What-if Analysis for Business Professionals: Current Practices and Future Opportunities
Sneha Gathani, Zhicheng Liu, Peter J. Haas, Çagatay Demiralp
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 work"ows of data scientists and analysts, and largely overlooks business professionals who engage in WIA through nontechnical means. To bridge this gap, we conduct a two-part user study with 22 business professionals across marketing, sales, product, and operations roles. The #rst 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 e$ciency and con-#dence while underscoring the need for better support in data preparation, risk assessment, and domain knowledge integration. Finally, we o%er design recommendations to enhance future business analytics systems.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 3135b11a-d64b-42ed-9769-82100101e4e0Builds on16
- Manipulating and Measuring Model InterpretabilityForough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan et al.CHI 2021 · 663 citations
- Boba: Authoring and Visualizing Multiverse AnalysesYang Liu, Alex Kale, Tim Althoff, Jeffrey HeerIEEE VIS 2020 · 79 citations
- Untidy Data: The Unreasonable Effectiveness of TablesLyn Bartram, Michael Correll, Melanie ToryIEEE VIS 2021 · 56 citations
- A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?Evanthia Dimara, John T. StaskoIEEE VIS 2021 · 56 citations
- "Look! It's a Computer Program! It's an Algorithm! It's AI!": Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?Markus Langer, Tim Hunsicker, Tina Feldkamp, Cornelius J. König et al.CHI 2022 · 52 citations
Related papers
- Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the LoopAnamaria Crisan, Brittany Fiore-GartlandCHI 2021 · 52 citations
- A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and ApplicationsXiao Xie, Fan Du, Yingcai WuIEEE VIS 2020 · 47 citations
- Comparison Conundrum and the Chamber of Visualizations: An Exploration of How Language Influences Visual DesignAimen Gaba, Vidya Setlur, Arjun Srinivasan, Jane Hoffswell et al.IEEE VIS 2022 · 16 citations
- Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning PipelinesStefan Grafberger, Paul Groth, Sebastian SchelterSIGMOD 2023 · 18 citations
- Here's what you need to know about my data: Exploring Expert Knowledge's Role in Data AnalysisHaihan Lin, Maxim Lisnic, Derya Akbaba, Miriah Meyer et al.IEEE VIS 2025 · 1 citation
