Data Valuation

How Much Is Your Company’s Data Worth?

While determining the economic value of data is a prerequisite for effectively managing data, it is at the same time a very challenging task. A central question is: What constitutes a data asset in the first place? Another question is: Are there any suitable valuation methods available?

The economic valuation of data (also sometimes called “infonomics”) has been in the focus of the CC CDQ’s research activities for some years now. At the last CC CDQ workshop in June 2017, Mr. Andreas Zechmann presented key findings of this research and explained how two well-established approaches of determining the value of intangible assets can be applied in the field of data.

When Andreas Zechmann started his work, he built on the results of the work conducted by former CC CDQ researcher Dr. Ehsan Baghi. In doing so, he first had to clarify what a data asset actually is and what practitioners expect from a data valuation method to deliver. Following reviews of academic literature and expert interviews, he then investigated how proven concepts from intellectual capital measurement can be transfered to the data domain.

In two subsequent research projects, Andreas refined these concepts, which resulted in two alternative approaches for the financial valuation of data: a cost approach (based on Dr. Ehsan Baghi’s work) for valuation of data reproduction cost and an income approach for valuation of future benefits resulting from the use of data assets.

Approach

Cost approach: The value of a data object equals the cost incurred for reproducing an exact copy of that object.

Income approach: The value of a data object equals the total economic benefit created by that data object in the future.

Key Takeaways
  • Considers data quality as a value determinant
  • Helps manage data quality effectively
  • Helps raise awareness of data as an asset («price tag for data») and quantify a «minimum data value»
  • Is easy to apply and therefore cost-efficient
  • Allows internal benchmarking
  • Does not take the value created by data into account
  • Presupposes high maturity of DQ-tools
  • Provides no incentive for data managers to save data production cost
  • Considers data quality as a value determinant
  • Analyzes one specific process and how data is used therein
  • Takes the future value created by data into account (e. g. cost savings, risk reduction, increase in revenue)
  • Is very flexible and scalable
  • Reveals process inefficiencies
  • Is highly subjective in terms of being a company-specific and/or process-specific value indicator
  • Is dependent on process knowledge and/or expert knowledge and therefore not very cost-efficient

Bottom line in terms of being applicable to data valuation

Delivers valid results, but does not consider the value created by data

Takes the value created by data into account, but is highly subjective and quite costly

Both methods have been applied in practical data valuation use cases with four European companies participating in two research projects, each with a duration of 18 months, and over 30 project workshops in total. Alongside with collecting a lot of valuable experience in applying the two methods, main results of these projects were best practices, tools and templates for practical application of the methods, and a detailed and comprehensive documentation of the findings.

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.

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