Case Study: Strategy & Governance

Data Quality
Management

Insurance Division, Canadian Banking


Data quality is a key measure of business success for all organizations, particularly those in the banking sector. Not only is data quality crucial to meet regulatory requirements, it is also essential for banks to remain competitive. Banks need to trust their data to make intelligent, data driven decisions.

The Challenge

The Insurance Division of a Big 5 Canadian Bank sought to improve their data accuracy, regulatory reporting, and decision-making with a solid data quality management framework as its foundation.

The Solution

Optimus Data provided programming capabilities, project oversight, and strategic support to the bank’s insurance division to create a robust and comprehensive Data Quality Management framework. Leveraging a proven methodology, the Optimus Data team developed an automated platform to identify data quality exceptions in Critical Data Elements (CDEs) across all Systems of Records (SORs). This automated platform facilitates timely and accurate management and regulatory reporting leading to more effective decision-making.

The Results


  • Automated Data Quality Analytics Platform – developed platform through IBM InfoSphere to capture data quality exceptions on a regular and ad hoc basis
  • Comprehensive Analytics Reports – developed automated Tableau dashboards to report data quality analysis for decision-making
  • Issues Management and Resolution Framework – supported the development of framework to intake, prioritize, analyze and resolve exceptions
  • Current State System Health – performed large-scale, multiple system, profiling exercise to provide executive leadership team with ‘big picture’ of all insurance databases’ general health
  • Downstream Impact Review – informed Risk, Finance and IT of issues prior to consumption within Data Lake and Critical Reports
Developed an automated Data Quality Analytics platform to capture data quality expectations
Built Tableau dashboards to report data quality analysis for improved decision-making
Provided executive leadership team with ‘big picture’ of all insurance databases’ general health