Over the years, we imagine plenty of tech innovations have been hailed as ‘the next frontier in digital transformation’, but that hasn’t discouraged Forbes from using precisely those words to describe the process of data commercialisation. This, of course, is the popular new way for businesses to increase their revenues . . . often by several millions. The likes of Facebook, Amazon, and Google have been doing this for years, but now we’re seeing companies from other industries follow in the tech giants’ plus-size footsteps.
During a research project earlier this year, Wilbury Stratton spoke to senior data commercialisation sources across a number of industries, including automotives, technology, financial services, logistics, pharmaceutical and payments. Our conversations demonstrated that data commercialisation is a topic of considerable interest in boardrooms up and down the country. Interestingly, though, we were unable to identify any one sector which is demonstrably leading the way. While there are isolated incidences of companies undertaking progressive data commercialisation work, plenty of other companies are still doing far more talking than doing.
Why is this, when so many of their competitors have amply showcased the profits to be achieved?
The short answer is that data commercialisation isn’t simply something you start doing overnight. In the fashionable parlance, it needs to be ‘set up for success’. It’s this preparatory work which we would argue is getting in the way of many companies’ best intentions.
Principally, there are three things a company needs to get right before the undisputed advantages of data commercialisation become achievable:
Like all fine things, data commercialisation costs money. You need to speculate to accumulate. The exorbitant price attached to some of the relevant technology – and we’ll come to that specifically in a moment – is enough to give some CEOs pause for thought. But apart from money, there is also a necessary investment of time. Several stakeholders need to agree upon the value and purpose of data commercialisation. The challenge therefore is sometimes less about winning additional budget as winning hearts and minds.
The three major technological developments expected to drive significant growth in data commercialisation are (i) the Internet of Things (IoT); (ii) blockchain and smart contracts; and (iii) machine learning and artificial intelligence (AI). While each of these has already changed how businesses manage their data sets, the smartest company strategists will consider using a combination of the three. But what precise combination? And what specific branded tools? And what if your company doesn’t possess that kind of strategic thinker or technology expert? You could hire in the know-how but if you’re not quite sure what you’re doing, how can you safely judge when someone else does know what they’re doing? Which leads us to . . .
Finding the right talent is crucial to making data commercialisation work. The obvious place to look is in the big tech firms but it’s usually tough to lure the best people out of these organisations. And indeed, their data teams are so big that it’s not always immediately obvious who the best people are. Even assuming you’ve identified your preferred hire, how do you persuade them to join your business? For a start, you’ll need to have addressed our first two points, i.e. you’ve got the budget to invest in the necessary technology and you’ve won the buy-in of all senior stakeholders. But you’ll also need to pay attractive sums of money, probably well over £200k base salary for the top performers.
In summary, for those companies prepared to ‘go big’ on data commercialisation, the investment of time, money, process and technology is considerable. No wonder then that some are reluctant to press ahead for the time being. Perhaps for those companies the best course of action is to start creating the right culture, i.e. building a strong talent pool of (albeit not terribly senior) data experts and beginning discussions with senior management about the need to move towards a more formal data commercialisation approach. The ultimate prize is not in doubt, the question is how to go about winning it.