It is at this stage that the data strategy is defined, indicating the objectives to be achieved. The guidelines are established so that, from the beginning of the journey, good practices are applied and a strategy defined to maximise the value obtained at each phase. The risk associated with such an initiative, which is intended to be transformational, is thus reduced.

Corresponds to the installation of the necessary software, on-prem or in the cloud, as well as the assignment of accesses to enable users to take advantage of it. This component is carried out according to the guidelines identified in the discovery phase and aims to ensure secure access to the analytics platform.

The solution is implemented iteratively for each use case, from data connection to visualisation. Delivery is made as quickly as possible, allowing immediate value to be derived from the solution. This stage requires various specific skills, namely at the level of data modelling, visualisation and possibly even data science for advanced analytics.

The process of evolution begins, aimed at ensuring the robustness of the solution, through maintenance and support. Furthermore, emphasis is placed on user adoption, through specific initiatives such as training and ensuring the incorporation of data in decision-making processes. This phase will run simultaneously with subsequent implementation phases that will ensure new use cases.