In common daily discussions about data economy, we often use terms like data product and data as a product. With this rhetoric, we indicate that we consider and treat data like a product. Something that has to be productized. This kind of phenomenon happened also in the API Economy some years ago. People started to treat APIs like business-driven products rather than pure technical solutions alone.
However, the data economy landscape has already changed. Data as a Service is the term that dominates the emerging data economy and is likely to be the foundation of the future data value streams. Umbrella term above data product and data as a service is data commodity. In practical life data products often act as feed for data as a Service, but other kind of relationships between the two exist as well.
Using the term Data as a Product might sound outdated to some. Some of us want to approach the data economy from a service-dominant logic point of view. In that vocabulary, we use Data as a Service. What is the difference and what are examples of Data as a Service? Let’s have a look.
Differences as range sliders
Drawing differences between data as a product and data as a service can made by estimating where the sliders should be on 4 aspects. In the above picture you can see the axis (4) to use. The further left all sliders are, the more product alike data commodity is. Likewise, the more on the right sliders are, the more service alike data commodity is.
End of bipolar thinking
This model has benefit that we are not forced into bipolar selection or debates whether we are talking about a product or service. Instead, as a result we can say that one data commodity has more service features or nature. Sliders of Data commodities very rarely are all in the far left or far right. There's pretty much always some kind of mix.
But if we want to list some features of data products and data as as service, we can say that
Often a snapshot
often data content is large (relative term)
Content is often fixed
Less refined - less directly usable value for customer
Used as feed for Data as a Service
Might have One time payment pricing
Data as a Service:
Often small batch but timely fresh
Currently often GUI driven, but APIs offer machine-level access as well
Customer involved in package content and value creation
Returned result might come from an analysis
More often subscription based pricing