Data value chain and your position in it is something that is considered as a must to understand. Any company now is creating value for the customers in cooperation with other companies and we rely on each other more and more. Understanding each others business and needs helps to identify own spot in the food chain and build partnerships.
Below is a refined picture of a drawing I did with one of our customers in a meeting just couple of days ago. Let's use that as an example. In the data servitization workshop we ended up discussing how their planned data as a service offering is going to fit in the markets. Customer has now partial business driven designs for the yellow and green boxes: 3 of them are data servitization and one (green) is data productizement.
In the discussion of the customers of the designed offering we ended up considering the customer experience and customer segments as well as how the customer engages to the services and are we talking about end user service or some component which is used in creating the end user service by someone else.
As a side note, customer said directly after the session that this simple framework really opened their eyes.
Layers used in framing the discussion
In the above drawing we crafted some layers just to frame the discussion. It is not intended to be any kind of logical architecture or like that. It's just a tool to facilitate discussion. As a side note, customer said directly after the session that this simple framework really opened their eyes.
At the bottom is raw data, which comes from software actions, sensors, documents etc. That raw data is used in the next layer which is a data platform harmonizing the data. At this stage for example all measurements of co2 look technically the same regardless of source system. Also the need for tacit knowledge has dropped since data payload is coded to follow common openly defined ontology. At this point starts the business driven data servitization process. Some of the data streams are pushed to analysis engine (ML/AI) and resulting analysed data flow is the core of resulting data as a service and data products.
In this case customer initially started to develop the dashboard-ish data as a service offering, but discovered additional data monetization options while going forward. Data Stories (just another data as a service) is a service which offers insights wrapped around with narrative for example in your email. From the data story we can offer link to dashboard solution containing more details. Both of these can be sold separately or bundled. Then there is API driven service which offers the insights (result of analysis) in machine readable data stream, which can be used in various environments. The last one is a data product: static set of values which can be uploaded to datalakes and alike for further use. As seller of data you need to figure out the platform / lake solutions used by your customer and enter the marketplaces offered for example by Snowflake. If your customer is using for example Snowflake, putting your data in that marketplace lowers the purchase barrier significantly.
In other words, go to the customer instead of trying to get them to you.
The above shortly described data commodities have different role and customer. This often neglected by organisations proceeding with data servitization.