Freddie Mac's Data Transformation with Collibra: A One-Stop Shop for Data
Technology Category
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Consumer Goods
- Transportation
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Last Mile Delivery
- Visual Quality Detection
Services
- Cloud Planning, Design & Implementation Services
- Testing & Certification
About The Customer
Freddie Mac is a leading mortgage loan company with a mission to make home ownership possible for everyone. In 2019, they helped 480,000 first-time home buyers. The company collects and processes vast amounts of data to achieve its mission. Vikram Chopra, the Senior Manager, Single-Family, Data & Decisions at Freddie Mac, led the selection of Collibra to address their data governance and data stewardship needs. He now leads the enhancements and adoption of Collibra across all Freddie Mac divisions. More than 2,000 Freddie Mac data users have access to the business glossary on their computers and mobile devices.
The Challenge
Freddie Mac, a leading mortgage loan company, was faced with the challenge of managing a colossal amount of data collected over the years. The data ecosystem was multi-generational, with Cobolt Mainframes, relational and star schema base data warehouses, data marts, and system-to-system integrations. The company also implemented Hadoop’s analytics platform for big data needs. This complex data ecosystem created complications for data consumers, making it overwhelming to locate the required data. The challenge was to transition to a self-governed cloud native data lake, making all data available in a single place, while ensuring security, resiliency, scalability, reliability, and availability. The business was heavily dependent on the seamless provision of this data for innovation, analytics, and predictive analysis.
The Solution
Freddie Mac turned to Collibra, a data governance and stewardship solution, to address their data challenges. The solution involved designing and implementing a modern cloud native ecosystem in three steps. First, they refreshed their data strategy to align with their divisional business objectives and the departmental mission of a cloud native data strategy. Next, they built the cloud onboarding solution with Collibra at the forefront. Finally, they added integrations where needed to ensure smooth and optimized data pipelines. Collibra was used to manage content data, provide data quality scorecards and metrics for data elements, and empower data users to find data quickly. They also integrated Collibra with their own data quality processes to measure the quality of data as it flowed into the lake. An intake workflow was designed and developed in Collibra to identify and classify data sets that needed to be provisioned in the cloud.
Operational Impact
Quantitative Benefit
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