Data Strategy & Governance

In order to benefit visibly from the asset “data”, we at ADWEKO support you in setting up sustainable, company-wide data governance, including the implementation of data quality rules and reports as well as the customizing of metadata management tools.

UNIFORM DATA STRATEGY & GOVERNANCE FOR BANKS

Data is a key asset of any financial institution and the amount of data that needs to be processed is constantly increasing. This increases the importance ofa standardizedData governance. Clear guidelines and data standards, roles and responsibilities as well as reliable data quality management ensure that the right data is used by the right people at the right time and in the right form.

Such data quality management can only be ensured efficiently by means of appropriate IT support. The current data quality processes of many banks, on the other hand, focus on event-driven data quality audits. To benefit from data quality measurement in a meaningful and lasting way, the current process and system infrastructure should be fully validated and enhanced through data quality testing.

DATA & IT ARCHITECTURE

Introduction of DQ tools and support in the selection process, Setting up and/or improving automated data quality rules via the IT supply chain, setting up DQ processes.

GOVERNANCE & INFRASTRUCTURE

Introduction of MDM tools and support in the selection process, design of role target images, creation of data lineages, definition of data lineages, definition and maintenance of glossary entries, creation of delivery agreements.

DATA VISUALIZATION

Introduction of (DQ) reporting as well as support in the selection process (e.g. B. Tableau or PowerBI), Creation and Improvement of reports, technical conception, implementation and testing.

01

CONCEPTION AND CONSTRUCTION OF SOLUTIONS FOR YOUR DQ-MANAGEMENT

In addition to the far-reaching transformation of your bank management with SAP ADWEKO supports you in the successful design and development of future-proof, cost-efficient and integrated solutions for your data quality and metadata management:

  • Setting up automated data quality rules and DQ processes (e.g. evaluating, tracking and correcting DQ)
  • Setting up MDM processes, metadata management and use for effective data management
  • Creation and maintenance of data lineages, definition and maintenance of glossary entries and data catalogs
  • Introduction of DQ and MDM tools
  • Our partner: Synabi, Innoscale

02

SELECTION OF DQ SOLUTIONS IMPLEMENTED BY ADWEKO

  • Implementation of automated data quality comparisons to check consistency and completeness across the retail loan delivery route:
    • Automated front-to-end reconciliation (F2E) using RPA solutions for retail and wholesale loans.
    • Automated end-to-end reconciliation (E2E) to ensure consistency of data between dispositive banking systems
  • Modeling of a metadata model (MDM)
  • Establishment of data governance incl. Creation of guidelines, specifications and processes, the development of a new role model and the development of change and data culture measures
  • Introduction of DQ reports using Tableau or PowerBI
  • Monitoring of DQ measurement results
  • Design and delivery of data governance training
  • Our partners: Synabi, Innoscale,SAP

The world’s most valuable resource is no longer oil, but data.

The Economist

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TOBIAS SCHOLZ!