Enterprise Data Architecture and Data Governance Strategy

Enterprise Data Architecture and Data Governance S...

Case Study

Enterprise Data Architecture and Data Governance Strategy

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Customer Challenge

The client, a global trading venue, embarked on a firm-wide data strategy programme to address a number of issues that limited the value of the firm’s data.

  • Data was difficult to locate and access
  • Integrity of data was not guaranteed because of poor data lineage tracking
  • Self-service data discovery was not possible
  • Data entitlements and access controls were not appropriate for business needs
  • Inconsistent data models made systems integration complex and costly

Citihub was asked to evaluate the current data landscape and develop a strategy and roadmap for data that would deliver the data strategy programme objectives.

How Citihub Helped

Citihub recommended an enterprise data management strategy that defined the data architecture, a data governance model and solutions that would make data more accessible, usable, accurate and secure.

This allowed the client to optimise the use of data across the firm, creating value through monetisation of proprietary sources, improved access, and improved analytics.

Citihub used their data governance and data architecture framework and maturity model to provide structure for the following activities:

  • Evaluate the current data landscape against best practices
    • Determine current state data model
    • Determine current state data processes, systems, and lineage
  • Develop a strategic vision for firm-wide data management
  • Develop high-level target data architecture and governance model
  • Develop a strategy and roadmap that will deliver the business objectives

The strategic recommendations improved business operations’ effectiveness with a consolidated data warehouse and improved data access and analytics with a data lake hosted on public cloud.


Improved operational effectiveness

The client had a number of data warehouse solutions with inconsistent views of operational data. Consolidating operational data into a single data warehouse supported better data integrity, consistency, and lineage tracking. The resulting operational data service was more accessible, usable and accurate, improving the business’ operational effectiveness.

Data-driven business value

Introducing a public cloud hosted data lake enabled improved self-service data access and data analytics; allowing business analysts and researchers to rapidly innovate and generate value from business insights.

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