Case Study

Data Quality Remediation Program

Arrayo, a leading consulting firm, was engaged by an international financial institution to assist the Data Quality (DQ) Pillar Team within Enterprise Data Management (EDM). The DQ Pillar is tasked with ensuring effective and consistent management of the bank’s data, which involves the resolution of various data issues. Given the backlog of critical data issues, Arrayo was brought in to review and improve the data remediation process, as well as expedite and manage the actual remediation of a large number of issues, primarily related to Finance and Risk data.


The primary objective of this engagement was to address and resolve the backlog of data issues affecting critical data sets. These data sets are vital for external reporting, continuous information provision to the bank’s board, and preventing potential financial loss or severe customer impact.


The Arrayo team was responsible for:

  1. Manage existing DQ Issue Management Process
  2. Review the existing data remediation process
  3. Recommend and implement improvements to the existing process
  4. Maintain Consolidated DQ issue list
  5. Provide guidance and expertise to enhance the DQ Issue Management Process, including the use of JIRA, development of Power BI Reports, and use of Microsoft Teams Form
  6. Performing and tracking Business-As-Usual (BAU) DQ issue remediation activities.
  7. Leading working sessions with various other EDMO pillars, Data Stewards, Business Users, IT teams, and Data Owners.
  8. Conducting root cause analysis of identified issues.
  9. Determining the appropriate team to handle each remediation issue.
  10. Reaching consensus on suitable remediation actions (both tactical and strategic).
  11. Assigning tasks and tracking remediation activities to resolution.
  12. Reporting and escalating issues as necessary.
  13. Communicating with the EDM DQ team to suggest enhancements to existing controls based on lessons learned.
  14. Creating process flows, including controls, and documenting procedures
  15. Gathering DQ requirements from business users.


Step 1: Root Cause Analysis

Arrayo conducted detailed root cause analyses for each identified data issue. This involved:

  • Analyzing the data issues to understand their origin.
  • Collaborating with relevant stakeholders to gather necessary information.
  • Documenting findings and proposing initial remediation steps.

Step 2: Team Designation and Consensus Building

The team held working sessions with various stakeholders, including Data Stewards, Business Users, IT teams, and Data Owners, to:

  • Designate appropriate remediation owners.
  • Reach consensus on remediation strategies, whether tactical (short-term) or strategic (long-term).

Step 3: Remediation Activities

Arrayo coordinated the remediation activities by:

  • Assigning tasks to relevant teams.
  • Tracking the progress of remediation efforts.
  • Ensuring timely resolution of data issues.

Step 4: Reporting and Escalation

Regular reports were generated to track the status of remediation activities. Arrayo also:

  • Escalated unresolved issues to higher management when necessary.
  • Provided updates to the EDM DQ team.

Step 5: Communication and Controls Enhancement

Based on the remediation activities, Arrayo:

  • Suggested enhancements to existing data controls.
  • Communicated these suggestions to the EDM DQ team for implementation.

Step 6: Documentation and Requirements Gathering

Arrayo documented the data lineage, including controls, and gathered DQ requirements from business users to prevent future issues.



  • Backlog Reduction: Significant reduction in the backlog of critical data issues.
  • Improved DQ: Enhanced the overall quality of critical data sets.
  • Effective Collaboration: Fostered better collaboration among EDMO pillars, Data Stewards, Business Users, IT teams, and Data Owners.
  • Enhanced Controls: Improved data controls and prevention mechanisms based on lessons learned.
  • Procedure document which can be used as an artifact for Internal Audit and Regulators


  • Regulatory Compliance: Ensured compliance with regulatory requirements through accurate and reliable data for external reporting.
  • Decision-Making: Provided the bank’s board with trustworthy data for informed decision-making.
  • Risk Mitigation: Reduced the risk of financial loss and customer impact due to data deficiencies.