As firms scale up the use of information in business using Big Data analytics there is an increasing interest in measuring the value of the data. Not just the ROI of the projects that use it, but the asset value of the data itself.
Pete Swabey recently wrote an article in Information Age about the connection between the value of a firm’s data assets and the market value of that firm. He highlighted how most firms do not formally measure the value of their data assets so their data’s value is not included in their market value. Commonly, they do not treat their data assets appropriately, and even more worryingly according to his article, their insurers do not recognise the value of their data assets.
However, measuring the value of data is easier said than done because valuation is ‘in the eye of the beholder’, i.e. value is personal and individual. Value is also dynamic because it is affected by personal experience and events, and it depends on personal context, i.e. the user’s past history and future goals. So valuation of a resource depends on a fit between the resource and each individual user’s ever changing personal needs.
Value is not some frozen and unchanging characteristic, it is ‘value-in-use’ and the value of data depends what you use it for – which makes things even more complicated because you can use and reuse your data assets many times without wearing them out. (Maybe you only ‘wear out’ data when you tell someone something that they already know).
Swabey’s article describes a few useful ways to start to calculate the value of your information assets but measuring value of your data is not straight forward when there are so many unexplored uses for it.
A solution to this problem needs to start with an understanding of how your data could be used and the individuals that could use it. I.e. start by mapping out all your uses of your data (by staff role and function); then do the same for your current business partners (since you are in the same supply chain or ecosystem); then think about opening it up even more.
But if the value of data is only really apparent when it is used, and its uses are mostly unknown, then the best way to explore this problem is to ask potential users for ideas, i.e. to open source it.
Opening your data up to new users and infomediaries would let you access new ideas for using it. And the value (and risks) of each new idea could then be assessed. This way also brings in highly engaged customers and partners for services that are based on your data assets.