Practical example
Data Governance and Data Analytics Functions – Introduction and Implementation
Background
An increasing number of vehicles in the automotive and commercial vehicle industries are fully connected. This also significantly increases the potential availability of data in this sector. In the development sector in particular, analytics can support product design and optimization, as well as early fault detection to ensure vehicle safety.
However, traditional organizational structures in development departments are rarely designed to make optimal use of the data generated. To fully realize the potential,
- generated the correct data,
- Data analysis widely available,
- improved analytical skills and
- the data is linked together.
It is important to distinguish between activities that should be centrally managed and executed and those that benefit from greater efficiency through flexibility and cross-functional collaboration. A central analytics function can be useful for managing the analytics landscape, creating connections between analytics units, and building cross-functional expertise. In addition, a data governance function is needed to ensure data transparency and compliance, data quality, data acquisition, and ultimately data usage.
Specific task
Development and implementation of an organizational framework for data governance and data analytics functions within the development division, as well as oversight of the implementation process.
KBC's Approach
A prerequisite for a flexible yet structured organizational framework for data collection and processing is comprehensive transparency regarding the individual analytical units and how they function in practice. As part of the project, we worked with the relevant departments to identify areas that require standardized procedures in order to avoid duplication of effort and generate synergies.
We then worked with the analytics teams to identify relevant use cases for ensuring data availability and analysis quality. As part of this process, we developed three organizational scenarios—including the necessary tasks and responsibilities—for consideration, detailed the organizational structure, defined the collaboration model, and determined staffing requirements. The establishment of a data governance and data analytics function was an integral part of these scenarios.
As part of this process, we identified and described the necessary tasks and responsibilities of the data governance and data analytics functions, developed accountability frameworks, and defined the relevant interfaces.
Based on this, we developed three organizational scenarios for consideration, which outline the organizational structure in detail, define the collaboration model, and determine staffing requirements.
Finally, after the proposal was presented to the executive board and a decision was made on an organizational plan, we supported our client in expanding their“Data Analytics”division.
