Organizations find a wide range of possible scenarios to benefit from data analytics for innovating their existing business. Various studies reveal, though, that in doing so organizations still have a strong internal focus. Organizations optimize process efficiency, increase productivity, support strategic decision making and create additional insight into their customer base. This is referred as data-enabled improvements (Schüritz at al). In these cases value proposition is not changed or affected.
As an advanced step of servitization, Schüritz at al refer to this transformation as datatization and define it as the innovation of an organization's capabilities and processes to change its value proposition by utilizing data analytics.
The data-enriched Products and Services can be said to refer to boosting existing offerings with help of data-related addons and alike. A simple example is to provide small online real-time analysis of a product or service usage. In this box the thinking is still partially or completely goods-dominant and product-centric. Even if the addon is small, it will change the value proposition.
The green box is what intrigues me the most. That is where the Data as a Service dart lands. Here the thinking is mandatory to be service-dominant logic.
The transition from servitization to datatization
According to research jumping into datatization as the next level of servitization requires some additional things from the organization. Here's a compact list of things to consider:
Strategy: Organizations that want to take advantage of datatization are challenged to make a clear decision about the role of data analytics in their service strategy as well as to develop a clear data strategy. In such a strategy involves series of decisions regarding the access and usage of data are crucial.
Organizational structure and governance: Organizations initiating datatization need to decide if involved teams are placed in a centralized, new unit that works more independently from the rest of the organization or if the teams are segmented across the organization in line with their product or service association.
Processes: While most organizations have gained experiences on service development and service delivery through their servitization efforts, data analytics imposes a set of new challenges. While software may have been one of many elements in the service development before, now software becomes a cornerstone of the service.
Skills and capabilities: When it comes to data analytics, it is claimed that data scientists hold “the sexiest job of the 21st century”. They are broadly demanded by the market and seem to be in tremendous shortage right now. Required data science skills show a much broader variety and sophistication compared to required skills in servitization. Due to the strong technological focus of these new offerings, IT capabilities become an essential cornerstone for datatization as well.
Design of offering: Designing new data-driven services or enriching existing products or services brings to bear new challenges for organizations. It is particular challenging for some organizations to identify business value in the gathered data, develop innovative offerings on top of them. Another major challenge is to ensure scalability through standardization (not to develop individual solutions for all).
Design of Revenue Model: The design of the revenue model for datatized offerings can be challenging, but shows similarities to the servitization endeavor. This includes also the ‘service paradox’; a situation in which adding services to the offering portfolio of a product focused company leads to increased revenues, but decreasing profits. Organizations that decides to uncouple the service from the core product have the potential to open up a new revenue stream and take advantage of new revenue models (e.g. subscription, usage fee, gain-sharing)
Market: it is crucial to gain a sound understanding of the respective market in order to develop a data strategy. The market for data analytics offerings is still immature. Some customers are reluctant to take advantage of such services and you might have trouble to gain approval for purchasing such services.
Culture: Organizations that want to take advantage of data analytics face the challenge of incorporating data analytics specific aspects in their culture. As addressed in organizational structure and governance, a high degree of collaboration across departments is necessary. Priority is to adapt a culture that accepts failure at an early stage as it is sometimes tedious to find value in datasets.
Co-creation: When utilizing data analytics to deliver new innovative services, co-creation even seems to be of higher importance. Data becomes a resource to which an organization needs reliable access and which may not exclusively be generated in the spheres of an organization but very well be created within the boundaries of the customer. It is often necessary to engage with partners in order to gain access to data and capabilities that are needed to offer the desired service.