Talking about the data economy is fun and people engage in it nowadays pretty well. At some point in the discussions, focus turns into practical models to clone instead of reinventing the wheel. One of the things most business people want to see is model how the data monetization wheel spins. I will shortly introduce one practical model selected from the one globally operating company I have had pleasure to work with. The case company is new to the data economy and thus the model is the initial version and due to change over iterations and time. The overview of the model is the presented picture. Let’s have a look deeper look at it.
Goal is to develop high revenue primary data-driven service
At the core of monetization is data as a service commodity which is solving customer segment problems by offering a GUI-driven view to discover hidden signals around selected topics. In the image that is at the bottom as the orange box. This is primary service to develop since it is expected to have a big user base and it will have a high return of investment.
Ensure data generation to survive
To be able to offer such a service, the company needs a huge amount of data. More importantly, data must be regenerated constantly. Otherwise, the service will be obsolete. Getting this part running is vital and in some cases, the customers become the feed for data. In the picture, this is the top grey box. In this case source of data is huge over 1 000 000 highly educated professionals and they provide curated information every day. That is the core data flow coming in. Of course, the company has opened the door for external data as well but at this stage, additional data flow has not been activated.
Productize data for efficient reuse and sales
That constantly updated information is analyzed and processed among other things with ML and AI solutions and the result is for example topic signals. Think about it. Raw data is curated information created by professionals and is refined to easy-to-use signals. The result is packaged or as the process in general is called productized into data products. The resulting data products are offered via productized APIs to partners as commercial commodities. In short, data products are servitized for internal and partner usage.
Servitize data for maximum revenue and value for customer
The resulting data products are not opened to 3rd party since that would cannibalize their own primary service value proposition in the form of competing solutions. Data usage is limited with agreements and thus partners are not posing a threat to primary service. Data would be anyway productized to shorten the reuse time and increase the value. Notable is that data has been productized and monetized as a side process - the focus is still on the primary service. This is the first tier of the data monetization cycle. Over time more and more data products are created and the money wheel keeps on spinning.
The same productized data which is sold to partners is used in its own primary service development and value creation for customers. Target segment and users of the primary data service are different compared to the data products. Also, the market size is bigger and thus this is expected to make most of the data-driven revenue in the company. Some of the customers are also the data creators and enable the data monetization cycle to regenerate.