Where oil and data do meet

As Bernard Marr suggests “Data is the new oil!” It is a claim you will have heard many times. Marr goes on to suggest that “It’s easy to draw parallels due to the way information (data) is used to power much of the transformative technology we see today – artificial intelligence, automation and advanced, predictive analytics.

Marr states that “in many ways, it’s also lazy and inaccurate – and while it’s handy as a marketing model (because it gets across the fact that data is a valuable commodity with many different uses across many applications) it’s also potentially problematic”.

Many impressive subject matter experts have commented on the similarities between data today and oil in yesteryear. John Thornhill, the Financial Times's Innovation Editor, used the example of Alaska, to argue that data companies should pay a universal basic income. An idea that has become highly fashionable in policy circles.

However, there are other properties that cause Marr’s comparison to break down, on more detailed inspection. For example, while oil is a finite resource, data is effectively infinitely durable and reusable. This means that treating data like oil – hoarding it and storing it in silos, has little benefit and reduces its usefulness. Nevertheless, due to the concept that data is like oil, due to its scarcity, this is often what is done with it.

What is surprising is how the debate has escalated to whether data is like oil or whether it isn’t. One article I read recently suggested that the key difference is that the data industry is much faster to evolve than the oil industry. Surely, we have been wrestling with ways to move data around for much-much longer?

Clive Humby, the British mathematician who established Tesco’s loyalty program, who has been credited with that observation, highlighted the fact that although inherently valuable, data needs processing, just as oil needs to be refined, before its true value can be unlocked. Perhaps Humby’s observations were based on operational principals and not necessarily as a comparison of the business models of the sectors, oil versus data, or whether the oil barons were more innovative than today’s media barons.

Taking this tack, and integrating the principles of managing data and oil, there are a lot of similarities including, collection and safe storage, processing/refining, enrichment and high-quality refined products for market sectors.

Data, like oil, must be managed very carefully, so security is extremely important. Even though there will always be fundamental differences between oil and data, there are a lot of good operational practices that can be transferred. What is also very interesting is where data meets oil, the mobility sector.

What is clear is that moving people and things around requires energy. Understanding the purchasing needs for the travelling consumer (at work or on leave), or the distribution of products is going to be fascinating. Mobile apps today help to manage the optimum routes for us to take, whether it is trains, planes or automobiles.

The demand for mobility is increasing dramatically. Clever A.I. is helping us to choose the right routes or means of transport. It is not too much of a stretch to suggest that in the future our mobile apps might also show the carbon footprint associated with those choices. A.I. will and is already helping to optimise how the end consumer (B2C and B2B) undertakes and pays for a journey or for a delivery

Mobility optimization will need a ‘cradle to grave’ analysis, so data on the optimal transport method with the best energy sources (e.g. oil) will be critical. In the B2C world, free delivery services at point of purchase must be paid for, either by the supplier (the retailer) or by the consumer.

Clearly in the B2B sector, understanding delivery patterns in payment data will play a greater role in the future, by helping to enriching consumption information. This, in turn, will help to understand the impact of long and short delivery tasks, and the type of fuel sources that are being used.

Not only is A.I. an exciting sector for understanding human behaviours, it is also extremely interesting to see how mobility will evolve with the growth of populations around the world.  What is for sure is that going forward, we will have greater information to help us plan more accurately and with greater precision than in the past.