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Faster Insights Drive Better Business Outcomes: A Case Study on Fannie Mae
Technology Category
- Analytics & Modeling - Machine Learning
- Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries
- Chemicals
- Equipment & Machinery
Applicable Functions
- Quality Assurance
Use Cases
- Leasing Finance Automation
The Challenge
Fannie Mae, a leading financial services company, was facing a challenge in managing its vast amount of business data. The company, which enabled the acquisition of more than 2 million home purchases and refinancings, and financing of approximately 598,000 rental units across the United States in 2022, was becoming increasingly digital and data-centric. To leverage all its business data across new and legacy applications, and to break down existing data silos, the company wanted to create an agile and dynamic enterprise data lake. However, the process of managing this data lake was complex and time-consuming. Every single one of its 15,000 datasets went through an initial registration process to assign a unique identifier, and every field had to be documented manually. This approach increased compliance and transparency but made the process slow due to the need to add an elaborate set of metadata to every dataset.
About The Customer
Fannie Mae is a leading financial services company that provides lenders with a reliable source of mortgage financing across the United States. By purchasing mortgage loans, the company helps lenders to offer new mortgages to more people. In doing so, Fannie Mae expands access to affordable housing opportunities, supporting renters, homebuyers and homeowners. With its approximately 8,000 employees, Fannie Mae enabled the acquisition and financing of approximately 2.6 million home purchases, refinancings, and rental units in 2022.
The Solution
To establish a faster and more dynamic data infrastructure, Fannie Mae selected Pentaho Data Catalog as a centralized, data-agnostic tool to accelerate data availability. The software runs fully in the cloud on Amazon Web Services (AWS) across multiple availability zones with auto-scaling to ensure fast performance and business continuity. It processes tens of millions of files and related attributes and aggregates them into thousands of high-level datasets that are easy for the business team to consume and reference for actionable insights. Fannie Mae now relies on process automation based on the Pentaho Data Catalog API, which enables the company to connect its wide range of business applications to the enterprise data lake and update datasets on a daily basis. Pentaho Data Catalog performs an automated pre-registration step, using machine learning and AI to validate and tag metadata and detect sensitive data. It then makes everything immediately available to the company’s metadata analysts, data stewards, data governors and business data officers for further processing and analytics.
Operational Impact
Quantitative Benefit
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