Monetizing on 'big data' - how to move from buzz to value

Big Data and Advanced Analytics. The vast possibilities for monetizing on data which these terms convey have been hyped to death during the last five years in the business community, up to a point where the words by now is considered all fluff and empty calories by many. There has been high expectations, but few companies have actually managed to drive meaningful value from their data. Tellingly, in Gartner's tech hype cycle from last year, big data had reached the point of inflated expectations. This is a shame: the data which companies already have stored, and the data which they have the potential to retrieve in some form or another, can be of significant value if applied deliberately in the right context. You do not have to be Google, Amazon or any other high-tech company to get value from data.

The main reason for why a lot of companies struggle with realizing value from data is that they fail to ask meaningful questions about in which relevant use cases it can be applied and how it can drive value in these applications. Instead they either rely on the illusive hope that expensive technological solutions will perform some sort of magic trick and all of the sudden make the data valuable by the press of a button, or they become overwhelmed and end up just giving up before really getting started. Data monetization is not magic - it's not even that complicated, but it can admittedly be a difficult exercise to go through because it forces organizations to make some hypotheses about possible applications, the value of data, and venture into unknown terrority.

Starting with the value first - a deductive approach to data monetization

Instead of an inductive approach where one first look at the data available and then try to figure out how it can be of value, it can be much more effective to look at data deductively, first asking how it can drive value before venturing into the exact nature of the available data. Following these five high-level steps can be a useful anchor in the initial stage of your data monetization efforts:

1. First set some well-thought hypotheses about which overall application segments your data could be of use in, both internally within the organization's different departments, and externally to potential customers and partners. For instance, data about your production effectiveness or lack thereof is obviously of value to the production department, but could it perhaps also be of value to the suppliers which are involved in the production?
2. Map the value drivers for each overall use case, looking both at in which ways your data potentially can increase revenue and reduce costs. Ask questions such as: what drives value in the sales department vis-a-vis at an external partner? Where lies the biggest potential to drive value from the data for this specific application - is it in driving increased sales through retention, or perhaps by decreasing costs by reducing the need for labor?
3. Investigate the possible use cases for the most relevant application segments. Most often there are several ways which your data can provide value for a specific application segment. Therefore a question about what the potential business events and interactions that the data can provide to should be raised and answered. If looking at an external application of data, different monetization options should also be assessed in order to give a comprehensive foundation for making a decision on the business model.
4. Assess the level of data insight which is required in order to make the use cases come to life. Do we have this data available today? If not, will it be available in the future? What can we do to make the needed data available if we do not have access to it today?
5. Start looking into possible inhibitors to bring the data to life, including technical, regulatory and market challenges. These inhibitors can admittedly be show-stoppers for some use cases, at least initially. Nonetheless it gives an understanding of what it will take to get the business model for the data monetization up and running.

There still lies a lot of unfilled value potential hidden within the realm of big data. Until companies start to deductively look at the potential value which their data possesses first, the potential will remain unfulfilled.