Case Study

How optimizing data management supports financial systems modernization

A roadmap for how DHS ICE will execute an efficient and accurate financial systems modernization program.

The better the question

How do you move from data to insight?

Analyzing raw data by business process is crucial in constructing a complete data model.


The U.S. Department of Homeland Security (DHS) Immigration and Customs Enforcement (ICE) Office of the Chief Financial Officer (OCFO) mission is to resource and enable the ICE mission, while providing sound stewardship through dedication and professionalism of its financial, budgetary and asset management workforce. To support this objective, ICE is working across DHS to execute a Financial Systems Modernization (FSM) program to modernize financial, accounting and other business operations as part of DHS’s overall modernization initiative to update legacy financial systems with an emphasis on security, data integrity and more accurate, transparent financial reporting.

At the foundation of financial systems is the data generated from all of the agency’s financial management business processes. Without an understanding of this data and the ability to directly interact with it, ICE cannot be successful in this large-scale modernization effort. Since 2020, Ernst & Young LLP has been supporting ICE in conducting in-depth analysis of its data to identify relationships, detect data quality issues and understand root causes to address today, prior to migration to a new ERP system.

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The better the answer

Give your people the data tools and knowledge to drive success

The journey to prepare ICE and its Customer Components for a comprehensive data management platform.


ICE needed a comprehensive data management platform that will serve as the foundation for data quality analysis, cleansing and migration design to help move the overall modernization program forward. As such, we embarked on a journey to prepare ICE and its Customer Components for this challenge.

Building a data model

The first step was to analyze ICE’s raw data and design an integrated, nonproprietary financial data model (FDM) to help ICE better understand its data. We leveraged its data architecture and financial management experience to iteratively build this data model, by business process, focusing on referential integrity, data populations and required elements to construct and trace complete financial transactions.

Along with this data model, we sought to develop an actionable tool for ICE and ICE’s Customer Components to execute data analysis and track cleansing activities in advance of financial systems migration. We envisioned a secure, cloud-based analytical tool that allowed users to develop queries by functional users, assess data quality, identify areas for standardization and address reconciliation concerns before implementation.

Developing and maintaining a data pipeline


Transforming and populating data from financial management, asset and procurement systems for DHS Cube Components — ICE, USCIS (U.S. Citizenship and Immigration Services), CISA (Cybersecurity and Infrastructure Security Agency), S&T (Science and Technology Directorate), DMO (Management Directorate), FPS (Federal Protective Service) and OBIM (Office of Biometric Identity Management) — into a secure and reliable data repository required a scaled data integration solution with minimal manual intervention. We developed an automated, scaled integration solution that supported both current pre-migration data quality analysis and cleansing needs but also provided ICE with the rapid data pipeline that would be required during mock conversions and eventual migration to the new system. 

Deploying an integrated data management system

With the universe of financial management data integrated into a single platform, we developed a financial data dictionary to document, define and catalog all financial tables, data elements, data relationships, and other business and technical attributes. Armed with the data model, data dictionary and the data platform itself, we piloted the platform for ICE key financial management users at headquarters and supporting finance centers. We executed a roadshow across the agency to demonstrate functionality and training tailored to each person’s data analysis and cleansing role within the larger financial systems modernization program. Well received by users, the platform was soon expanded to all of ICE’s customer agencies and included providing support and guidance.

DMO used the FDM to obtain an understanding of what subledger data looks like on the back end and how we could connect the dots to pull and use that data. We’ve used the trial balance tables to perform some transactional research, pulled master data records to inform how we can link data between operational reports and verified that transactions were provided to the appropriate vendor.

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The better the world works

Data and people will drive the implementation

ICE now has the tools and capabilities to access, move and understand its data in a way like never before.


Agencies must have a detailed understanding of their data to confirm that their business decisions and actions are based on an accurate and reliable foundation. This data platform has enabled DHS to develop a deeper understanding of its data, perform analysis to identify those data quality issues and then review the business processes to address the underlying cause, well in advance of ERP migration. This platform will also serve well in testing and migration activities, as ICE already has a reliable and effective process for moving and validating large volumes of data efficiently. However, the data platform alone will not guarantee FSM success. As such, we developed a comprehensive data management and governance plan that will guide the roles, responsibilities and processes to execute the migration and enable digital transformation for DHS.
Delivering optimal data quality for the financial systems modernization in DHS is our driving goal. Having proper data hygiene means our organizational identity and the underlying data is clean, standardized and free of errors, and all entries we will make in the new system are linked and accessible. Our team objective is to ensure that all users of the new business solution suite have the best possible experience, leading to both improve...

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