This is how you find use cases for modern technologies such as machine learning.
As a result of extensive digitization and connectivity, companies are accumulating huge amounts of data. They can use these with the help of intelligent processes and machine learning to generate added value. But many projects fail because companies neglect the strategic relevance of the entire use case or, in some cases, do not even consider it. This is where a comprehensive data strategy can help. This enables companies to successfully shape their digital future, make better decisions and optimize processes.
“I’ve got an idea: why don’t we …” is often how new projects start in companies. An employee or the boss has either developed an idea for a use case himself or has been inspired to do so – be it at a conference, through an article or a conversation with customers.
In fact, many projects start from such spontaneous ideas. And in principle, it’s not bad to start with them in order to gain initial experience and learn from it. But it often ignores the fact that there are limited or, in some cases, not the right resources available, such as people, tools, data and time.
Possible consequences: The implementation fails, comes to nothing or develops into a never-ending story. If the use case is implemented, it offers little added value and is not effective. This means that it will not be used in practice. Accordingly, companies should clarify in advance whether the necessary resources are available and whether the use case increases efficiency and is strategically relevant. A number of successful examples from various areas of application show how this works.
Implemented use cases
- Higher ticket sales at soccer club
- Fewer rejects at pharmaceutical company
- Detection of pseudo defects at semiconductor manufacturer
- Development of new markets at automotive supplier
To learn more about those use cases read the full article (in German): Ihr erster Schritt zum datengetriebenen Unternehmen