Business Analytics Talks
The primary goal of business analytics is to translate data into value for individuals, companies, and organizations. To achieve this, business analytics covers the entire pipeline from data generation, collection, and management over feature engineering and data preprocessing to machine learning, derivation of decisions, and communication of results. Over the last decade, a large share of research in business analytics has provided us with innovations in machine learning. However, machine learning often represents a tiny part of the entire pipeline, whereas other parts can be similar or even more decisive in successfully implementing a business analytics project. To address this gap, we offer the track Business Analytics Talks to provide the opportunity to discuss crucial aspects along the entire pipeline of business analytics projects.
We invite the submission of the following two contribution formats:
- Business Analytics Tweak Report: We invite submissions that describe ideas in the entire business analytics pipeline that empirically showed success in the past and are worth discussing in theory. It is expected that the ideas have already undergone an initial (preliminary) evaluation, but the evaluations do not have to be fully completed. The authors are encouraged to report and discuss initial evaluation results.
- Business Analytics Pitfall Report: We also welcome submissions describing ideas from the past that resulted in negative results but are still worth discussing in theory. It is expected that the authors clearly describe the reasons or potential sources for the negative results. The goal here is to provide a venue for such research that is of interest to the broader community but might otherwise go unpublished. We believe that such research could be incredibly useful in dissuading other researchers or practitioners from pursuing similar, ultimately unsuccessful ideas.
For both contribution formats, submissions will be evaluated based on their potential contribution for practice and research, as well as their potential for inspiring discussions.
The submission may be written in English or German and must be prepared as extended abstracts that follow the standard submission template (see https://www.wi22.de/call-for-papers/). The contributions must be preceded by an abstract of max. 150 words and three to five keywords and must not exceed the limit of four pages, including everything (that is, abstract and references).
The contributions must be submitted exclusively via the conference online review system (https://www.conftool.pro/wi22/) in the following formats:
- First submission (anonymous version for review): pdf document
- Final submission (print-ready version): pdf-document
Accepted papers will be published in the AIS Electronic Library.
Accepted contributions will be presented in a separate “Business Analytics Talks” session as 30-minute talks. The first 15 minutes are reserved for a presentation in each talk and the last for a discussion. The presentation should be prepared in English. The main idea of this format is to encourage researchers and practitioners to discuss their experience from practice and exchange knowledge that leads to successful business analytics projects.
Potential research topics
Research from both contribution formats can cover topics from various business contexts. Exemplary, but not exclusive topics of focus include:
- the generation, collection, management, representation, and visualization of data,
- applications of descriptive, predictive, and prescriptive analytics,
- advancements in statistical and machine learning techniques,
- techniques of communicating results from business analytics to management,
- techniques of managing business analytics projects,
- ethical and legal aspects of business analytics.
Schedule for the review process
The submission deadline and final decision is aligned with the dates of the regular, scientific tracks:
|Last opportunity for submission (submission deadline)|
|Fast and constructive AE feedback|
|Submission of reviewer opinions|
|Submission of AE reports|
|Decision of the track chairs|
|Information to authors|
||Submission of revised papers|
|Final decision and information to authors|
Prof. Dr. Mathias Kraus
Friedrich-Alexander Universität Erlangen-Nürnberg
Mathias Kraus is Assistant Professor for Data Analytics at Friedrich-Alexander University Erlangen-Nuremberg. The main objective of his research is to translate health data into better treatment decisions through the use of data analytics. For this purpose, he develops innovative methodology from the fields of statistics, machine learning and big data in order to contribute to further research developments in data-driven decision support across various tasks in health management.
Friedrich-Alexander Universität Erlangen-Nürnberg
Sven Weinzierl is currently a researcher and PhD candidate at the Chair of Digital Industrial Service Systems, Friedrich-Alexander University Erlangen-Nuremberg. His research interests focus on data-driven decision support in organizations. This includes the design and use of innovative machine learning and deep learning solutions, with a special focus on different tasks in data-driven business process management.
- Nicolas Banholzer (ETH Zürich)
- Fabian Giesecke (Westfälische Wilhelms-Universität Münster)
- Gunther Gust (Albert-Ludwigs-Universität Freiburg)
- Nikolai Stein (Julius-Maximilians-Universität Würzburg)
- Matthias Stierle (Friedrich-Alexander Universität Erlangen-Nürnberg)
- Patrick Zschech (Friedrich-Alexander Universität Erlangen-Nürnberg)