The primary objective of a data quality audit is to evaluate the reliability of data that are used in key business decisions – improving retention, understanding market penetration, customer profiling etc. The fundamental premise of each data quality audit is that business decisions can have varying degrees of sensitivity to data quality and integrity. Our team works closely with key stakeholders to understand the business issues and data workflows before we do our analysis and prepare our recommendations.

Insight’s data quality audit doesn’t end with a simple report of the results. Each audit includes specific recommendations for ongoing data management that can be implemented by your organization and enabled by Insight tools.

Where’s your data quality scorecard?

“Data quality is the foundation of any data intensive business like yours, and assuming that you have good quality data, is like assuming that the foundation of your office building is good!”

Why Data quality score card?

A data quality score card provides a snap shot of your current data quality issues and allows you to take remedial actions in critical areas of concern. The scorecard also provides benchmark metrics which can be tracked over time to measure progress.

Insight’s unique methodology

  • A review of business issues and objectives
  • A definition of the appropriate sampling methodology
  • A system walk-through
  • The use of proprietary software tools
  • An exploratory analysis to detect anomalies
  • A content based analysis
  • A final report with recommendations

The DQmeter test

If you want to get your data quality tested – sign up for a DQmeter to test measure your DQuals (units of data quality).

DQual – A unit of measure of data quality. The DQual measure needs to be read along with the end objective – to provide a consolidated report.