Use case development model for data spaces
Fecha de la noticia: 25-05-2023
We live in the era of data, a lever of digital transformation and a strategic asset for innovation and the development of new technologies and services. Data, beyond the skills it brings to the generator and/or owner of the same, also has the peculiarity of being a non-rival asset. This means that it can be reused without detriment to the owner of the original rights, which makes it a resource with a high degree of scalability in its sharing and exploitation.
This possibility of non-rival sharing, in addition to opening potential new lines of business for the original owners, also carries a huge latent value for the development of new business models. And although sharing is not new, it is still very limited to niche contexts of sector specialisation, mediated either by trust between parties (usually forged in advance), or tedious and disciplined contractual conditions. This is why the innovative concept of data space has emerged, which in its most simplified sense is nothing more than the modelling of the general conditions under which to deploy a voluntary, sovereign and secure sharing of data. Once modelled, the prescription of considerations and methodologies (technological, organisational and operational) allows to make such sharing tangible based on peer-to-peer interactions, which together shape federated ecosystems of data sets and services.
Therefore, and given the distributed nature of data spaces (they are not a monolithic computer system, nor a centralised platform), an optimal way to approach their construction is through the creation and deployment of use cases.
The Data Office has created this infographic of a 'Model of use case development within data spaces', with the objective of synthetically defining the phases of this iterative journey, which progressively shapes a data space. This model also serves as a general framework for other technical and methodological deliverables to come, such as the 'Use Case Feasibility Assessment Guide', or the 'Use Case Design Guide', elements with which to facilitate the implementation of practical (and scalable by design) data sharing experiences, a sine qua non condition to articulate the longed-for European single data market.
The challenge of building a data space
To make the process of developing a data space more accessible, we could assimilate the definition and construction of a use case as a construction project, in which from an initial business problem (needs or challenges, desires, or problems to be solved) a goal is reached in which value is added to the business, providing a solution to those initial needs. This infographic offers a synthesis of that journey.
These are the phases of the model:
PHASE 1: Definition of the business problem. In this phase a group of potential participants detects an opportunity around the sharing of their data (hitherto siloed) and its corresponding exploitation. This opportunity can be new products or services (innovation), efficiency improvements, or the resolution of a business problem. In other words, there is a business objective that the group can solve jointly, by sharing data.
PHASE 2: Data-driven modelling. In this phase, those elements that serve to structure and organise the data for strategic decision-making based on its exploitation will be identified. It involves defining a model that possibly uses multidisciplinary tools to achieve business results. This is the part traditionally associated with data science tasks.
PHASE 3: Consensus on requirements specification. Here, the actors sponsoring the use case must establish the relationship model to have during this collaborative project around the data. Such a formula must: (i) define and establish the rules of engagement, (ii) define a common set of policies and governance model, and (iii) define a trust model that acts as the root of the relationship.
PHASES 4 and 5: Use case mapping. As in a construction project, the blueprint is the means of expressing the ideas of those who have defined and agreed the use case, and should explicitly capture the solutions proposed for each part of the use case development. This plan is unique for each use case, and phase 5 corresponds to its construction. However, it is not created from scratch, but there are multiple references that allow the use of previously identified materials and techniques. For example, models, methodologies, artefacts, templates, technological components or solutions as a service. Thus, just as an architect designing a building can reuse recognised standards, in the world of data spaces there are also models on which to paint the components and processes of a use case. The analysis and synthesis of these references is phase 4.
PHASE 6: Technology selection, parameterisation and/or development. The technology enables the deployment of the transformation and exploitation of the data, favouring the entire life cycle, from its collection to its valorisation. In this phase, the infrastructure that supports the use case is implemented, understood as the collection of tools, platforms, applications and/or pieces of software necessary for the operation of the application.
PHASE 7: Integration, testing and deployment. Like any technological construction process, the use case will go through the phases of integration, testing and deployment. The integration work and the functional, usability, exploratory and acceptance tests, etc. will help us to achieve the desired configuration for the operational deployment of the use case. In the case of wanting to incorporate a use case into a pre-existing data space, the integration would seek to fit within its structure, which means modelling the requirements of the use case within the processes and building blocks of the data space.
PHASE 8: Operational data space. The end point of this journey is the operational use case, which will employ digital services deployed on top of the data space structure, and whose architecture supports different resources and functionalities federated by design. This implies that the value creation lifecycle would have been efficiently articulated based on the shared data, and business returns are achieved according to the original approach. However, this does not prevent the data space from continuing to evolve a posteriori, as its vocation is to grow either with the entry of new challenges, or actors to existing use cases. In fact, the scalability of the model is one of its unique strengths.
In essence, the data shared through a federated and interoperable ecosystem is the input that feeds a layer of services that will generate value and solve the original needs and challenges posed, in a journey that goes from the definition of a business problem to its resolution.
