‘Data is the new oil’ is an attempt to suggest how valuable data is. It’s an analogy that works when we consider data isn’t valuable in its raw form, it needs to be processed to become something more valuable. Oil is valuable because so much can be done with it, just like data. But there the analogy breaks. Oil is a limited resource, and once processed it cannot be reversed. Data is constantly and continuously being generated, and using it does not use it up. The same data can be reused repeatedly in different ways.
In many ways data is unique and non-analogous. Data is both what is processed by computers and what they use to run the processing. Nothing else in the world uses fundamentally the same thing to do the processing and be processed. Data is an abstraction of the real world, which means all data is interrelated with every other piece of data. Nothing else in the world is as connectable.
Conceptually, all data is connected. Someone buys a desk.The measurements of the desk are on a spec sheet used by the manufacturer, along with the safety standards the desk meets and what materials it’s made of. The manufacturer holds data on the employees that built the desk, including how much they were paid, when, etc. That employee’s bank also has data on how much and when they were paid, but also what they spent their money on. The retailer they spent their money with knows some of what they did with their money, including that they bought a desk. But because all these data sets are independent no one person or system sees how they connect.
As more data becomes more interconnected its usefulness increases exponentially. But in order to achieve the interconnectedness of datasets and make them useful, collection, storage and processing have to be decoupled from each other. When competitive advantage comes from the collection, storage and processing of smaller specific datasets that organisations use to draw insights only relevant to themselves, interconnection is prevented. If data collected from numerous sources is stored in a way that is equally available to everyone then competitive advantage can only come from processing. Those organisations that have the capabilities to utilise insights from the analysis of huge aggregated datasets win out, but require an intermediary to store the data and prevent monopolisation.
Data Trusts work like a bank but for data rather than money. Just as no organisation keeps the money it makes, nor would they keep the data they collect. Industry standards would standardise data collection and laws would make it illegal for organisations to store data. Data would be held by these Data Trusts and made available only to those that contribute their data. Anonymised data is accessible in real time for processing by organisations to draw insights that enable them to make decisions that take account of an unimaginably huge number of data points.
Data Trusts would specialise in particular types of data; retail, health, manufacturing, etc., creating a further layer of anonymisation and aggregation for organisations wishing to correlate datasets. Interesting new commercial models would develop around the cost of accessing data to take account of increasing returns mechanisms and the decay of relevance.