There’s no such thing as a free app. “What?” I hear you say, “but I download free apps all the time!” So then why do organisations spend considerable time and effort creating free apps? Often their goal is to collect data and turn it into money.
Consider this example. There’s a popular photo editing app in the US that allows you to edit photos as you take them. Yes, your bestie looks fab with that hat on, but what you might not realise is that your photos are being used to create a heat map of tourist activity. Over time, trends will emerge from that location data which the app developer can sell to cafes, stalls, bars and anybody else looking to set up shop in a profitable area. Forget eyeballing foot traffic! Today’s app data can tell restauranteurs the hottest spots on the hottest days at the hottest times.
And there you have it – the data monetisation of a mobile app. In this example, no personal data was captured – the legal requirements of using personal data is something all companies are grappling with (with opt in clauses built to withstand the tests that may eventually be exercised against them).
Monetising data is one of the most exciting outcomes of the big data analytics world. So how can you monetise your data? Here are three common ways traditional businesses are doing it.
1. Sell the raw data
Selling the data in the same format you collect it is by far the simplest method. This requires little effort on your part other than sorting the legal data reuse statements. The one caveat, however, is that considerable data preparation effort is often required. A good data management strategy can mitigate this effort drastically. Blumberg and Thomson Reuters are great examples of organisations providing raw data to the market for consumption.
2. Offer insights to select markets
In this scenario you keep the raw data in-house and apply data visualisation or predictive modelling techniques to identify valuable insights. This requires you to identify the use cases of the insight and sell that value into relevant markets. This type of data monetisation usually requires a dedicated team.
Investment Advisory Houses are a good example of this. They offer subscription-based access to their analysis of equity investment options. This insight is based on their deep industry knowledge combined with their analysis of complex data sets. As with No. 1 above, this data monetisation strategy also requires a strong data management and governance strategy to prove your insights are reliable.
3. Provide action-based offerings
Action-based offerings are services spun off the insights generated above, enabling you to apply your niche expertise to help the customer operationalise the insights you uncovered. For example, SAS has provided analytics software, consulting and training services for 40 years now. Recently we created an analytics-as-a-service offering called SAS Results. It’s for customers who are unable or unwilling to invest in creating their own advanced analytical capabilities. SAS generates insights on their behalf and then helps them apply those insights to their business.
Each of these data monetisation options have their merits – what seems valuable to one company may be viewed as a disadvantage to others. Success comes down to your business culture, appetite for risk, need to diversify and ability to stand behind the quality of what you deliver. Tremendous value can be achieved for those willing stand back and look at their data and its market potential. So how do you get started? I’ll leave that for next time. Until then, check out this great IDC infographic that provides a sense of today’s data monetisation playing field.