Should we see data as a commodity like oil?

In 2016, Intel Chief Executive Brian Krzanich stated that “data is the new oil” because of its growing value, but is this merely hype or the new reality?

Every day we leave a trail of data behind us, made up of items such as Wi-Fi pings from mobile devices, web and app usage, loyalty and travel card swipes and social media etc. Each of these trails provides a picture of an individual’s activities, preferences and thoughts, which can then be analysed and used by organisations, to improve and develop services and deliver more targeted marketing and sales. Transport for London (TfL) uses Wi-Fi tracking in some of its stations to see how travellers move around, allowing it to identify ways to reduce congestion. While retailers like Amazon, track the purchases you make and the items you look at on their platform, so they can make further suggestions based on the data gained on previous visits.

With data offering greater insights and improved outcomes, it is not surprising that people have begun to think about equating a value to it. In May, The Economist stated that the data Tesla gathers from its self-driving cars is the reason that it is now worth more than General Motors (GM). Despite the fact it sold only 25,000 cars in the first quarter 2017, compared to GM’s 2.3m ¹.

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.

Unlike some assets, data’s value does not necessarily increase with age. In today’s fast paced world, organisations need current data to base decisions on. If I want to understand today’s air passenger, I would not look to data from the 1960s or even the 2000s because the flying experience has changed so much in that time. However, if I wish to consider how flying has changed over a period of time, then there is a value to the older data, as it would allow me to look at trends and patterns and learn from them.

The format of the data also causes problems. Currently there is no international standard for the way data must be labelled, which means it is hard for organisations to use other people’s data. You could have the most insightful data in the world, but if no one else can use it, its value is greatly diminished. Artificial Intelligence (AI), which learns from data patterns and trends, also finds it much harder to deal with data in multiple formats. An issue that Matt Bencke, CEO of Mighty AI, believes needs to be addressed, before data can “become the catalyst that differentiates and accelerates your AI" ². Moreover, as AI’s use continues to grow, it is not hard to imagine how the value of data could become defined in part by its ability to be assimilated by AI, regardless of what it could tell us.

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.

About the author

Andrew Pryke

Managing Director - BAM Design

Andrew leads BAM Design’s architectural, structural and interior design departments, working closely with our construction and FM divisions.

He also leads the adoption, development and integration of Building Information Modelling (BIM) adoption at BAM, to increase efficiencies at all stages of design, construction and FM.

Andrew joined BAM in 2012, following 25 years as director and project lead at James Stirling Michael Wilford & Associates and John McAslan and Partners. He has worked on projects such as The Lowry, Manchester, No 1 Poultry, London and The Royal Academy of Music, London.

A triathlete in his spare time, Andrew also applies his competitive spirit to working out the best design solutions for clients, integrating sustainable design, lean construction and full FF&E (furniture, fixtures and equipment) solutions.

“We are developing BIM as a tool for greater collaboration between design, construction and facilities management, to deliver better buildings that are easier to manage and maintain, and perform better for their users over their entire lifecycle.”

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What does this mean for the construction sector?

About the author

Andrew Pryke