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Self-Service Business Intelligence and Analytics (Robert Bosch GmbH)

Self-Service Business Intelligence and Analytics (Robert Bosch GmbH)

Sven Michalczyk

Project Group:

Intelligent Enterprise Systems


Robert Bosch GmbH

Self-Service Business Intelligence and Analytics – Enables Users to Prepare, Analyze and Consume Data on their Own, with Nominal IT Support.

Short description

This project has the objective to develop new concepts and design principles for Self-Service Business Intelligence and Analytics (SSBIA) in cooperation with the Robert Bosch GmbH, Corporate Sector Information Systems and Services. SSBIA refers to a set of tools which encourages users to prepare, analyze, and consume data on their own, with nominal IT support (based on Gartner IT Glossary, 2019 and Lennerholt et al., 2018). The concepts and design principles will be created, implemented, and refined by going iteratively through several design cycles by applying design science research. In the focus of each cycle is the artifact, which can be mostly understood as a software-based prototype. The artifact will be created utilizing real-world data and expertise from the industry partner.

Problem space

With the advancing field of machine learning, there exists a need to apply extended analytical functions to data, such as building a classifier to automate tasks or cluster data to unlock hidden data patterns. However, the IT infrastructure of most mid- to big sized companies has been grown over time. Representatives have to deal with a set of reporting systems which typically miss relevant functionalities users actually need to execute their tasks effectively and efficiently. For example, users still spend a lot of time and effort to get the required data out of reporting systems, clean or integrate data, or are forced to rely on diverse tools and varying formats to create a report. This happens because it is challenging to offer and support adequate self-service capabilities for corresponding user roles, competencies and underlying tasks. But not only company representatives have to deal with data. Nowadays, even end-customers get access to data and require self-service analytical functions. For instance, eBike customers wish to know the next charging station on their tour based on the kilometer range or to have a prediction for the date of the next service checkup based on their cycling behavior.

Project objective

The main objective of the project is to develop a system for SSBIA which supports users going through the stages of the Data Science Lifecycle (e.g., data preparation, data modeling). Hereby, this system shall enable users to conduct analytical functions without the need for coding or setting up a compelling IT infrastructure upfront. Furthermore, as knowledge is required to apply analytical functions, the system shall give clear guidance based on expert knowledge to the user.

To build and evaluate the system the project pursues three sub-objectives:

  1. Investigate user roles, competencies and corresponding tasks starting with end-customers going to the business users and ending with engineers and developers in the SSBIA context.
  2. Harmonize different Data Science Lifecycle models in literature and classes of tools that are available in the industry.
  3. Define a measurement framework for SSBIA to evaluate the artifact(s).

Based on the main objective, the following research question can be formulated:

How to design a SSBIA system in order to help users leveraging analytical functions to improve decision-making?

Expected Result

In summary, the design principles will be derived to extend existing Business Intelligence & Analytics Systems by making them “Self-Service”. In the research process, several artifacts will be created which serve as an instantiation of the design principles making the research itself observable, understandable and applied. The major outcome of the research will be a system, which gives clear guidance in all phases of the Data Science Lifecycle by considering the users itself.

Example Artifact: Tailend Analytics

A high situated artifact for supply based reduction in the context of Self-Service Analytics. Sven Michalczyk has implemented this R-Shiny Application in his master thesis conducted together with the Global Indirect Purchasing Department at the Robert Bosch Bosch GmbH. The Tailend Analytics has the objective to enable buyers on any hierarchical level to first understood and second reduce the number of suppliers in their responsible material field significantly.

View 1 serves mainly Corporate Lead Buyer material fields and regions where they should start their optimization activities.

View 2 targets buyers on an operational level who want to inspect reallocations on item-level per supplier.


The project is third-party funded by the Corporate Sector Information Systems and Services at the Robert Bosch GmbH.


  • Gartner IT Glossary, 2019.
  • Lennerholt, C., Van Laere, J., and Söderström, E. 2018. “Implementation Challenges of Self Service Business Intelligence: A Literature Review,” in Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS), pp. 5055–5063