If you’re thinking about data monetization strategies, you’ve hopefully moved past the idea that you can simply obfuscate some sensitive parts of your customer data and then sell it to external organizations. In my view this is a terrible idea — potentially risking the very existence of your organization.
Instead, the best data monetization strategies start with the methodical aggregation of existing data to remove any personally identifiable information (PII). Only then can you begin to perform the data analytics that will produce insights that are truly yours — and that you could sell without the risk of angering customers or clients or creating a public relations nightmare.
Looking beyond titles
I humbly suggest that to really have effective data monetization strategies, you probably need to take a new look at the people you have analyzing your data. A CEO might reasonably think that the best person to talk with about this topic would be the Chief Information Officer, or possibly the Chief Marketing Officer. Those might be good individuals to consult with, but I believe that what you should really be looking for is not titles, but rather individuals’ innate sensibilities and personal preferences.
Specifically, I suggest that you need to find those rare individuals who are skilled at analyzing both qualitative and quantitative data. They need to be, in a sense, hardwired for this type of insight and practice. In my view, it’s much more important to find analysts with these types of insight than requiring a previous job title, or even a particular degree. In fact, when I look for data analysts, my interest is in seeing where they end up on the Keirsey Temperament Sorter. Keirsey is a personality instrument similar to the Myers-Briggs, but one which I find generates more useful insights into the types of abilities that I value in a data analyst.
In particular, I look for people who are rational, curious, and analytical. Remember: by the time a skilled data analyst has taken your data and derived truly new inferences from it, it’s far more likely that it will be relevant (and valuable) to organizations outside your particular industry. In fact, one could argue that it makes strategic sense to try to develop data products with the aim of selling them to companies that could never actually compete with you directly. That’s one value of deriving an advanced tier of data: it contains insights that are based on your customer information, but that move far beyond it.
Ideally, the people on your data analytics team should also have a bit of insight into broader businesses beyond your own particular industry. You also want people who can learn rapidly, and who are familiar with the latest data analysis products. Five years ago, that would have been Hadoop, but now technologies such as Spark and Storm have made even Hadoop seem a little dated.
Consider the benefits
Making good decisions about monetizing data can help you maximize the amount of revenue you get from it. By ensuring that the data you produce and sell is truly unique, you can can minimize the risk of alienating customers, shareholders, regulators, and other interested parties by inappropriately selling information.
The data analytics landscape is changing — not only in the data monetization strategies organizations are using, but also in terms of the technology, processes and people involved. If data monetization is worth doing (and I absolutely believe it is) it’s worth doing right. To ensure you can do that, it pays to start with the right people.