- Current State (As Is) Assessment
- Conduct a detailed as is assessment of the data landscape, covering:
- Data storage platforms (on prem, cloud, lakehouse, warehouse)
- Data ingestion and integration pipelines (batch, streaming, real time)
- Data processing frameworks and analytical workloads
- Data consumption patterns (BI, reporting, advanced analytics, AI/ML)
- Evaluate the current architecture against industry best practices and reference architectures in areas such as:
- Cloud native and Lakehouse patterns
- Data mesh / domain-oriented architectures
- Identify architectural gaps, technical debt, and design constraints.
2. Future State Architecture & Solution Design
- Define future state data architecture blueprints aligned to:
- Modern data platform best practices
- Cloud native and scalable design principles
- Analytics and AI enablement
- Propose technology agnostic and vendor aligned solutions based on prior experience with modern data architectures.
- Establish enterprise standards for:
- Data ingestion, modeling, storage, and access
- Data interoperability and integration
- Architectural patterns and reusable components
4. Roadmap & Capability Planning
• Drive the development of a long-term data architecture roadmap, including:
o Target capabilities and architectural milestones
o Phased initiatives with sequencing and dependencies
o Modernization priorities aligned to business outcomes
• Partner with engineering teams to guide execution and ensure alignment to architectural intent.
5. Governance Alignment
• Collaborate with data governance and security teams to ensure:
o Architecture aligns with governance, privacy, and compliance requirements
o Governance controls are embedded into data platforms by design
Expected Outcomes
• Clear current state vs future state architecture view
• Scalable and resilient data platform foundation
• Reduced architectural complexity and technical debt
• Architecture that supports analytics, AI, and future growth
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