When developing your organization’s data monetization strategies, the first ideas that come to mind are probably not the best. For example, you might assume that all you need to do before you can start selling your customer data to external organizations is to obfuscate or remove a few sensitive fields – think again. Doing only that could potentially risk your organization’s very existence.
The best data monetization strategies begin by applying an organization’s intellectual capital to raw data elements to create truly new and unique insights. That is the fastest path to creating ‘data products’ that a broader market will be motivated to purchase.
Naturally, data monetization strategies are most effective when an organization has the right people analyzing the data. Your leadership team might reasonably think that the best person to tap for this role would be the Chief Information Officer, or possibly the Chief Marketing Officer. Such individuals might indeed be fine for the task, but in my opinion, you should be looking beyond titles to find individuals with the innate sensibilities and personal preferences that make them suited to the practice.
To be specific, I recommend finding those rare individuals with analytic skills in both qualitative and quantitative areas. In a sense, they need to be ‘hardwired’ for this type of insight and practice. In my experience, it’s much more important to find analysts with such insights instead of requiring that they had this or that previous job title, or even a particular degree. As a matter of fact, when I’m trying to fill data scientist positions, I’m far more interested in seeing how the individual rates on the Keirsey Temperament Sorter, a personality instrument similar to the Myers-Briggs, in conjunction with their quantitative reasoning. It’s not as well known as the Myers-Briggs, but I find it gives me more useful insights into the preferences, abilities and ways in which a candidate prefers to think that I value most in a data scientist.
To drill down a little more, I tend to look for rational, curious and analytical people. They might not even need to be familiar with my particular industry. My reasoning is that by the time they’ve sliced and diced my data to derive truly new inferences from it, it’s likely that it will be relevant to (and valued by) organizations in other industries.
To go even further on this point, it might make strategic sense to focus on developing data products with the specific aim of selling them to companies with whom you could never directly compete. By the same token, your new data should feature insights that are based on your customer information, but bear little or no resemblance to the original data.
Ideally, your data analytics team members should be able to learn rapidly and also be familiar with the latest data analytics products. A few years ago, that would have probably been Hadoop, but in today’s data analytics world, technologies like Spark and Storm have begun to attract more market attention.
The better decisions you make about monetizing data, the greater your likelihood of maximizing the revenue it will generate. In addition, by making sure that you produce and sell only data that is truly yours, you can minimize the risk of angering or alienating customers, shareholders, regulators and other interested parties.
The world of data analytics is changing – not only in the types of data monetization strategies that organizations are pursuing but also in the types of technology, processes and people involved. If you think that data monetization is worth doing (and there are plenty of reasons to think so), it’s also worth doing right. For those and other reasons, it pays to start by hiring the right people for the task.