@inproceedings{27df3205d8aa4b68813468e95739b671,
title = "Enhancing business dashboards with explanatory analytics",
abstract = "Business dashboards are a popular tool for descriptive analytics, significantly aiding the decision-making process in business and management. This study examines the possibility of enhancing data analytics in business dashboards with AI integration. With the growth in data volume and complexity, explanatory models for automatic diagnostic analytics have been developed. We extend the explanation formalism by Daniels and Feelders (2001) by employing Generative AI to transform the explanatory analytics process from manual data aggregation to conversation analysis. The prototype, Explanatory Text Generator, handles both descriptive and diagnostic questions from users and provides human-digestible answers. It is evaluated using a cognitive walkthrough of a case study related to business accounting, demonstrating its capability to transform data figures into a comprehensible text format. This study contributes to the fields of information management and business intelligence by presenting a human-centric design combining existing explanatory models with AI. This approach bridges the gap between data analysis and decision-making, providing comprehensible explanations of data trends and anomalies.",
keywords = "Business Intelligence, Generative AI, Business dashboards, Explanatory analytics, Descriptive analytics",
author = "Emiel Caron and Sam Tran",
year = "2026",
month = may,
day = "1",
language = "English",
isbn = "978-3-032-16233-5",
volume = "571",
series = "Lecture Notes in Business Information Processing (LNBIP)",
publisher = "Springer Cham",
pages = "18--34",
editor = "\{Barateiro \}, \{Jos{\'e} \} and Rivkin, \{Andrey \} and Zdravkovic, \{Jelena \} and \{Borbinha \}, \{Jos{\'e} \} and \{Mira da Silva\}, \{Miguel \}",
booktitle = "Enterprise Design, Operations, and Computing",
}