Addressing growth of business needs in the area of Data Governance, in the first instance addressing the challenges posed to banks by Recommendation D of the Polish Financial Supervision Authority, we offer comprehensive consulting services and implementations of data management solution, covering data architecture and data quality fields.

Data architecture management

Organizations which intend to control operational risks involved in data processing should be aware of data processed as part of their activities. They should know sources of the data (i.e. whether they are internal or external), and know which business units, processes and systems are involved in such processing. To that end, they should take inventory of data processed and regularly review inventory findings for agreement with the facts. The scope and the level of detail of such inventory should be commensurate with the scale of operations and the significance of each data group to the organization.

Based on our experience with such projects, in the first instance in the banking sector, we can perform relevant analyses, covering business processes and data processing locations, segregation of processing roles and duties, and existing (or missing) procedures involved in data capture, processing and distribution.

Having studied analysis findings, we may implement dedicated IT tools to support data architecture management using a central knowledge repository.

Data quality management (data governance)

The next natural step following an inventory of resources, processes, systems and key data processing roles is the implementation of data quality controls. We offer you comprehensive analyses of your data sets, including the development of current data quality reports, supplemented with the development of error identification procedures, data cleansing (correction) rules, and regular quality control objectives.

We offer you professional tools to support the on-going data quality management process, which make it possible to formalize and specify consistent data quality management rules:

  • Data owners;
  • Qualitative criteria;
  • Verification frequency;

‚óŹ Quality tests.


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