How does a company successfully introduce data science?
Primeo Netz AG leverages with eoda the potential of AI in the energy transition
Primeo Netz AG wants to adopt a strategic approach to data science in order to successfully meet the challenges of the energy transition.
From vision to production use: eoda's experienced data science consultants were able to significantly increase the AI maturity level of Primeo Netz AG in a short period of time.
Primeo Netz AG has leveraged the data science solution component with eoda, generating tangible added value and laying a strong foundation for future AI initiatives.
With over 600 employees, Primeo Energie supplies customers in Switzerland and France with reliable energy. Primeo Netz AG serves the business field of grids – from the transport and distribution of electricity to the provision of grid-related services and e-mobility.
Especially with increasing volatility due to the expansion of renewable energies and the growing importance of e-mobility, new approaches to solutions are needed in the energy industry. To be able to meet the challenges of the energy transition even better, Primeo Netz AG would therefore like to act in a more data-driven manner and establish the data science / big data unit.
Primeo Netz AG wants to position itself strategically in data science to successfully develop data as a component in the energy transition.
To achieve this goal, Primeo Netz AG relies on the data science empowerment of eoda GmbH. With this established approach, eoda’s analytics experts accompany companies from the idea of generating added value from data to the first exemplary AI solutions.
The milestones of Data Science Empowerment at Primeo Netz AG:
Knowledge transfer and inspiration: Artificial intelligence and the opportunities and risks associated with it are the most influential technology issues of our time. This makes it even more important to create a shared basic understanding as early as possible. In a comprehensive keynote speech, the employees of Primeo Netz AG were explained the background, fields of action, and opportunities and risks of artificial intelligence were explained. This shared knowledge base is the breeding ground for all further initiatives.
Development of a data science vision: Successful data-driven processes rarely end at the front door of a department, and data-driven decision-making does not end at the next level of the hierarchy. This means it is even more important to give this company-wide change process a goal – a clear vision for data science. Together with the management of Primeo Netz AG, the eoda’s experts identified the most important future topics in a workshop and combined them into a common vision.
We keep our data easily accessible and use it with a sense of responsibility as the foundation for an efficient, secure network, a compelling customer experience, and the active creation of the energy transition.
Data Science Vision of Primeo Netz AG.
The further milestones of Data Science Empowerment at Primeo Netz AG:
Development of the data science roadmap: Based on future topics and with the vision as the goal image, eoda developed the data science roadmap for Primeo Netz AG for the next few years, which shows those responsible specific recommendations for action and milestones on the way to achieving their vision.
Identification of the most important use cases: The identification and analysis of potential use cases is the basis for successful AI projects. In this phase, the business value and analytical feasibility of AI use cases must be examined, as well as the given framework conditions (legal situation, data protection, ethics, etc.) and the resulting expenses.
In the course of an AI use case workshop, the Primeo Netz AG managers identified more than 30 relevant use cases together with eoda and prioritized five of them as particularly promising.
Based on an initial elaboration using the Data Science Canvas, eoda further specified the evaluation of the use cases and developed initial approaches and solutions.
Deep dive for selecting the right use case: In the deep dive workshop, the prioritized use cases were further specified. Using a set of guiding questions, the necessary information for the successful implementation of the use cases was determined or information gaps were uncovered at an early stage. The guiding questions relate, for example, to the target variables, the subsequent users and the integration of the possible results into the work processes. The deep-dive workshop enables a reliable assessment to be made of whether it makes analytical and economic sense to transfer a prioritized use case to the pilot phase and thus to implementation.
This approach significantly increases the probability of success of data science projects for Primeo Netz AG in the pilot phase and avoids time-consuming and costly development of unsuitable use cases. In addition, the pilot phase can be better planned and there is transparency about what still needs to be clarified or prepared before the start of implementation.
Implementation of the AI pilot: For the most promising use case for forecasting the feed-in of renewable energies, eoda develops an AI pilot with Primeo Netz AG within two months. This makes the enormous data science potential tangible in a concrete use case, the control of grid expansion. eoda accompanies Primeo Netz AG in this phase from data preparation, explorative analysis and model building to documentation and presentation of results.
Infrastructure assessment for the productive use of data science: For the productive use of AI solutions and the implementation of data-centric processes, the right IT infrastructure is needed in addition to the know-how and the right use cases. As part of eoda’s Data Science Empowerment, Primeo Netz AG’s technical infrastructure is also being assessed for its AI readiness and specific recommendations for action are being developed to build an environment that will enable Primeo Netz AG to achieve its data science vision.
From vision to real solution: With eoda, Primeo Netz AG has identified data science as a central component of the energy transition, generated tangible added value, and built a strong foundation for future AI initiatives.
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