BI Governance

Learn how to establish trust in your analytics through proper governance, ensuring data quality, security, and consistency while balancing innovation and control.

BI Governance Framework

BI Governance Framework

Core components of an effective BI governance program

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Components

Planning

People

Data

Security

Process

Connection Types

Process Flow

Defines

Assigns

Manages

Governance Operating Model

🏛️

BI Governance Models

Technical

The governance model directly impacts business value delivery:

• Centralized models improve consistency but may create bottlenecks
• Federated models increase responsiveness but risk inconsistency
• Hybrid models balance control and flexibility for most organizations
• Self-service models accelerate insights but require strong guardrails
• Project models align analytics with specific business outcomes

🏛️

BI Governance Models

Non-Technical

Technical implementation of governance models:

• Centralized model with standardized processes and tools
• Federated model with distributed teams and shared standards
• Hybrid model with central CoE and distributed power users
• Self-service model with guardrails and certification
• Project-based model for specific business initiatives

BI Governance Team Structure

Center of Excellence (CoE) Approach

Role	Primary Responsibilities
BI Governance Lead	Overall strategy, standards, and program management
Data Stewards	Subject matter expertise, metadata management, quality assurance
BI Platform Administrators	Security configuration, permissions, platform management
BI Developers	Enterprise content creation, data model development
Data Quality Specialists	Data profiling, quality monitoring, remediation
Training Specialists	User education, documentation, knowledge base

Federated Governance Approach

Team	Responsibilities
Central Governance Team	Standards, core infrastructure, centralized templates
Line of Business Teams	Department-specific content, local data expertise
Technical Services	Platform support, integrations, security coordination
Cross-Functional Committee	Priority setting, resource allocation, conflict resolution

Policies and Standards

📜

Governance Policies

Technical

Effective governance policies deliver tangible business benefits:

• Ensure consistency and reliability across business analytics
• Reduce duplication of effort and conflicting reports
• Establish clear expectations for analytics quality
• Balance innovation with controls for regulatory compliance
• Create sustainable analytics practices that scale with the business

📜

Governance Policies

Non-Technical

Technical implementation of governance policies:

• Naming conventions enforced through validation rules
• Content certification workflows and approval gates
• Change management procedures with version control
• Development, testing, and production environments
• Automated policy enforcement through platform tools

Sample BI Governance Policies Document

# Business Intelligence Governance Policies

## 1. Content Development & Management

### 1.1 Naming Conventions
- All dashboards must follow pattern: [Department]-[Purpose]-[Frequency] (e.g., "Sales-Pipeline-Weekly")
- Reports must include owner and last update information
- Standardized prefixes for certified (C_) vs. development (DEV_) content

### 1.2 Content Certification Process
- Development content must be reviewed by subject matter expert
- Data accuracy must be validated against source systems
- Business relevance must be confirmed by business owner
- Documentation must be complete before certification
- Re-certification required quarterly or after major data changes

### 1.3 Change Management
- Major changes require documented business requirements
- Testing mandatory in development environment
- User acceptance testing with documented results
- Scheduled deployment windows for production changes
- Post-implementation review for significant changes

## 2. Data Standards

### 2.1 Metric Definitions
- Enterprise metrics must use approved calculation methodology
- Local metrics must be clearly identified as non-standard
- All calculations must be documented in central repository
- Standard time periods and fiscal calendars must be used
- Currency conversions must use approved exchange rates

### 2.2 Data Quality
- Critical reports must include data quality indicators
- Data lineage must be documented for compliance-related reporting
- Known data limitations must be documented
- Automated data quality checks for critical metrics
- Data refresh schedules must be documented and monitored

## 3. Security & Access

### 3.1 Access Controls
- Access based on role and data sensitivity classification
- Quarterly access reviews required
- PII/sensitive data requires additional approval
- External sharing requires business owner authorization
- Administrator access limited to governance team

### 3.2 Data Protection
- Sensitive data must be identified and classified
- Export controls enforced for confidential information
- Embedding in external applications requires security review
- Mobile access requires appropriate security controls
- Audit logging enabled for sensitive content access

Metadata Management

🏷️

Metadata Strategy

Technical

Effective metadata management delivers substantial business value:

• Creates a shared business vocabulary across the organization
• Improves discovery and reuse of analytics assets
• Enables impact analysis for proposed changes
• Builds trust through transparency of data sources and transformations
• Accelerates onboarding of new analytics users and developers

🏷️

Metadata Strategy

Non-Technical

Technical implementation of metadata management:

• Business glossary integration with BI semantic layer
• Metadata repository implementation with APIs
• Automated lineage capture and documentation
• Technical and business metadata association
• Metadata-driven automation for documentation

Metadata Management Architecture

Key components of an enterprise metadata management system for BI

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Components

Data

Processing

Model

Metadata

Visualization

Connection Types

Process Flow

Extracts from

Loads to

Modeled in

Security and Compliance

🔒

BI Security Model

Technical

A robust BI security model delivers key business protections:

• Ensures sensitive information is only visible to authorized users
• Maintains compliance with industry regulations and privacy laws
• Reduces risk of data leakage and unauthorized access
• Enables safe data sharing with partners and customers
• Supports data democratization while maintaining proper controls

🔒

BI Security Model

Non-Technical

Technical implementation of BI security:

• Row-level security with dynamic user-based filters
• Column-level security for sensitive attributes
• Object-level permissions for content access control
• Integration with enterprise authentication systems
• Audit logging and monitoring for security events

Row-Level Security Implementation Example

-- 1. Create security tables
CREATE TABLE user_roles (
  user_id VARCHAR(50),
  role_id VARCHAR(50),
  PRIMARY KEY (user_id, role_id)
);

CREATE TABLE role_permissions (
  role_id VARCHAR(50),
  region_id VARCHAR(50),
  department_id VARCHAR(50),
  PRIMARY KEY (role_id, region_id, department_id)
);

-- 2. Create security view that filters data based on user context
CREATE OR REPLACE VIEW sales_secure AS
SELECT s.*
FROM sales s
JOIN role_permissions rp ON 
  s.region_id = rp.region_id AND
  s.department_id = rp.department_id
JOIN user_roles ur ON
  rp.role_id = ur.role_id
WHERE 
  ur.user_id = CURRENT_USER();

-- 3. Tableau/Power BI implementation: Use custom SQL with user functions
-- Tableau example:
-- SELECT * FROM sales s
-- WHERE s.region_id IN (
--   SELECT rp.region_id 
--   FROM role_permissions rp
--   JOIN user_roles ur ON rp.role_id = ur.role_id
--   WHERE ur.user_id = USERNAME()
-- )

-- 4. Alternative: Create database users mapped to application users
-- and create appropriate grants
GRANT SELECT ON sales_secure TO ROLE analyst_role;

Platform-Specific Security Features

Tableau Security

• Site roles and user permissions
• Projects for content organization
• Data source certification
• Row-level security via user filters
• OAuth and SAML integration

Power BI Security

• Workspace roles and app permissions
• Row-level security (RLS) with DAX filters
• Object-level security (OLS) for column restrictions
• Sensitivity labels for data classification
• Integration with Microsoft Information Protection

Looker Security

• Model-based access controls
• Content access controls with folders
• Data-level security with access grants
• User attributes for parameterized filtering
• Field-level permissions in LookML

Content Lifecycle Management

BI Content Lifecycle

Stages in the lifecycle of BI content from creation through retirement

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Components

Planning

Development

Governance

Operations

Connection Types

Process Flow

Informs

Guides

Undergoes

🔄

Version Control & Change Management

Technical

Effective version control delivers business stability and agility:

• Prevents disruption to business operations during updates
• Creates audit trail of changes for compliance requirements
• Enables rollback capability when issues arise
• Facilitates collaboration among development teams
• Accelerates development through workflow automation

🔄

Version Control & Change Management

Non-Technical

Technical implementation of version control:

• Git integration for BI content versioning
• CI/CD pipelines for automated deployment
• Staging and production environments
• Version tagging and release notes
• Automated testing for content validation

BI CI/CD Pipeline Example

# Azure DevOps Pipeline for Power BI Deployment
trigger:
  branches:
    include:
    - main
    - release/*

pool:
  vmImage: 'windows-latest'

stages:
- stage: Build
  jobs:
  - job: BuildAndTest
    steps:
    - task: PowerShell@2
      inputs:
        targetType: 'inline'
        script: |
          # Install required modules
          Install-Module -Name MicrosoftPowerBIMgmt -Force -Scope CurrentUser
          Install-Module -Name ALMToolkit -Force -Scope CurrentUser
          
          # Connect to Power BI Service
          $password = ConvertTo-SecureString "$(PowerBIPassword)" -AsPlainText -Force
          $credential = New-Object System.Management.Automation.PSCredential("$(PowerBIUser)", $password)
          Connect-PowerBIServiceAccount -Credential $credential
          
          # Run validation on PBIX files
          Get-ChildItem -Path "$(System.DefaultWorkingDirectory)" -Filter "*.pbix" -Recurse | ForEach-Object {
            Write-Host "Validating $($_.FullName)"
            Test-PowerBIReport -Path $_.FullName -OutFile "$($_.BaseName)_validation.json"
            
            # Check for major issues
            $validationResult = Get-Content "$($_.BaseName)_validation.json" | ConvertFrom-Json
            if ($validationResult.issueCount -gt 0) {
              Write-Warning "Issues found in $($_.Name): $($validationResult.issueCount)"
              $validationResult.issues | ForEach-Object {
                Write-Warning "- $($_.category): $($_.description)"
              }
            }
          }
      displayName: 'Validate Power BI Reports'

- stage: DeployToTest
  dependsOn: Build
  condition: succeeded()
  jobs:
  - job: DeployToTest
    steps:
    - task: PowerShell@2
      inputs:
        targetType: 'inline'
        script: |
          # Connect to Power BI Service
          $password = ConvertTo-SecureString "$(PowerBIPassword)" -AsPlainText -Force
          $credential = New-Object System.Management.Automation.PSCredential("$(PowerBIUser)", $password)
          Connect-PowerBIServiceAccount -Credential $credential
          
          # Deploy to test workspace
          Get-ChildItem -Path "$(System.DefaultWorkingDirectory)" -Filter "*.pbix" -Recurse | ForEach-Object {
            Write-Host "Deploying $($_.Name) to test workspace"
            New-PowerBIReport -Path $_.FullName -WorkspaceId "$(TestWorkspaceId)" -ConflictAction CreateOrOverwrite
          }
      displayName: 'Deploy to Test Workspace'

- stage: UAT
  dependsOn: DeployToTest
  jobs:
  - job: UserAcceptanceTesting
    steps:
    - task: ManualValidation@0
      timeoutInMinutes: 4320 # 3 days
      inputs:
        notifyUsers: '$(UATApprovers)'
        instructions: 'Please validate the deployed reports in the test workspace.'
      displayName: 'UAT Approval'

- stage: DeployToProduction
  dependsOn: UAT
  condition: succeeded()
  jobs:
  - job: DeployToProduction
    steps:
    - task: PowerShell@2
      inputs:
        targetType: 'inline'
        script: |
          # Connect to Power BI Service
          $password = ConvertTo-SecureString "$(PowerBIPassword)" -AsPlainText -Force
          $credential = New-Object System.Management.Automation.PSCredential("$(PowerBIUser)", $password)
          Connect-PowerBIServiceAccount -Credential $credential
          
          # Deploy to production workspace
          Get-ChildItem -Path "$(System.DefaultWorkingDirectory)" -Filter "*.pbix" -Recurse | ForEach-Object {
            Write-Host "Deploying $($_.Name) to production workspace"
            New-PowerBIReport -Path $_.FullName -WorkspaceId "$(ProductionWorkspaceId)" -ConflictAction CreateOrOverwrite
          }
          
          # Update app if needed
          Update-PowerBIApp -AppId "$(AppId)" -WorkspaceId "$(ProductionWorkspaceId)"
      displayName: 'Deploy to Production Workspace'

Data Quality Management

✅

Data Quality Framework

Technical

Data quality management directly impacts business decision-making:

• Builds trust in analytics and increases adoption
• Prevents costly decisions based on inaccurate information
• Reduces time spent reconciling conflicting data sources
• Creates accountability for data quality throughout the organization
• Improves customer experience through accurate reporting

✅

Data Quality Framework

Non-Technical

Technical implementation of data quality management:

• Data profiling and validation rules implementation
• Automated quality checks in ETL processes
• Exception handling and remediation workflows
• Quality metrics and KPI dashboards
• Data quality dimensions: completeness, accuracy, consistency, timeliness

Data Quality Checks Example

-- Create a data quality monitoring table
CREATE TABLE data_quality_results (
  check_id INT,
  check_name VARCHAR(100),
  table_name VARCHAR(100),
  column_name VARCHAR(100),
  check_date TIMESTAMP,
  records_checked INT,
  records_failed INT,
  failure_percentage DECIMAL(5,2),
  severity VARCHAR(20),
  status VARCHAR(20),
  details TEXT
);

-- Example data quality check procedures

-- 1. Check for null values in required fields
CREATE PROCEDURE check_null_values(table_name VARCHAR, column_name VARCHAR, severity VARCHAR)
LANGUAGE plpgsql
AS $$
DECLARE
  total_records INT;
  null_records INT;
  failure_rate DECIMAL(5,2);
  check_status VARCHAR(20);
  check_id INT;
BEGIN
  -- Generate a unique check ID
  SELECT COALESCE(MAX(check_id), 0) + 1 INTO check_id FROM data_quality_results;
  
  -- Count total records
  EXECUTE format('SELECT COUNT(*) FROM %I', table_name) INTO total_records;
  
  -- Count null records
  EXECUTE format('SELECT COUNT(*) FROM %I WHERE %I IS NULL', table_name, column_name) 
    INTO null_records;
  
  -- Calculate failure rate
  IF total_records > 0 THEN
    failure_rate := (null_records::DECIMAL / total_records::DECIMAL) * 100;
  ELSE
    failure_rate := 0;
  END IF;
  
  -- Determine status
  IF null_records > 0 THEN
    IF severity = 'CRITICAL' THEN
      check_status := 'FAILED';
    ELSIF failure_rate > 5 THEN
      check_status := 'WARNING';
    ELSE
      check_status := 'PASSED';
    END IF;
  ELSE
    check_status := 'PASSED';
  END IF;
  
  -- Record results
  INSERT INTO data_quality_results (
    check_id, check_name, table_name, column_name, check_date,
    records_checked, records_failed, failure_percentage, severity, status, details
  ) VALUES (
    check_id,
    'NULL_CHECK',
    table_name,
    column_name,
    CURRENT_TIMESTAMP,
    total_records,
    null_records,
    failure_rate,
    severity,
    check_status,
    format('Found %s null values out of %s records', null_records, total_records)
  );
  
  -- Raise exception for critical failures
  IF severity = 'CRITICAL' AND null_records > 0 THEN
    RAISE EXCEPTION 'Critical data quality check failed: % null values found in %.%', 
      null_records, table_name, column_name;
  END IF;
END;
$$;

-- Example usage:
-- CALL check_null_values('customer_dimension', 'customer_id', 'CRITICAL');
-- CALL check_null_values('customer_dimension', 'email', 'WARNING');

Self-Service Governance

🛠️

Self-Service Enablement

Technical

Effective self-service governance balances business needs:

• Accelerates time-to-insight without sacrificing quality
• Empowers business users while maintaining consistency
• Reduces IT bottlenecks while ensuring proper controls
• Enables innovation while protecting enterprise assets
• Scales analytics capabilities throughout the organization

🛠️

Self-Service Enablement

Non-Technical

Technical implementation of self-service governance:

• Semantic layers with governed metrics and dimensions
• Content certification workflows for user-created dashboards
• Controlled data access through governed data sources
• Development sandboxes with appropriate guardrails
• Template and starter content libraries

Self-Service Governance Maturity Model

Dimension	Level 1: Ad-Hoc	Level 2: Repeatable	Level 3: Defined	Level 4: Managed	Level 5: Optimized
Data Access	Uncontrolled data extraction	Managed extracts with approval	Governed data sources	Self-service semantic layer	Context-aware data access
Development	Unmanaged desktop tools	Shared development spaces	Template-based creation	Guided analytics development	AI-assisted development
Certification	No certification process	Manual review process	Defined certification workflow	Automated validation checks	Continuous quality monitoring
Education	Ad-hoc training	Formal training curriculum	Role-based certification	Embedded learning tools	Analytics mentorship programs
Community	No collaboration tools	Discussion forums	Knowledge sharing platform	Cross-functional practice groups	Analytics champions network

Training and Support

🎓

BI Education Program

Technical

Effective training programs deliver significant business results:

• Increases analytics adoption and return on BI investments
• Reduces support costs and reliance on specialized resources
• Improves data literacy across the organization
• Accelerates time-to-value for new analytics initiatives
• Creates a data-driven culture with better decision-making

🎓

BI Education Program

Non-Technical

Technical implementation of training programs:

• Role-based learning paths with targeted content
• Hands-on labs and sandbox environments
• Documentation and knowledge base infrastructure
• Video tutorial production and hosting
• Certification and assessment frameworks

BI Support Model

BI Support Structure

Tiered support model for enterprise BI environments

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Components

Support

Resource

Connection Types

Process Flow

Escalates to

Engages

Enables

Measuring Governance Success

📏

Governance Metrics

Technical

Governance metrics demonstrate business value and effectiveness:

• Quantifies return on investment in governance programs
• Identifies areas for process improvement
• Demonstrates compliance with regulatory requirements
• Tracks progress against data strategy objectives
• Builds support for continued investment in data governance

📏

Governance Metrics

Non-Technical

Technical implementation of governance metrics:

• Platform usage and adoption tracking
• Data quality monitoring dashboards
• Content certification rate tracking
• Performance and availability monitoring
• Security and compliance audit reporting

Key Performance Indicators for BI Governance

1. Adoption Metrics

   • Active users per report/dashboard
   • Self-service content creation rate
   • Time spent in BI applications
   • Report-to-user ratio

2. Quality Metrics

   • Data quality score by dimension
   • Error rate in certified content
   • Time to identify and resolve data issues
   • Content certification rate

3. Efficiency Metrics

   • Average time to develop new reports
   • Support ticket volume and resolution time
   • Resource utilization (server, license)
   • Content reuse rate

4. Value Metrics

   • Decision time reduction
   • Cost savings from consolidated efforts
   • Business impact of analytics initiatives
   • User satisfaction scores

Additional Resources

• TDWI Data Governance Fundamentals
• Tableau Blueprint
• Power BI Adoption Framework
• Data Governance Institute Framework
• DAMA Data Management Body of Knowledge