Do you want to find out how much the data in your company is worth?


Here are 8 tips what you should keep in mind for your data valuation project.

data valuation

1. Clearly specify which data objects are to be included in the valuation.

2. Use appropriate DQ-KPIs. Data quality is a key determinant of the financial value of data assets, since low quality decreases the data’s financial value and/or results in value impairments. It is therefore important to use a DQ KPI that is reliable and valid. If data valuation is based on the wrong DQ KPIs, valuation results will be implausible and doubtful.

3.Choose a data driven use case comprising clear causal relationships. The results of a data valuation project will be plausible and valid only if the use case comprises clear and unambiguous causal relationships (e.g. between the use of certain data and its financial implications). Furthermore, the use case should be characterized by a high level of data dependency (e.g. a data-driven process).

4. Clearly explain its purpose and what can be expected from it to all stakeholders. Data valuation is an interdisciplinary task, involving data managers and financial groups, all having very heterogeneous expectations. Therefore, explain very clearly the purpose of the intended valuation project and the benefits to be expected from it.

5.Find a powerful sponsor.Find a sponsor who really supports your project one-hundred percent and who is willing to use their good name and reputation to promote the project. The sponsor will be key to the success of your project, as she or he can help you raise awareness and increase acceptance among all stakeholders. 

6. Check. Check. Check.Do regular, effective checks during the valuation process to ensure the validity of your assumptions, the suitability of the methods and models you use, and the plausibility of the results you obtain.

7. Don’t get lost in too many details. Be as idealistic as necessary, and as pragmatic as possible. Going too much into details can be a show-stopper.

8. Be aware that data valuation is story-telling. Try to capture the use case in its entirety, as only a complete picture – from data assets to quality and financial value – makes an attractive story and increases credibility and plausibility of valuation results.
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