So far we have heard about data products, data as a service, and data points. All those are just isolated terms sometimes pushed in the same sentence. What actually happens and is created in the data refinement process? I'm not referring to finetuning SQL queries or JSON formatting. What I'm referring to is: hiding the complexity, DX, and .....
Data servitization process refers to data refinement, analysis, merging, processing, and packaging as well as visualizing in which the result is a service consumed by 3rd parties. Often the result is dynamic and interactive applications such as dashboards. The result is Data as a Service (Daas)
Hide the complexity
In the process, DaaS provider is hiding the complexity from the consumer. They do not need to worry about the things that happen behind the scenes. This is the value. We take care of the complexity and you cherry-pick the value. More often the DaaS is eating data products (see below) and raw data. This pretty much the same as with modern web APIs. Those are facades hiding the backend complexity. Before modern web APIs, we were forced to do direct intergrations which required pretty thorough understanding of the integrated system. By removing that the APIs changed the architecture landscape. That is also happening with data.
You do not need to know that, but more often you want to know to be able to test the credibility of the analyzed result. Just like with academic articles, you look at the references to see what is the foundation.
Reusable Data Products
In the servitization process data products are used as feed instead of raw data (see above). This is because data products have been preprocessed and packaged for later use and bring speed and ease of use in the process. Of course, there are situations you just don't have needed data products available. In those cases, it might make sense to take the raw data source and convert it into a data product. This makes even more sense if the created data product is likely to be used in other DaaS as well. This picture is a bit black and white since Daas can be used as feed for other DaaS solutions too.
From a DaaS provider perspective the process is enabling data reuse in more efficient way, reduces the costs or data reuse and shortens the timespan needed to enter markets with new data-driven, solutions.
remove the need for tacit knowledge
In the process data as a service provider also removes the need for tacit knowledge regarding the data source data point meanings. We are consumers do not need to know the meaning of "a1" which might be coming as a response from a sensor. We get the value given to us in vocabulary and format we understand - either as Data product or DaaS.