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:

1. Most important metrics at top-left
2. Summary → Detail flow (top to bottom)
3. Related metrics grouped together
4. 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

### Related Datasets
- 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