We experience the rise of the knowledge economy and an emerging importance of big data technologies for value creation. Companies start to understand the economic value of creating proper data collections feeding their own products and processes with a unique competitive edge. They realize that the compilation of proprietary, large-scale data collections will become very important investment projects in future. They will learn to consider data collections as a new kind of asset, enabling them to extract new value through new and better products and services.
Assets building are investment projects. Consequently the compilation of competitive data collections are ongoing investment projects. For any investment class methods for the determination of the return on investment and the inherent risks are essential. But how the potential value created through data collections can be estimated? It is data valuation. It is important for the up-front decisions on the profitability of these investment projects, as well for decisions on complementary data purchases or the legitimation of maintainance costs. It’s worth to have a deeper look on the topic of data valuation.
Three types of data valuation
In a recent blog post at TechCrunch Pauline Glikman and Nicolas Glady name three different perspectives on data valuation: data valuation by the shareholder, data valuation by the company itself, and data valuation by the individual user.
Data valuation by the shareholders is quite simple. How much money are investors willing to pay in merger and acquisitions. If $30 is the average price for one Instagram user, we can assume that most from this value is driven by the purchased data about the relationship with this customer. Transaction prices for data-driven companies are proxies for price tags on data assets.
Data valuation by the company is usually done by customer lifetime valuations, where the net present value for each user other data assets is calculated. This approach is much more conservative, because it does only consider the real value which can be generated by the firm itself. The data valuation by the shareholders does include much more goodwill and strategic willingness to pay.
Data valuation by the individual user raises the question how much we as consumers are willing to pay for our data. Which price tag to we accept to secure our own data from re-use through the service provider? In the most cases this money is less compared to the value the service provider can extract by other means. In consequence, fee-based services for privacy-aware users are less profitable than free, but data-greedy services.
Are we addicted from personal data?
As baseline of their argumentation Glikman and Glady recognise personal data as the current main value driver in the worldwide data market. At the same time they recognize, that data protection regulations like in Germany are significantly affecting this kind of value extraction. While looking at the new EU data protection regulations it might be a good bet that the German attitude will partly become more mainstream on a larger scale. This will affect any data valuations which are addicted by personal data. Consequently, it is a good idea to work harder on new ideas for value extraction from non-personal data.
Not Raw Data Collections, but Data Processing is the key to higher data valuations
However the regulations on personal data will look like in future, corporate data collections will become a strategic asset in the knowledge economy. Glikman and Glady summarize the competitive edge: “For the shareholder, data embodies a financial potential. For the company itself, data can be used to optimize the way it does business: acquisition, retention, targeting, pricing, etc.” While they understand that the valuation of data largely depend on how it is used and in which context, they emphasise a basic law in data valuation:
“raw data has a very low value, the more it is enriched, analyzed and leveraged for specialized uses, the more its value increases.”
The true value generating exercise is not the raw data collection. But the true value comes from data processing activities which transform the raw data into a scare and unique input factor for value creation purposes. And if we achieve to become less addicted from personal data in future, we will much easier unlock new fields for data-driven business models. Which, in the end, will boost data valuations.