Data & Analytics • Enterprise

Enterprise Data Warehousing

Building Data-Driven Decision Making Infrastructure

Duration

10 months

Team Size

6 members

Industry

Enterprise

Role

Data Architecture Lead & Business Intelligence Consultant

Project Overview

Designed and implemented a comprehensive enterprise data warehousing solution, centralizing data from disparate sources to enable advanced analytics, business intelligence, and data-driven decision-making across the organization.

Data Silos and Fragmentation

The enterprise client faced significant challenges with fragmented data across multiple systems, making it nearly impossible to gain unified insights or make informed strategic decisions.

▸Data scattered across 15+ disconnected systems
▸Inconsistent data definitions and quality issues
▸No single source of truth for business metrics
▸Time-consuming manual report generation
▸Limited historical data for trend analysis
▸Inability to perform cross-functional analytics

Centralized Data Warehouse Platform

Implemented a robust enterprise data warehouse with ETL pipelines, data quality framework, and self-service BI capabilities enabling organization-wide data access and insights.

Comprehensive data source inventory and mapping

Dimensional modeling for optimized query performance

ETL pipeline development for data integration

Data quality and governance framework

Self-service BI layer with semantic models

Real-time and batch processing capabilities

Security and access control implementation

Data Warehouse Development

Phase 1: Planning & Design (8 weeks)

Architecture design and data strategy development

Deliverables:

•Data source assessment and cataloging
•Dimensional model design
•ETL architecture blueprint
•Data governance framework
•Technology stack selection

Phase 2: Foundation Build (16 weeks)

Core data warehouse infrastructure and initial ETL

Deliverables:

•Data warehouse database setup
•Core dimension and fact tables
•Initial ETL pipelines (5 sources)
•Data quality validation framework
•Monitoring and alerting system

Phase 3: Expansion & BI Layer (12 weeks)

Additional source integration and BI development

Deliverables:

•Remaining ETL pipelines (10 sources)
•Historical data migration
•BI semantic layer development
•Self-service reporting portal
•Executive dashboards and KPIs

Phase 4: Deployment & Enablement (4 weeks)

User training and production deployment

Deliverables:

•Production deployment
•User training programs
•Documentation and guidelines
•Support model establishment
•Performance optimization

Technologies & Tools

Data Platform

SQL ServerData WarehousingDimensional ModelingColumnstore Indexes

ETL & Integration

ETL ToolsSSISData IntegrationChange Data CaptureData Quality Services

Business Intelligence

Power BISSASDAXSemantic ModelsSelf-Service BI

Governance

Data CatalogingMaster Data ManagementData Quality FrameworkMetadata Management

Data-Driven Transformation

-85%

Report Generation Time

Reduced from days to minutes with automated dashboards

99.2%

Data Accuracy

Improved from 82% to 99.2% with quality framework

50x

Query Performance

Optimized queries running 50x faster than legacy systems

15 sources

Data Integration

Successfully integrated all 15 disparate data sources

7 years

Historical Data

Centralized 7 years of historical data for analysis

500+

BI User Adoption

Over 500 users actively using self-service analytics

Additional Impact

Established single source of truth for enterprise metrics
Enabled strategic decision-making with unified insights
Improved cross-departmental collaboration through shared data
Enhanced regulatory compliance and audit capabilities
Reduced IT dependency for reporting and analytics
Created foundation for advanced analytics and ML initiatives

"The data warehouse has transformed how we make decisions. Having all our data in one place with consistent definitions has been revolutionary for our business intelligence capabilities."

Chief Data Officer

Enterprise Client

Key Learnings

▸Data governance must be established early in the project
▸Dimensional modeling provides optimal balance of flexibility and performance
▸Self-service BI drives adoption but requires proper training
▸Incremental ETL development allows for iterative validation
▸Data quality issues must be addressed at the source when possible
▸Executive dashboard success depends on understanding business needs

Future Enhancements

▸Real-time streaming data integration
▸Advanced analytics and data science sandbox
▸Cloud migration for scalability and cost optimization
▸Machine learning model deployment and scoring
▸Natural language query interface
▸Data lake integration for unstructured data

Interested in Similar Results?

Let's discuss how I can help transform your business with proven strategies and measurable outcomes.

Data & Analytics • Enterprise

Enterprise Data Warehousing

Building Data-Driven Decision Making Infrastructure

Duration

10 months

Team Size

6 members

Industry

Enterprise

Role

Data Architecture Lead & Business Intelligence Consultant

Project Overview

Data Silos and Fragmentation

The enterprise client faced significant challenges with fragmented data across multiple systems, making it nearly impossible to gain unified insights or make informed strategic decisions.

▸Data scattered across 15+ disconnected systems
▸Inconsistent data definitions and quality issues
▸No single source of truth for business metrics
▸Time-consuming manual report generation
▸Limited historical data for trend analysis
▸Inability to perform cross-functional analytics

Centralized Data Warehouse Platform

Implemented a robust enterprise data warehouse with ETL pipelines, data quality framework, and self-service BI capabilities enabling organization-wide data access and insights.

Comprehensive data source inventory and mapping

Dimensional modeling for optimized query performance

ETL pipeline development for data integration

Data quality and governance framework

Self-service BI layer with semantic models

Real-time and batch processing capabilities

Security and access control implementation

Data Warehouse Development

Phase 1: Planning & Design (8 weeks)

Architecture design and data strategy development

Deliverables:

•Data source assessment and cataloging
•Dimensional model design
•ETL architecture blueprint
•Data governance framework
•Technology stack selection

Phase 2: Foundation Build (16 weeks)

Core data warehouse infrastructure and initial ETL

Deliverables:

•Data warehouse database setup
•Core dimension and fact tables
•Initial ETL pipelines (5 sources)
•Data quality validation framework
•Monitoring and alerting system

Phase 3: Expansion & BI Layer (12 weeks)

Additional source integration and BI development

Deliverables:

•Remaining ETL pipelines (10 sources)
•Historical data migration
•BI semantic layer development
•Self-service reporting portal
•Executive dashboards and KPIs

Phase 4: Deployment & Enablement (4 weeks)

User training and production deployment

Deliverables:

•Production deployment
•User training programs
•Documentation and guidelines
•Support model establishment
•Performance optimization

Technologies & Tools

Data Platform

SQL ServerData WarehousingDimensional ModelingColumnstore Indexes

ETL & Integration

ETL ToolsSSISData IntegrationChange Data CaptureData Quality Services

Business Intelligence

Power BISSASDAXSemantic ModelsSelf-Service BI

Governance

Data CatalogingMaster Data ManagementData Quality FrameworkMetadata Management

Data-Driven Transformation

-85%

Report Generation Time

Reduced from days to minutes with automated dashboards

99.2%

Data Accuracy

Improved from 82% to 99.2% with quality framework

50x

Query Performance

Optimized queries running 50x faster than legacy systems

15 sources

Data Integration

Successfully integrated all 15 disparate data sources

7 years

Historical Data

Centralized 7 years of historical data for analysis

500+

BI User Adoption

Over 500 users actively using self-service analytics

Additional Impact

Established single source of truth for enterprise metrics
Enabled strategic decision-making with unified insights
Improved cross-departmental collaboration through shared data
Enhanced regulatory compliance and audit capabilities
Reduced IT dependency for reporting and analytics
Created foundation for advanced analytics and ML initiatives

"The data warehouse has transformed how we make decisions. Having all our data in one place with consistent definitions has been revolutionary for our business intelligence capabilities."

Chief Data Officer

Enterprise Client

Key Learnings

▸Data governance must be established early in the project
▸Dimensional modeling provides optimal balance of flexibility and performance
▸Self-service BI drives adoption but requires proper training
▸Incremental ETL development allows for iterative validation
▸Data quality issues must be addressed at the source when possible
▸Executive dashboard success depends on understanding business needs

Future Enhancements

▸Real-time streaming data integration
▸Advanced analytics and data science sandbox
▸Cloud migration for scalability and cost optimization
▸Machine learning model deployment and scoring
▸Natural language query interface
▸Data lake integration for unstructured data

Interested in Similar Results?

Let's discuss how I can help transform your business with proven strategies and measurable outcomes.