Automating Data Management and Accelerating Time-to-Market - Microsoft Velocity Project
Introduction
The Microsoft Velocity Project addressed challenges in managing a centralized data repository and meeting strict SLA timeframes. By providing downstream teams access to centralized data for building data cubes, the project tackled obstacles such as handling large data volumes, creating an efficient ETL process, limited source database access, and manual process delays. Through innovative solutions and automation, the Velocity Project achieved improved efficiency, reduced development efforts, cost savings, and enhanced decision-making capabilities for Microsoft.
Problem
The Microsoft Velocity Project faced challenges in managing a centralized data repository, including handling large data volumes within strict SLA timeframes, creating a high-density low-latency ETL process, limited source database access, and manual processes causing delays and inefficiencies..
Solution
To overcome these challenges, the Velocity Project implemented solutions such as Standard Transformation Mapping (STM) for metadata acquisition, automated database script generation, and an enhanced data acquisition model to automate the creation of databases and Informatica workflows.
Result
The implemented solutions yielded significant results:
- Development work reduced by 50 to 80%, allowing resource allocation to performance tuning and efficiency improvements.
- Time-to-market for ETL sequences improved by 20% to 30%, enabling faster data cycles and timely information availability.
- Cost savings achieved through reduced resource deployment for development and QA.
- Enhanced decision-making and increased opportunities due to streamlined processes and timely information.
Conclusion
The Microsoft Velocity Project successfully addressed the challenges of managing a centralized data repository and meeting stringent SLA timeframes. By implementing solutions such as STM, automated script generation, and enhanced data acquisition models, the project achieved remarkable results, including reduced development efforts, improved time-to-market, cost savings, enhanced decision-making, and increased opportunities for the organization. Automating and streamlining data management processes proved crucial for driving efficiency and competitiveness in a fast-paced business environment.