A white paper for anyone who wants to systematically develop, prioritize and successfully implement use cases with the data thinking framework

Successful use cases don't just arise from one perspective. Data thinking combines four perspectives: strategic, problem-oriented, data-oriented and technology-oriented. As a result, long-term business goals as well as acute operational challenges, existing data sources and new technological opportunities can be taken into account. It is precisely this multi-perspective derivation that reduces the risk of overlooking relevant potential or prioritizing technically irrelevant data projects.

A data thinking workshop typically consists of three phases. In the first phase, as many use case ideas as possible are collected from all four perspectives. In the second phase, these use cases are specified, for example with regard to business goals, processes, data and technologies. In the third phase, use cases are prioritized according to benefits and costs, resulting in a robust portfolio and an actionable roadmap for the next data initiatives.

It depends on the level of maturity, resources, and complexity of data work. A decentralized organization has often grown historically, but easily leads to silos and untapped potential. A central data team enables rapid initial success and clear control, but as the number of use cases grows, it is more likely to reach communication limits. A hybrid, federated model combines central standards with more subject-oriented analysts and is therefore often particularly effective when it comes to scaling data work while remaining close to business.

Use cases provide clarity about goals, requirements and expected added value. They help to better understand user needs, improve communication between departments and implementation teams, set priorities and make project success measurable. Specific use cases are crucial, especially when introducing BI or AI solutions, because they prevent technologies from being introduced without clear business benefits.

The Data Value Creator is the central role that permanently anchors data thinking in the company. He combines data expertise with business goals, identifies and prioritizes value-adding use cases, and ensures that individual ideas become effective data initiatives over the long term. At the same time, it strengthens the data culture in the company and acts as an internal driver for not only launching data-driven projects, but also putting them into practice in a sustainable way.