Data Analysis Analytics Comparison Information Concept

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Data Governance

Achieve maximum user acceptance by extremely high data quality.

We prefer the following approach:

  1. For whom are the data? (sales team, accounting, planning etc.)
  2. What are the 80% requirements that have to be fulfilled quickly?
  3. Which data are on hand, which have to be added?
  4. Are the existing master data enough for the target group or are there parameters that have to be added?
  5. Are the transaction data enough for the target group, which parameters have to be added?
  6. Which transactions must not be adopted for this target group?
  7. How can the transactions be loaded intelligently?
  8. How are comparisons possible and with which other target groups?
  9. Keep the data transparent, everything must be explicable for the target group.
  10. Quality checks that are adaptive and proactively refer to problems, e.g. a kind of ‘machine learning’

An example:

When editing credit notes the ERP system indicates the amount of the article that has been credited – irrespective of the fact if the article is returned or only a value-related credit note is granted.

From Sales’ point of view the amount has to be zeroized, if it is a just value-related credit note, because otherwise the amount of the sold articles is no longer right.

From Controlling’s point of view the amount has to be kept as it is because it has to be found out for which articles the most value-related credit notes have been granted.

We give you what you want by providing what you need!

As experts with lots of experience we have a very keen sense of what has to be recorded and how.

Care for more examples of tricky data problems?

Practical examples showing how difficult it can be to interpret data and indicating most of all that data always have to be considered, and perhaps modified, with reference to the target group.

Please click here to see the examples

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