What I Deliver

Six core pillars of data excellence, each delivered with principal-led accountability

1. Analytics & Decision Intelligence

Problems Solved

  • Data exists but doesn't drive decisions
  • Analytics are reactive, not predictive
  • Decision-makers lack timely, relevant insights
  • Metrics are inconsistent across departments

Approach

  • Define decision-critical metrics aligned to business objectives
  • Build semantic layers that standardize definitions
  • Create self-service analytics capabilities
  • Implement predictive and prescriptive analytics where appropriate

Deliverables

  • Decision intelligence frameworks
  • KPI dashboards and scorecards
  • Self-service analytics platforms
  • Predictive models and forecasting
  • Documentation and user training

Outcomes

  • Faster, data-driven decision-making
  • Consistent metrics across the organization
  • Reduced time-to-insight from days to minutes
  • Proactive rather than reactive analytics

2. Analytics Engineering

Problems Solved

  • BI teams spend too much time on data transformation
  • KPI definitions differ across reports
  • No single source of truth for business logic
  • Data models are inconsistent and hard to maintain

Approach

  • Implement semantic layer patterns (dbt, metrics layer)
  • Standardize KPI definitions and business logic
  • Build reusable, tested data models
  • Establish version-controlled transformation pipelines
  • Create self-documenting data catalogs

Deliverables

  • dbt projects with standardized models
  • Semantic layer implementation
  • KPI standardization frameworks
  • Data model documentation
  • CI/CD for analytics pipelines

Outcomes

  • 90%+ reduction in transformation time
  • Consistent metrics across all reports
  • Self-documenting, maintainable data models
  • Faster onboarding for new team members

3. Data Engineering

Problems Solved

  • Legacy ETL processes are brittle and slow
  • Real-time data requirements can't be met
  • Data pipelines fail without proper monitoring
  • Scalability concerns as data volume grows

Approach

  • Modern ELT patterns with cloud-native tools
  • Batch and streaming pipelines as needed
  • Data lake and warehouse architecture
  • Orchestration with Airflow/ADF
  • Comprehensive monitoring and alerting

Deliverables

  • ETL/ELT pipelines (batch and streaming)
  • Data platform architecture
  • Data lake/warehouse implementation
  • Orchestration and monitoring
  • Data quality checks and validation

Outcomes

  • Reliable, scalable data pipelines
  • Real-time data availability where needed
  • Reduced pipeline failures with proactive monitoring
  • Infrastructure that scales with growth

4. BI & Visualization

Problems Solved

  • Reports are slow and users lose confidence
  • Security and access controls are weak
  • DAX calculations are incorrect or inconsistent
  • Dashboard performance degrades with usage

Approach

  • Enterprise-grade Power BI architecture
  • Optimized data models and DAX
  • Row-level security (RLS) implementation
  • Performance tuning and optimization
  • User-centric design principles

Deliverables

  • Power BI enterprise solutions
  • Optimized data models
  • DAX measures and calculations
  • Row-level security (RLS)
  • Performance-optimized dashboards
  • User training and documentation

Outcomes

  • Sub-second dashboard load times
  • Secure, role-based access
  • Accurate, trusted reporting
  • High user adoption and satisfaction

5. Data Governance & Quality

Problems Solved

  • Data quality issues cause downstream errors
  • No visibility into data lineage
  • Reconciliation processes are manual
  • Data quality rules aren't enforced

Approach

  • Define data quality rules and standards
  • Implement automated quality checks
  • Build data lineage tracking
  • Create reconciliation frameworks
  • Establish governance policies and processes

Deliverables

  • Data quality frameworks and rules
  • Automated quality monitoring
  • Data lineage documentation
  • Reconciliation processes
  • Governance policies and procedures
  • Data quality dashboards

Outcomes

  • Proactive detection of data quality issues
  • Full visibility into data lineage
  • Automated reconciliation processes
  • Trustworthy, reliable data

6. Audit, Compliance & Trust

Problems Solved

  • Audit processes are manual and time-consuming
  • No traceability for data transformations
  • Compliance requirements aren't met
  • Lack of transparency in reporting

Approach

  • Build audit-ready data architectures
  • Implement comprehensive audit trails
  • Ensure traceability for all transformations
  • Create transparent reporting frameworks
  • Establish compliance controls and monitoring

Deliverables

  • Audit-ready data platforms
  • Comprehensive audit trails
  • Traceability documentation
  • Compliance frameworks
  • Transparent reporting systems
  • Control documentation

Outcomes

  • Audit-ready systems with minimal effort
  • Full traceability for regulatory compliance
  • Transparent, trustworthy reporting
  • Reduced compliance risk

Engagement Types

Advisory & Architecture Review

Assess your current data architecture, identify gaps, and provide actionable recommendations for improvement.

Delivery Leadership

Lead end-to-end implementation of data platforms, analytics solutions, or BI modernization projects.

BI Modernization

Transform legacy BI systems into modern, performant, enterprise-grade solutions.

Governance & Quality Audit

Evaluate data governance maturity, data quality, and compliance readiness with detailed recommendations.

Training & Enablement

Train your team on modern data tools, best practices, and enable independent operation.

Discuss an Engagement