Every other month DevMynd(custom software development company) hosts an event we call the Executive Leadership Roundtable. It’s a great opportunity for some of Chicago’s best business and technology minds to come together for an insightful discussion. At one of our recent events the topic was the monetization of data. It was such a great exchange of ideas that I thought I’d share some of the insights that emerged.
To set the stage, we had panelists from an array of backgrounds, executives from companies like Truven Health, Rand McNally, Sportvision, and Rise Interactive. It was through this diverse set of industry experience that we found a lot of common ground in how organizations think about deriving profit from data. Here’s what we learned:
Solve a Problem
Like any product, producing “data for sale” has to help solve a problem for your customers. Whatever industry you are targeting, you have to understand what’s keeping your customer up at night. This means extensive research into the trends and challenges they face. And, understanding their competitive landscape against which your data could provide an advantage.
It’s also helpful to know what kind of problem you are solving, and what value you are adding. Are you creating insight for the customer that increases their top-line revenue, reduces operating cost and bolsters profit, or mitigates risk? Knowing the broad category here will help you position your data, and also helps to encourage creative thinking into the customer use cases.
Almost every business generates data of some kind, and they probably also produce more data than what might actually be considered valuable. However, sometimes it’s helpful to go dumpster diving, and look at some of the data that is effectively thrown away. You may be surprised at what discarded data you might find some potential value in.
Also, don’t look at the data you have now (what you produce) as having to represent standalone value. It may very well be that buying some outside data (and combining it with your own) produces something more useful than the sum of the parts. Several of the panelists commented during the evening that they both buy and sell data in this way.
A Framework for Insight
One way to look at the combination of various data sets is through a simple framework that one of our panelists introduced. The idea is to look at data that represents three distinct dimensions of a domain, then use the combination to drive insight:
- Demographics: This can be attributes of people, companies, or other entities.
- Behavior: What measurable things do these entities do?
- Context/Environment: What were the conditions when these behaviors occurred?
Another interesting aspect that doesn’t always exist in the data…is time. Sometimes insight can only be derived when trends can be discovered in all three elements of this framework.
Create a Value Chain
Another insight that came up in our conversation, was taking a value chain view of your data. Raw data might be worth something to some customers, but others will need it compared against various benchmarks. Still, other customers will need those benchmark comparisons to be aggregated and put into context against other data sets. At each stage in the chain, additional value is added to the data, therefore opening new markets for your data to become a viable resource.
All of the panelists agreed that pricing data was a complex task. And, unless you plan to offer your data to the general public through a commodity model–then each deal would likely be unique. Several panelists agreed that value-based pricing was a good way to go. In other words, deferring some amount of revenue and tying it to the value the customer received, either in increased revenue or reduced costs or risk.
Give to Get
One recurring theme, was the idea of a data exchange rather than just a one-way purchase arrangement. One example of such an arrangement, is where a customer will provide source data that you can enhance with your own data and processing. The second is more of a “rising tide floats all boats” scenario, in which several customers pool data together to be aggregated and enhanced.
These scenarios create new opportunities for inventive pricing models. For example, customers who provide their own data into the collective pool receive a discount on the resulting enhanced data.
Privacy Is Paramount
It is critical to consider privacy in your data products. This is especially true when the data includes, or is derived from, personally identifiable or sensitive information. At various points in the discussion, the experts all indicated that privacy was a concern that should span contracts, controls, and audits.
There are many ways to address privacy concerns but masking (obfuscation) and aggregation seem to be two of the favorites. Masking means removing or replacing enough of the fields in a data set in order to make the identification of an individual subject impossible. However, historically this has proven difficult. Much more care should be taken with this method, when it involves source data. With aggregation, the data is rolled up to a level where individuals no longer are visible, only cohorts.
I hope some of these points have been helpful as you think through how your organization can build revenue streams with data. As always, the DevMynd team is standing by to help with these and other strategic technology needs. We look forward to hearing from you.