Data Warehouse Modernization — The 5 Phases (+ Optional AI Phase)
Built New or Modernized at Scale
1. Data Landscape Assessment & Strategic Alignment
We evaluate your current warehouse, pipelines, and reporting ecosystem to understand gaps, technical debt, and modernization opportunities.
Steps:
- Review existing warehouse architecture and data flows
- Identify performance issues, bottlenecks, and reliability risks
- Assess data quality, lineage, and governance gaps
- Map business requirements to modernization priorities
- Define the modernization roadmap and success criteria
2. Modern Data Platform Architecture & Cloud Design
We design a scalable, cloud‑ready architecture that supports long‑term growth, performance, and analytics maturity.
Steps:
- Define modern warehouse architecture (lakehouse, medallion, etc.)
- Establish data zones, layers, and governance patterns
- Design for scalability, cost efficiency, and performance
- Align with Microsoft best practices and cloud standards
- Document architecture and platform blueprint
3. Data Ingestion, Migration & Warehouse Modernization
We rebuild pipelines, migrate workloads, and modernize your warehouse foundation using efficient, reliable engineering practices.
Steps:
- Modernize ETL/ELT pipelines using cloud‑native tools
- Migrate legacy workloads and schemas
- Rebuild or optimize data models and transformations
- Improve refresh performance and reliability
- Implement data quality and validation frameworks
4. Enterprise Analytics, BI Modernization & Operationalization
We enable trusted analytics by delivering clean semantic models, governed metrics, and modern BI experiences.
Steps:
- Build or refine semantic models for Power BI
- Standardize KPIs and metric definitions
- Improve data accessibility and self‑service readiness
- Validate metrics with business stakeholders
- Operationalize BI with governance and best practices
5. Optimization, Governance Maturity & Continuous Improvement
We tune performance, strengthen governance, and ensure the platform remains cost‑efficient, secure, and scalable.
Steps:
- Optimize compute, storage, and pipeline performance
- Implement governance for access, lineage, and quality
- Establish monitoring and alerting for warehouse health
- Provide documentation and enablement for teams
- Create a continuous improvement and enhancement cycle
6. (Optional) AI Opportunity Assessment & Readiness Evaluation
We identify where AI can create value by reviewing data readiness, business processes, and high‑impact use cases.
Steps:
- Assess data readiness for AI workloads
- Identify practical, high‑value AI use cases
- Evaluate opportunities for predictive analytics
- Explore natural‑language and automated insight capabilities
- Recommend pilot opportunities and next‑step roadmap
