[BA/MA] Analysing machine learning platforms for business intelligence (BI)

Introduction

Business analytics and intelligence is an ever-evolving field that holds great promise for improving decision-making and shaping the future of business. It is an interdisciplinary field that uses statistical and mathematical methods to analyse data and make informed business decisions (Vidgen et al. 2017). In recent years, the rapid growth of big data, advances in computing and data storage technologies, and the increasing demand for data-driven decision making have made business analytics a critical component of modern organizations. Machine learning (ML) techniques have have made significant impact on business intelligence software, which is also indicated by the adaption of ML and spawning of new BI-Platforms (Isdahl & Gundersen 2019).

Students in these fields have the opportunity to make significant contributions to ongoing research and development.

Research gap/Problem statement

Artificial intelligence (AI) is a fuzzy term to describe a broad range of technologies. What technologies should be considered AI is an open debate in the public, by practitioners and in academia. It is often unclear to organisations how they can address BI-challenges they face. In addition, ML-platform providers mostly implement their own terminology and use different types of structures to design their software tools.

Subject of the thesis and directions for research

To shed light into the professional application of AI, which is mostly comprised of machine learning techniques in modern times, this research aims to analyse popular ML-platforms. The goal is to extract design elements of AI-projects within these platforms. This not only helps to understand the ways in which AI-based solutions are developed, but also provides insights into the current market of ML-platforms, which is key for organisations undertaking business intelligence efforts.

Requirements

This thesis offering is in the field of information systems and requires business understanding, motivation to learn new applications and methods. Experience with business analytics, intelligence and machine learning is not required, but can be helpful.

Language

The thesis and application material can be submitted in English or German.

Call for action

Please apply to Julius Kirschbaum following the guidelines for thesis applications on our chair’s website.

1. Apply for this thesis by sending an e-mail with a short motivational text, your CV and current transcript to julius.kirschbaum@fau.de
2. Initial meeting to discuss the topic and get to know each other
3. Drafting an exposé [2-4 weeks, registration of thesis after 2 weeks]
   1. Refine the problem statement
   2. Demonstrate the relevance
   3. Find your research question
   4. Build your research design and methodology
4. Feedback meetings with supervisor during development
5. Hand-in your thesis