"Data make a lot of work" - do you feel the same way?

Data-driven companies make better decisions and thus improve their business success. However, companies face major challenges when it comes to mastering and utilizing data.

Many data initiatives fail due to a lack of synchronization between business, operational and technical controls. Our Data-to-Value Framework harmonizes the three levels of control of data transformation to systematically deepen and broaden the use of data. In this way, data becomes value drivers.

The poster of mm1 enables the classification into defined levels of utilization and shows methodical approaches for all three control levels of a data transformation:

• Data strategy: The data strategy is the business control level of Date-to-Value. It ensures that the business benefit of data is understood and systematically pursued within the company.
• Information governance: Information governance is the operational control level of Data-to-Value. It is the central element of every data transformation, as it ensures the permanent synchronization and mapping of the real in the digital world.
• System architecture: The system architecture is the technical control level of Data-to-Value. At this level, an efficient system landscape is created through reference architectures, IT standards, guidelines, development plans and roadmaps. The system architecture level enables targeted data transformation.    
• Degree of utilization: The targeted degree of utilization defines the level of ambition across all control levels.