEL
    │                   ├── Calls Made
    │                   ├── Deals Closed
    │                   └── Tasks Completed

Retry Logic

const executeWithRetry = async (operation: () => Promise<any>) => {
  const retryConfig = {
    maxRetries: 3,
    backoffMs: [1000, 2000, 4000],
    retryableErrors: ['TIMEOUT', 'NETWORK_ERROR', 'RATE_LIMITED']
  };

  for (let attempt = 0; attempt <= retryConfig.maxRetries; attempt++) {
    try {
      return await operation();
    } catch (error) {
      if (attempt === retryConfig.maxRetries) throw error;
      if (!retryConfig.retryableErrors.includes(error.code)) throw error;
      await sleep(retryConfig.backoffMs[attempt]);
    }
  }
};

Logging Hooks

const skillHooks = {
  onSkillStart: (params) => {
    console.log(`[BI-FUNDAMENTALS] Starting: ${params.topic}`);
    metrics.increment('skill.bi_fundamentals.started');
  },

  onSkillComplete: (result) => {
    console.log(`[BI-FUNDAMENTALS] Completed successfully`);
    metrics.increment('skill.bi_fundamentals.completed');
  },

  onSkillError: (error) => {
    console.error(`[BI-FUNDAMENTALS] Error: ${error.message}`);
    metrics.increment('skill.bi_fundamentals.errors');
  }
};

Unit Test Template

describe('BI Fundamentals Skill', () => {
  describe('KPI Design', () => {
    it('should validate SMART criteria', () => {
      const kpi = {
        name: 'Customer Retention Rate',
        target: '90%',
        frequency: 'monthly',
        owner: 'Customer Success Manager'
      };
      expect(validateSMART(kpi)).toBe(true);
    });

    it('should reject vague KPIs', () => {
      const kpi = { name: 'Improve things' };
      expect(validateSMART(kpi)).toBe(false);
    });
  });

  describe('Metrics Framework', () => {
    it('should balance leading and lagging indicators', () => {
      const framework = buildBalancedScorecard(input);
      expect(framework.leadingIndicators.length).toBeGreaterThan(0);
      expect(framework.laggingIndicators.length).toBeGreaterThan(0);
    });
  });
});

Troubleshooting

Common Issues

Issue	Cause	Solution
KPI not moving	Wrong metric selected	Review leading vs lagging
Data not available	Missing data source	Map data requirements first
Stakeholder confusion	Complex formula	Simplify and document
Gaming the metric	Misaligned incentive	Add balancing metrics

Debug Checklist

1. ✓ Is the business objective clearly defined?
2. ✓ Does the KPI formula produce expected results?
3. ✓ Is the data source reliable and timely?
4. ✓ Are targets realistic based on historical data?
5. ✓ Is there an owner accountable for the KPI?

Resources

• Kaplan & Norton: The Balanced Scorecard (foundational text)
• John Doerr: Measure What Matters (OKRs)
• DAMA DMBOK: Data Management Body of Knowledge
• Bernard Marr: KPI Library (industry benchmarks)

Version History

Version	Date	Changes
1.0.0	2024-01	Initial release
2.0.0	2025-01	Production-grade with schemas