What-if Analysis for Business Professionals: Current Practices and Future Opportunities
Name
3706598.3713672.pdf
Size
1.54 MB
Format
Adobe PDF
Checksum (MD5)
f12f2fa5ff1b431605754ff5d7bc09c2
Author(s)
Gathani, Sneha
Liu, Zhicheng
Haas, Peter J.
Demiralp, ?a?atay
Date Issued
April 25, 2025
Publisher
ACM|CHI Conference on Human Factors in Computing Systems
Citation
Sneha Gathani, Zhicheng Liu, Peter J. Haas, and Çağatay Demiralp. 2025. What-if Analysis for Business Professionals: Current Practices and Future Opportunities. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 973, 1–17.
Version
Final published version
Abstract
What-if analysis (WIA) is essential for data-driven decision-making, allowing users to assess how changes in variables impact outcomes and explore alternative scenarios. Existing WIA research primarily supports the workflows of data scientists and analysts, and largely overlooks business professionals who engage in WIA through non-technical means. To bridge this gap, we conduct a two-part user study with 22 business professionals across marketing, sales, product, and operations roles. The first study examines their existing WIA practices, tools, and challenges. Findings reveal that business professionals perform many WIA techniques independently using rudimentary tools due to various constraints. We then implement representative WIA techniques in a visual analytics prototype and use it as a probe to conduct a follow-up study evaluating business professionals’ practical use of the techniques. Results show that these techniques improve decision-making efficiency and confidence while underscoring the need for better support in data preparation, risk assessment, and domain knowledge integration. Finally, we offer design recommendations to enhance future business analytics systems.
Description
CHI ’25, Yokohama, Japan
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3706598.3713672
