Take the first step to become a data driven company

Comprehensive digitization and networking mean that huge volumes of data are generated in companies. And they want to use this data 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 fail to consider it at all. This is where a comprehensive data strategy can help. It enables companies to successfully shape their digital future, make better decisions and optimize processes.

New business projects often start with the words “I’ve got an idea: Why don’t we ...”. An employee or the CEO has developed an idea for a use case or was inspired to do so—be it at a conference, through an article or a conversation with a customer.

In fact, many projects start from such spontaneous ideas. And it is generally also not a bad starting point for gaining first experiences and learning lessons from them. But many ignore the fact that they may have limited or sometimes not the right resources in terms of people, tools, data and time available.

Possible consequences: The implementation fails, comes to nothing or turns into a never-ending story. Once the use case is implemented, it offers little added value and is ineffective. Therefore, it is not put into practice. Accordingly, companies should clarify in advance whether the necessary resources are available and the use case will increase efficiency and be strategically relevant. A few successful examples from various fields of application show how this is done.

These examples show that a company should first develop suitable use cases based on existing business models, strategies and processes and evaluate them in terms of added value before implementing them. However, the starting situation varies greatly from company to company. This is why use cases need to be customized and developed for a specific purpose. They usually form the starting point for deriving a holistic long-term data strategy, including data governance and data management. However, excessive expectations also need to be addressed in this context. The management, for example, wants to immediately achieve optimization through AI, but the data quality, IT structures and the skills of the employees are not sufficient for this. To minimize risks, we recommend a step-by-step approach based on a specific use case and using the Design Thinking method.

This approach does not require wizardry and can be implemented through a well-managed two-day workshop. The workshop will identify data-driven approaches for each company and, on this basis, develop a holistic data strategy.

• A company should start with their goals or challenges and define the added value a use case should deliver. Only then should they assess which data and other resources and processes are required for implementation. Not the other way around!
• The right people should be involved right from the start: They must adequately represent the depth and breadth of the company.
• Expectations need to be addressed correctly! Exaggerated expectations should be put into perspective early on, while underestimated opportunities should be supported.
• Overall, a reflective facilitation process that is adequate for all stakeholders needs to be ensured.
   

• Which use cases is the company is busy with?
• What target group (internal, external/sales, marketing, production/decision makers, experts) do the use cases refer to?
• What added value do they provide with regard to this target group?
• How do these use cases contribute to the strategic goals of the company?
• How are successfully implemented analytics projects measured?
• Which projects have failed?
• What is the level of maturity? What skills, data, tools, technical expertise does the company have?