A use case-driven approach to data science
Many corporate strategies contain one or several points such as “being data-driven”, “utilising data as a strategic asset” or “leverage the potential of artificial intelligence”. For the technical parts of the organisation, it might be tempting to rush off and purchase a cool platform for doing data science and thereby fulfilling the corporate strategy. The hypothesis might be that the value will naturally appear along the way when the right technology is in place. Problem is, if technology is the only focus, the value most often is never realised.
Successfully implementing AI systems in general is extremely complex both from a technical and organisational perspective. The right technology must be chosen from a large technology landscape, and it is necessary to locate the right balance between technology and business domains. When the tech stack has been decided, the operating models will have to be designed and implemented. At the same time, the organisation might need to be adjusted, and the mindset of the organisation must be changed to think and work in new ways.
To ensure that the expected value is realised from data science investments, we believe that data science implementations must be started backwards by asking the simple question of Why we want to have a data science capability in the organisation in the first place. What are the use cases that justify making this investment (typically assessed by effort vs impact comparisons – see below), and is it realistic to get an acceptable return on the investments made if those use cases are implemented?