Phases of a master data project

1. Free data audit

  • You provide a representative extract of your master data
  • We evaluate the data quality according to defined quality criteria and create a data profile for you
  • The data profile provides you with analyses of the properties of your master data: Completeness, uniformity, duplicates (selection), enrichment potential, initial evaluation of the product category key (if available)
  • You decide on further cooperation

Request your free data audit

2. Data analysis

  • Quality evaluation of all master data based on statistical analyses and quality criteria (e.g., completeness, timeliness, accuracy, uniformity)
  • Creation of a data cockpit for result visualization
  • Development of an action plan for cleanup, classification, and enrichment

3. Data cleansing

  • Identification of inaccurate data records
  • Similarity check on data duplicates
  • Conversion of the original customer data into a normalized intermediate format (data normalization)
  • Consolidation and completion of incomplete data sets (data fusion)

4. Data classification

  • Taxonomy on request (eCl@ss, CPV, UNSPSC or individual)
  • Semi-automated classification approach - statistical and algorithm-based analysis methods
  • Manual plausibility check and feedback-based classification process

5. Data enrichment

  • Extraction of existing characteristics and attributes
  • Targeted supplement of missing data sets
  • Harmonization of enriched and extracted data sets
  • Creation of the final import file


Florian Boehme

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