The topic was a question asked from over 1800 data and analytics decision-makers by Forrester 2020. But what is commercialization and monetization? What is the difference?
Commercialization is the process of bringing new products or services to market. The broader act of commercialization entails production, distribution, marketing, sales, customer support, and other key functions critical to achieving the commercial success of the new product or service.
"Monetize" refers to the process of turning a non-revenue-generating item into cash. In many cases, monetization looks to novel methods of creating income from new sources; for instance, by embedding ad revenues inside of social media video clips to pay content creators.
The difference is obvious and thus not discussed here in more details. We might return to that in following posts. But let's return back to the topic and that is answer to the question how companies commercialize data?
The above graph does not contain all possible options which were present in the answers, but only the most common. The huge percentage (46) of selling raw data most likely as datasets was a bit surprising to me. That can be at least partially explained with two aspects. Firstly, some of the data has so short life cycle value that you can easily sell it since customer needs to buy next set anyway. The dataset can also be something only a few can offer and there is no real competition and most customers just need to buy it again to update information such as company profiles and basic information. The second explaining aspect is that companies lack skills to refine the data efficiently. The CDO might be pressured to make revenue from data and is making quick wins by selling the raw data. That of course is not the optimal solution in the long run, but it does offer some revenue. I urge you to see other ways to do business than selling your most valuable asset.
Exposing API to data payload is the most common way API economy connects to the data economy. API access might offer the consumer some ways to refine the data stream or filter it. 'The amount of commercialization is just slightly higher than with raw data sets. It's notable that even in this case the data stream might be raw data which often requires more from consumer since data might require tacit or context-specific knowledge. This option is the current de facto method to support expanding success and use of event-driven architectures your customers are using more and more. The role of datasets is fading and the API driven subscription or request-driven access is rising.
The third level up in the image is often a dashboard or alike data mining GUI driven tool in which customer can interact with the data and see the visualized result instead of JSON data stream like is the with data APIs. Of course, this can also be API which has exposed functionalities to analyse data and provide the results as response. In that case we are talking about functional API which is one subtype of APIs. At this stage we are definitely talking about Data as a Service and not Data Products.
In the fourth layer data is embedded to another digital solution and data is not so clearly the commodity anymore, but act as valuable content. The app is the vessel but customer is still paying for the data. Application alone does not have value.
Food for thought - get coaching to succeed
The above examples offer of course just a small narrow viewpoint of the data economy and options to commercialize your data. Yet the above should give you some food for thoughts. Don't be shy to contact us to learn more and get coaching in your commercialization journey. See our the services.