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Data Quality Rules


Data profiling is the statistical analysis of your data and is the first step in a successful data initiative.

The rules applied to your data will ultimately define whether your data is of good quality or not, how much reliance and trust you may place upon your data and if it is fit for purpose.

Data quality rules allow an organisation to measure and quantify the quality of their data and to understand where in the organisation data quality issues exist.

They allow a better understanding of what is wrong with the data: Is it missing, is it of inconsistent format or length, is it inconsistent with alternative sources, is it dynamic when it should be static, or is it a statistical outlier.

Data quality rules allow comparison of data quality in a consistent manner across multiple systems, against peers or for external bodies, such as regulators.

The Benefits

  • The organisation can assert its data quality.
  • Understand the scale of data quality issues both in terms of defects and the monetary impact of those issues.
  • Identify where the issues exist e.g. a particular system, table or field. A particular geographical region, a range of products or a process e.g. KYC or AML or Collateral management.
  • Can be mapped to dimensions of data quality.
  • Prioritisation of remediation efforts can be focused on the most material data quality issues.
  • Monitor data quality over time and track the progress of remediation activities.
  • Produce Key Performance Indicators (KPI’s), dashboards and scorecards of quality for executive management.
  • Data quality rules can act as a filter to route erroneous records to an error handling team and therefore stop poor quality data progressing into further systems.

Below is an example of a Data Quality Rules Report, showing some of the different types of data quality rules we can report on.

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