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.
Using the term Data as a Product might sound outdated to some.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.
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Subscription economy driving service hegemony
Before we take a deep dive into Data as a Service, it is fundamental to understand that other megatrends and business model trends affect data economy just like any other economy. That is why we need to observe what is happening in the business models applied, how the consumer segment behaviour is changing as an example. One of the big changes that has already occurred is that we have entered Subscription Economy. The Subscription Economy is a phrase, coined by Zuora, describing the new business landscape in which traditional pay-per-product (or service) companies are moving toward subscription-based business models.
At the same time generations of people are less interested inthat owning. Younger generations do not want to buy a house with 30 years of payments, they rather not own cars or cottages. We no longer buy CDs or DVDs either. Instead, we subscribe to services which offer us the goods - both physical and digital. We are no longer paying to own - we are paying to get access.
To watch movies and tv-shows we are not buying digital products or physical discs. We subscribe to Netflix of alike service. That way we get access to hundreds of movies and tv-series. Of course, our favorite shows are part of different streaming services and we need to subscribe to both of them and in the worst case more and more to multiple services.
This service-driven logic is not B2C specific. Products get service wrappers using the customer experience and solution levers. Many products, be it hardware or software, are being offered "as a service". A term used to describe the phenomenon is servitization.
The same is the reality on a big scale between companies as well. In its simplest terms, servitization refers to industries using their products to sell “outcome as a service” rather than a one-off sale. Although very different from media streaming businesses, manufacturing can also benefit from servitization.
servitization refers to industries using their products to sell “outcome as a service” rather than a one-off sale.
Manufacturing businesses can offer additional services to supplement their traditional products such as maintenance, keeping a fleet of vehicles on the road as a service. Servitization is usually a subscription model and can be applied to most industries in one way or another.
Power by the Hour
Let's look at the air traffic industry. Jet engines are sold as "Power by the Hour". The airline does not pay for the engines, but for the time they are flying. This model includes necessary services and for example data about the engines. This is a big change compared to the old jet engine landscape.
Servitization of Data
In data economy example is to provide updated situation as a stream of data just like we stream movies from Netflix. We are not interested to own the data, we want to consume it to create value for own purposes. With data instead of owning it, consumer wants to have rights to do something with it. Just like with APIs, which you do not buy to own, but to consume.
In the Data as a Service logic, the product might be there, but what is sold and purchased is the service. Furthermore, the data as a product is not always even present since it’s just data stream. Limitations of usage, pricing, name and all that traditional business logic is in the service instead.
The change has been noticed and expained also by the academic community. What we are witnessing is the age of service-dominant logic.
An academic article by Stephen Vargo and Robert Lusch from 2004 about new service-dominant logic fuelled a truly international discussion about the potential of service logic to change the mainstream, goods-based logic. The authors conclude that “perhaps the central implication of a service-centered dominant logic is the general change in perspective”. Here are a few selected aspects of the service-dominant logic.
Service-dominant logic is a perspective for understanding value creation. Value creation in turn is at the core of practically any business. The goal of service systems is to provide input into the value-creating process of other service systems. All involved parties should gain some value. The firm gains for example financial benefits and customer value in terms of becoming “better off” in some way. Let's discuss briefly a few fundamental principles of service-dominant logic.
The customer is always a co-creator of value
In service-dominant logic, the customer is actively participating in the value creation. Customer is not an object, but an active subject - one source of value creation. The customer is always a co-creator of value and is always involved in the value-creation process. According to some scholars, the customer is the primary creator and evaluator of value. The service provider could be invited to join this process as a co-creator. Without the customer, goods or services have no value except negative due to related costs.
The firm is fundamentally a value facilitator
The service approach focuses on interactions instead of exchange. The producer is not so much trying to match the customer's expectations in advance which is a must-have in product-dominant logic. Instead, the service provider is not restricted to offering value propositions only but also can directly and actively influence customers’ value fulfillment in some situations.
Service is more about the process than the result
According to research goods are a distribution mechanism for service provision. Goods have no value in themselves, but only as transmitters of service for the user. As an example razor provides service which previously was offered by barbers. Goods are one of several types of resources functioning in a service-like process, and it is this process that is the service that customers consume. Instead of luring consumers to the exchange process - the old goods-based -, we are geared towards facilitating interaction.
Given the above, the claim included in Service-Dominant Logis is that it is all about service.
Apply Service-Dominant Logic to The Data Economy
Now turn your head towards the data economy. Keep the things in mind we just discussed. Data product can be both something that is exchanged and a service. The product can be part of the service offering just as the literature suggests.
Here’s a stereotypical drawing of how the landscape of data as a service might look. The drawing is focused on data monetization and how to do that. It is built upon the level of data servitization and publicity of the service. Of course, the real world is not this simple and diffusion between the publicity levels occurs. The drawing is intentionally drawing your focus on the service by leaving products out of it.
Publicity levels and servitizaton level
The publicity levels have been taken from the API Economy in which over time such layers have been found to include practically all commercial APIs. Data as a Service and APIs share a lot of features and thus the categories are expected to apply to Data as a Service logic as well.
The X-axis is the level of servitization. The closer offering (Data as a Service) is to the origo, the less servitized it is. Likewise, the most servitized solutions are on the far right of the drawing.
Data as a Service for Internal use
In the lowest level of servitization is data which is servitized to gain speed and increase reuse and usability. Currently, data scientists use 30% of their time to prepare data for later use. Servitizing the data will decrease the time needed for processing it. Data at this level of publicity and servitization is intended mainly for internal use.
data scientists use 30% of their time to prepare data for later use.
Data as a Service in the network value chain
Consuming internally your data as a service does not sound like something to take a big role now. But in the network layer, for example, ecosystem partners, it makes sense it servitize data for them and the result is Data as a Service. In some cases, data in almost raw unprocessed format is provided for partners. Such an example is warehouse management. The technical means to offer data as a service might be API.
Data as a Service as public offering
Offering raw data for the public 3rd party consumption does not make sense. Selling your raw material results in a lower value for the company and customers often lack the necessary resources to process and refine the data for the value they aim for. At this highest level of publicity and servitization, data is often not even recognizable but visualized as graphs or even as functions answering questions and creating inputs for other systems.
In short, from the provider firm’s point of view, we can say that you need to servitize data for internal use to gain maximum speed, you need to servitize data to maximize partner network value and you need to servitize data to maximize sales and customer value.