Efficient and scalable data governance and architecture at a Fortune 100 company

Financial Services


The client is a Fortune 100 government-sponsored enterprise that purchases, guarantees, and securitizes home loans. The Enterprise Risk Management (ERM) Divisional Chief Data Officer wanted to advance their capabilities, governance, and infrastructure towards a more data-driven approach to compliance with regulatory, policy, and enterprise program requirements. Accelerating ERM'S data governance and analytics would be a critical driver of ERM's strategic imperative of effective oversight and credible challenger role. Our goal was to create a roadmap to achieve the following objectives: assess the current state of ERM's data maturity and outline actionable steps to reach greater maturity; develop a data governance platform to promote clarity, accountability, and partnership; prioritize and implement best practices for data management capabilities to ensure data reliability and usability; and collaborate with ERM partners to establish sustainable and agile data programs able to adapt to the developing enterprise data ecosystem.


The project consisted of two parallel efforts, data governance and data architecture. The technology stack included Collibra, Snowflake, BWise, Tableau, AWS, and Apache Spark (PySpark).  

Data governance had everything to do with people and processes. We established data ownership and accountability through clear delineations of responsibilities for maintaining data via the appropriate communication channels. We removed silos and established data centrality, or a trusted source of truth shared by all stakeholders with an ability to search and monitor expectations, risk data, and metadata.  

For data architecture, we pulled the client’s existing source data from BWise and loaded it into Snowflake. We cleansed, hydrated, and transformed that data in a way that would be usable by consumers (other systems and divisions within the enterprise). Then, we pushed it into their metadata tool, Collibra, so it was discoverable to ERM stakeholders.


  • A new data enterprise platform with quality, clean, and mature data.  
  • We added value to ERM’s existing data and established best practices for data management to ensure reliable, usable, and discoverable data across the enterprise.  
  • Together, we instantiated an efficient and scalable model for data architecture and governance and improved the capabilities of data oversight and management from an enterprise point of view.

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