Here's how you can craft a data architecture strategy for a large organization.

Discuss with leaders across your different organization business areas their vision, strategies, and objectives. Learn what data and technology limitation challenges and issues they are experiencing that they see as obstacles from reaching their goals. Understand what future business capabilities they are planning to introduce. Consult with Privacy and Compliance to understand what new regulatory laws/rules are pending they impact data management (e.g., state privacy laws including AI). Consolidate all of the points you gather and use as the basis for framing your data strategy.

Understand Business Goals! This will help you align your data architecture strategy with the organization’s strategic direction. Assess the current state of data management in the organization. This includes understanding the existing data architecture, data sources, data quality, data governance, and data management practices. Identify gaps in data quality, data integration, data governance, or data security. Define the future state of data management that supports the organization’s business goals. Develop a roadmap to transition from the current state to the future state. Implement the data architecture strategy according to the roadmap. Monitor the implementation of the data architecture strategy and adjust as necessary.

Defining right framework is the cornerstone of the overall data architecture as it considers not only current data demand but also the future demand without changing the existing framework aka scalable design. I strongly recommend domain driven design especially for the large organizations.

This is the most important step of the data architecture; if you don't standarize your business terms, rules and concepts; you will have problem on your data model and how it work from business activities perspective.

It is critical to ensure that all data policies are actually implemented. And the results of those implementation steps are captured and any anomalies escalated appropriately to the Chief Data Officer. A DG policy without the implementation will end up being just a document on paper with no teeth behind it.

I agree with all the mentioned important aspects of data lifecycle management. Additionally, I recommend considering flexibility as a crucial factor. The ability to adapt to ongoing technological innovations, scale, data consumption patterns, user personas, and the underlying data itself would significantly enhance the architectural solution. While this is easier said than done, it’s essential to give it ample consideration from the beginning

Start by conducting a comprehensive assessment of current data infrastructure, systems, and processes to identify strengths, weaknesses, and areas for improvement. Define clear goals and priorities based on organisational needs, such as enhancing data quality, improving accessibility, ensuring scalability, and strengthening data security and compliance. Develop a roadmap that outlines the desired future state of data architecture, including key milestones, timelines, and resource requirements. Incorporate best practices in data management, governance, and integration to ensure consistency and reliability across the organisation.