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Practical example

Establishment of a company-wide data analytics network

To ensure the sustainable management and consolidation of all analysis requests related to vehicle data, establishing a central point of contact was essential. This measure is designed to meet the growing demand for structured analyses in E/E development. By creating a central analytics service desk, processes can be optimized and efficiency within the departments can be improved.

Background

In the past, the large number of duplicate test requests led to inefficiencies and confusion in the workflow. Employees turned to various departments without a clear process structure in place. This not only reduced transparency regarding the availability of analyses but also led to insufficient use of existing analysis results. To address these challenges, a centralized approach was needed to ensure the availability of and access to relevant data and analyses.

Specific task

The main objective of the project was to develop a clearly structured process for handling analysis requests that would enable the distribution and prioritization of these requests. In addition, workshops were held to develop a common model for processing and evaluating the requests. Guidelines were also created, and a roadmap was developed to increase awareness of the Analytics Service Desk. 

KBC's Approach

A proactive approach was adopted for the development and implementation of the central analytics service desks. In a series of networking workshops, the relevant analysis units were identified to define clear communication and work processes. This ensured that analysis requests are not only processed efficiently but also consolidated. The central point of contact serves as an interface between the business departments and enables a more transparent use of analyses and data analytics expertise.

Impact achieved

A total of 11 analytics hubs were integrated with the Analytics Service Desk. During the pilot phase, 47 analytics requests were received, leading to a significant increase in the transparency and quality of data analysis. Employees now have clear access to relevant data and can process requests in a targeted and efficient manner. This has improved not only the effectiveness of processes but also decision-making within the company. In addition, analysis results are incorporated more effectively into key decisions, leading to a better understanding of vehicle data and its impact on customer behavior.