Data quality, Data governance

Data quality, Data governance

Control framework, which guarantees the credibility of your company’s data assets.

Control framework, which guarantees the credibility of your company’s data assets.

Those companies that already actively use BI solutions often neglect their governance area. Although in reality it would be extremely important to know from where and how the data come in, in most cases these aspects are unknown.

There are several reasons behind this oversight:

  • People don’t like to document these processes.
  • A regular update of the documentation does not appear to be useful in the short term.
  • The level of BI maturity in the company doesn’t consider the data as a valuable corporate asset.
  • There are no responsible persons assigned to data sets nor to reports.
  • The knowledge of data flow, data structure and reports are known, but the knowledge is often separated into information silos. Cooperation and common interpretation are not possible without sharing this knowledge in the company.
  • There is no suitable tool for collaboration that helps to keep corporate data assets updated and available for the target audience.
  • The required information includes only the IT side of metadata (e.g. database field names), but collection, calculation, business logic and the frequency are unknown for users.
  • Everyone trusts their own reports and doesn’t feel the need to have deeper knowledge about the available datasets in the company.
  • There are no integrated control points built into data flow stages, nor in the reports, e.g. if there is current sales information of an already withdrawn product (a report mistake), we can only hope not to miss this fault line.

For KPMG specialists these are well-known issues, which require a global view and comprehensive handling towards their mitigation.

Corporate assets and proper knowledge of them should be extremely important.

How can a decision maker trust in the data when governance components are unknown or there is no responsible person who can guarantee their reliability and quality?

How can new reports or indicators be created without knowing what the base we can build on is and how?

The answer is well-maintained governance which guarantees data quality as well.

What are the requirements for proper data governance?

  • Assigned data stewards, data-oriented approach in the organization, data access controls
  • Data and metadata dictionaries which create a bridge between IT and the business side
  • Control points, fixed-applied rules, required minimum format and content-related requirements
  • Tailored modification and approval processes
  • Information sharing solutions within the organization

Connect with us


Want to do business with KPMG?


loading image Request for proposal