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Data Quality

The increasing compliance regulations such as Perdarr (BCBS239), Basel, AnaCredit, IFRS9, Solvency has an enormous impact on data quality principles within organizations. Most of the organizations are still lacking behind of a good data quality process and framework. Next to the compliance regulations, a huge advantage can be retrieved when data qualities items are reduced or improved. This can help to decrease the cost of operations while increasing the level of services for business operations and for decision making. We have grouped the data quality items into six categories and with our Data Quality Process we are able to master these items individually.



The five minimum requirements for a successful implementation of a data quality process and framework are:
  Commitment to the necessary mindset and paradigm changes.
  Frequent communication with involved people in the data value chain.
  Collaboration between the involved teams or departments.
  People focused on the different processes that take place in the value chain.
  A robust process and support tools for the management of the quality.



We have created a Data Quality Assessment Model for companies to determine their level of compliance and the maturity of their data quality process. This DQAM helps you for benchmarking purposes, to identify the gaps with your current data quality and gives insights of the internal improvement areas