Hitachi Vantara (Hitachi) Case Studies Faster Insights Drive Better Business Outcomes: A Case Study on Fannie Mae

	Faster Insights Drive Better Business Outcomes: A Case Study on Fannie Mae Hitachi Vantara (Hitachi)

Faster Insights Drive Better Business Outcomes: A Case Study on Fannie Mae

Hitachi Vantara (Hitachi)

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	With the implementation of Pentaho Data Catalog, accessing critical business information has become easier than ever for Fannie Mae. The company is now using the solution to tag its data, for example, to highlight sensitive and personal information and classify more than 400 key data elements (KDEs). This has enabled faster analytics and insights, which translate into better business outcomes. The data-agnostic solution highlights changes in storage location, file size, file format and many other technical details that can help the team to tune and optimize the data processing. Built-in metadata versioning helps Fannie Mae keep track of changes in its data sources and better understand the context of its business data.

Impact #1

With the implementation of Pentaho Data Catalog, accessing critical business information has become easier than ever for Fannie Mae. The company is now using the solution to tag its data, for example, to highlight sensitive and personal information and classify more than 400 key data elements (KDEs). This has enabled faster analytics and insights, which translate into better business outcomes. The data-agnostic solution highlights changes in storage location, file size, file format and many other technical details that can help the team to tune and optimize the data processing. Built-in metadata versioning helps Fannie Mae keep track of changes in its data sources and better understand the context of its business data.

Benefit #1	Automated processing of 10 million files per day
Benefit #2	Supported decision-making to provide $684 billion in liquidity to the mortgage market in 2022
Benefit #3	Enabled the acquisition and financing of approximately 2.6 million home purchases, refinancings, and rental units in 2022

Benefit #1

Automated processing of 10 million files per day

Benefit #2

Supported decision-making to provide $684 billion in liquidity to the mortgage market in 2022

Benefit #3

Enabled the acquisition and financing of approximately 2.6 million home purchases, refinancings, and rental units in 2022

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Overview

Faster Insights Drive Better Business Outcomes: A Case Study on Fannie Mae

Operational Impact

Quantitative Benefit