Data Quality Analytics

Inconsistent, incomplete or incorrect data are among the most common problems. Errors in records continue and multiply – all the way to reporting to management. Data Quality Analytics handles the cleanup, enrichment of additional information, or the detection of duplicates or their complex consolidation. Data Quality Analytics creates the basic prerequisite for optimized processes, processes and applications in your organization: consistent, so reliable in all directions data. Whether in the evaluation of order data, the preparation of negotiations or consolidation for reporting purposes – with DQA you trust your data. Smart heuristics also identify excessive or unauthorized payments to suppliers or customers. In addition, the referencing of various product classifications (including eCl@ss, UNSPSC) is a central task. Data Quality Analytics includes all the components needed to extract, cleanse, reconcile, consolidate and re-import data from ERP systems and various source files, data from social media or commodity exchanges into a target system.

  • Data Fusion

    Merging of separate but related data from all relevant data sources

  • Master Data Cleanup

    Recognition of multiple material / customer / vendor master data

  • Data Error Detection

    Identification of vertical and horizontal inconsistencies for adjusted reporting

  • Billing Errors

    Identification of double & false payments

  • Cause Analysis

    Analysis of the causes of data quality problems (e.g. misconfigured ERP systems)

  • Classification and Goods Group Systems

    Transfer of master data to an industry-independent classification (e.g. eCl@ss)