Data value

These days I am preparing a lecture on the value of information and data (seen as information) as a driver of change in companies. In it we talked about aspects such as the fact that data has become the center of attention of companies and that it is an asset for them, since it can bring a lot of money and improve the results; and since we can really see them as a strategic asset, we should treat them as such.

To do this, one of the first steps we must take is to have an inventory of them, to know the sources we have, what types of data live in them and the information we have; to do this, it is very convenient to have a working framework and a regulated governance and management with procedures such as the one proposed by DAMA.

On the other hand, as a good asset, we must quantify its value, because well-treated information, in the right hands, has a lot of value. It is said that data is the new oil of companies, since it will be what drives them and the most quoted companies will work with it; but as Laney Douglas, author of the book Infonomics, said in one of his lectures, data is much more than oil. Oil can only be used once, you can’t use it many times and still increase its value, you can’t duplicate it, while data can; data can be duplicated, improved, shared… and on top of that, it stains less.

But how much is each piece of data worth? We already commented in another post that it is very difficult to give a concrete monetary value to data, it depends on each company and each situation, and when we talk about value, we do not only have to refer to monetary value. For a candy factory, the wrapper has hardly any economic value, but it does have value for the business, because if we don’t have a wrapper, we can’t sell the candy.

Does this mean that we don’t have to try to obtain value for them? On the contrary, despite not appearing in the company’s official balance sheets and the subjectivity of other valuations, it is highly advisable to assign one or more values to each type of data, so that we know how much we must worry about them; in fact, there are even some companies that are starting to use them economically in internal balance sheets.

Now there are no standard valuation methods, so we must rely on methodologies that we develop ourselves or some available ones such as the one proposed by Gartner, making a distinction between fundamental and financial measures:

Foundational Measures:

Intrinsic Value of Information (IVI): The completeness, accuracy and correctness of the data are considered. A detailed analysis of the data is made at the pure data level, which makes it very complete, but at the same time, we have no context of the data itself, what we are going to use it for.

Business Value of Information (BVI): We look at how these data influence the business, how to apply them and how useful they are for the business. It is interesting to detect those data and information that are collected, but never used or little used (known as “dark data”), but unlike the previous valuation model, this is very subjective depending on the context; a piece of information may be useless in one context and vital in another.

Performance Value of Information (PVI): This refers to the possibility of improving the business through this information. In this model we will find measures that we can use within the financial sphere; we will need to rely on a control group to check its calculation.

Financial Measures:

Cost Value of Information (CVI): The cost that the loss (considered as loss of control, theft, damage…) of this data would have for the company is reviewed. It focuses a lot on the cost itself, but not on the value it can bring to the business; however, we can make a translation of the economic impact on the business.

Market Value of Information (MVI): This looks at what could be achieved economically by selling the data to other organizations. It is complex to calculate since the typical supply and demand curves do not apply excessively to the information market (it can be copied multiple times and once it is sold, the right to use it is sold, not the property, so it can be sold again) and difficult to fit into a specific market, which is still incipient. Once the valuation parameters are set and the data is valued, we can use this model internally to highlight the importance of this data for treatment and investment, since the market economic factor is easy to understand by the different areas.

Economic Value of Information (EVI): It is about obtaining the net value of the data: the future benefit minus the acquisition and maintenance costs. It is very useful to have a clearer value of the information, but at the same time, since it is based on the forecast, we will be relying on assumptions.

To have a more complete view of the value of a piece of information, it is advisable to combine several valuation methods, let’s see a couple of examples:

Information with a low economic value (EVI), but with the potential to increase it and which in turn provides a high business value (BVI), would be an indicator that it is good information to invest in and improve, since it is good for the business and can bring us economic benefits in the future.

Information with a low cost of loss and at the same time very complete and that is valuable for a business (a low CVI and at the same time with a high IVI and BVI) can be an indicator that we are dealing with interesting data to sell to the market. And here we must consider that it is possible that for our business it may not have much value (low BVI) but for other businesses it may have it, which would be the niche market for this information.

As we can see, when we have the quantified value of the information for the organization, observed from different points of view, we have tools to better decide what data to invest in and how to get the best return on that investment.