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Data Quality Audit PDF Print E-mail

 In large enterprises, data is used and published by many systems that are not directly responsible for data quality and transformation but who rely on the integrity of the data to support decision making or operations.

 Data quality management is a basic requirement to ensure that there is confidence in the referential data that comes along with creating networks of interfaced systems. Following are some key requirements must be addressed to manage data quality effectively
A best practice is to have an independent data quality management team that performs data quality audits on ongoing basis. Some time project teams are not often empowered, equipped or funded to address and manage data quality challenges holistically. However, due to their closeness to the data they can play a major role in bringing up data quality related issues and thus creating awareness.
 
Once issues and the “root cause” have been identified, the data quality teams work closely with business units to access, identify and fix data problems (often times, this has a manual component). Data quality teams should work closely with IT teams to identify those processes which could be automated and define tests and validations that can be applied to catch erroneous data at the source, during data integration or transmission. Data quality teams address day-to-day data quality issues related to the specific nature of reference data. In case your organization has multiple master data management solutions hosting different types of reference data, you may need a cross functional data governance strategy that make sure best practices are followed by distributed data teams. A cross-functional governance board tends to focus on areas including:
  • Critical data production processes and enterprise integration points
  • Leverage how information and technology across the enterprise can maximize data production, efficiency and quality
  • Define data quality guidelines for individual data sets and compare with service level agreements
  • Benchmarking, policies and standards
CreditDimesions offers data quality teams for doing one time or planned audit of your reference data sets. We use our subject matter expertise and tools to indentify gaps and data errors. Data error could be due to lack of coverage in your database, inaccurate information, inconsistent information or formatting
 

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