Data Valuation – What is my data worth?

The increasing role of data concerning the digital transformation and the development of digital business models, will force companies to think about the value of their data as assets. Nowadays however, only the minority of companies can already asses the value of its data satisfyingly. The Fraunhofer Institute of Software and Systems Engineering (ISST) develops methodical approaches for data assessment with a strong scientific background and the required know-how. It supports its partners by turning these approaches into practical solutions, transforming the company’s existing implicit valuation processes into explicit ones and adding more transparency to the management of data resources by implementing a structured framework of data governance on a companywide level, in which all processes are supported by individual software systems.

In June 2016, Microsoft Corp. published the acquisition of the business focused social network LinkedIn by investing 26.2 billion $. LinkedIn owns, comparable to other companies of the digital economy, little physical resources and asset, leading to the assumption that its value can be solely attributed to the networks culminated user data and the data’s interconnectedness. With LinkedIn’s user base of 433 million registered users, the calculations to establish a price for a single user data set at 60.50 $ is fairly simple arithmetic.

Is this price for a user dataset justified? What are the decisive criteria for the assessment of a concrete price for single datasets? What could methods, to determine a transparent price for data, look like?

So far, there is no such thing as a satisfying answer to any of these questions. Data management is a key subject of research, enhanced by the digital transformation of our time and increasing emphasis on digital business models. The growing relevance of this key subject increases even further, when considering the growth rates of data generation and amounts of data used in business intelligence and data analytics projects. According to an article in Forbes Magazine, the amount of data used for these purposes until the end of 2025 will lie within 180 Zetabytes (180 trillion Gigabyte).

For any company’s bottom line, no matter what sector, the role data plays in it becomes clear by taking a close look at the revenue growth for data-driven business models at the Fortune 500, which is twice the growth of non-data-driven ones. Companies will prospectively buy and sell data assents as a separate product in its own right.

The lack of a standardized and practice oriented method of determining the (monetary) value of data, is what makes this topic highly relevant key subject of research. To better the status quo and to keep their competitive capacity, companies need to start developing such methods and invest in architectures, structures and processes aiding their data management.

More motivation for starting monetizing available companies’ data assets, comes from one of the worlds biggest consulting groups: PwC. They propose a revenue of 300 billion $ for 2018 by trading data assets for the finance sector, only.

First approaches for the valuation of data assets have already been identified. Based on the methods for the valuation of material goods, these can be divided in three different groups:

  • Cost of production/purchase: The value of data is determined by the cost for producing or purchasing it.
  • Use value: The value of data is determined by its contribution to a company’s business processes and overall performance (increase in customer satisfaction, reduced stock keeping, or more efficient deployment of sales staff in business models including direct sales, for example).
  • Market value: The value of data is determined by its price when sold in the market.

If you would like to know the value of your company’s data, contact us! Based upon scientific methods and our own, extensive expertise, we carefully develop valuation approaches for you. We will support you in transforming existing implicit valuation processes into explicit ones as well as adding more transparency to the management of data resources by implementing a structured framework of data governance on a companywide level, in which all processes are supported by individual software systems.