However, it is not a simple as it seems. Unlike other commodities, it is very hard to put value on data. It is true that not all data is created equal. I could collect lots of data, but unless it can be used to tell me/others something, it has little to no value at all. Data’s value is also time bound and dependent on situational requirements. As an example, the data on my bus app is very valuable when I am waiting for a 44 in the pouring rain but useless when I am sat at home and want to know what’s on TV.
Similarly data could be of a low value on its own but when it is added to other data sets, create a lot of value. This is because it is rare that one type of data will provide organisations with a clear understanding of why things happen, because in most cases multiple factors shape behaviours, situations and outcomes. A supermarket chain may sell out of a product one weekend but looking at sales figures alone won’t tell them why this was. Only by considering other factors such as promotions, the weather, national holidays, delivery lorry breakdowns etc. can they gain a true picture of what has occurred and why, to enable them to apply any lessons learnt the next time around.
Despite the issues, I do think that there will be scenarios where some types of data could command a higher price than others. Especially those data sets that provide good insights into human behaviours, which are difficult to capture in a research setting because of the sample sizes required to obtain a true picture of what is occurring. Data that helps retailers understand why consumers buy products, captures social interactions or provides us with better information as to how people interact with spaces and the wider built environment would fall into this category. As would data that tracks behaviours over time, allowing patterns and trends to be established, which is why Tesla’s driver data is seen as so valuable. On the flip side, there is a rising tide of people who don’t like their personal data being collected and used for sales and other similar purposes. As a result, there could be a point in time where holders of this type of data would be unable to use it due to public sentiment or legislation, making it worthless.
Only time will tell whether data will become a valuable commodity like oil, but it does have a lot of potential value. However, until we can establish a clear way of valuing the different data types globally, it could prove to be rather a high risk investment at this stage. There is also the danger that it could become a political pawn like oil, with companies and countries using it as a tool for good and bad.
What does this mean for the construction sector?
As an industry we are already collecting data via building information modelling and smart technologies such as sensors, which is used to help us design, construct and operate assets in a better way. However, in the future this could have even more value, as companies gain data from the multiple projects they have undertaken, which can be analysed to identify trends and patterns. These insights will enable us to foster an industry-wide culture of continuous improvement and allow us to predict operational outcomes with greater certainty, which in turn will result in more efficient buildings and better outcomes for stakeholders.