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Business Intelligence and Data Analytics in Enterprise Systems
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Business Intelligence and Data Analytics in Enterprise Systems

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5495

Special Issue Editor

Prof. Dr. Ana Ramona Bologa

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 15-17 Dorobanti Avenue, District 1, 010552 Bucharest, Romania
Interests: business intelligence; business analytics; ERPs; agent base systems

Special Issue Information

Dear Colleagues,

The increasing complexity of enterprise systems, combined with the huge amount of real-time generated data, raises new challenges related to the capacity of organizations to extract value, to support informed decisions, and to adapt in dynamic contexts. In light of these challenges, business intelligence and data analytics are no longer simple analytic tools, but components of a larger adaptive, intelligent digital ecosystem.

This Special Issue of Systems aims to explore the way that business intelligence, supported by recent advances in artificial intelligence and machine learning, transforms enterprise systems’ architecture and functions. We invite researchers, practitioners, and professionals in related fields to contribute their work that approaches theoretical perspectives and practical applications about the integration of  intelligent analytical solutions in complex systems.

This Special Issue encourages contributions that address, but are not limited to, the following topics:

  • Augmented business intelligence and AI application in automated insight generation;
  • BI integration with enterprise information systems (ERP, CRM, SCM, etc.);
  • BI and data analytics as factors of digital transformation and organizational resilience;
  • Using machine learning in predictive and prescriptive analytics in organizations;
  • Process mining and business process intelligence;
  • New data architectures and modeling approaches to support advanced analytics;
  • Modern data infrastructures (data mesh and data fabric) and real-time analysis;
  • Data governance and ethical aspects of AI-based analytics.

The main purpose of this Special Issue is to provide interdisciplinary perspectives that link systems theory, data science, organizational management, and AI technologies in order to find answers to the complex challenges of the real world.

Prof. Dr. Ana Ramona Bologa
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • business intelligence
  • data analytics
  • enterprise systems
  • artificial intelligence
  • machine learning
  • systems thinking
  • process mining
  • digital transformation
  • decision support systems
  • data governance

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

36 pages, 1552 KB  
Article
RO-FIN-LLM: A Benchmark with LLM-as-a-Judge and Human Evaluators for Romanian Tax and Accounting
by Maria-Ecaterina Olariu, Vlad-Gabriel Buinceanu, Cristian Simionescu, Octavian Dospinescu, Răzvan Georgescu, Cezar Tudor, Adrian Iftene and Ana-Maria Bores
Systems 2026, 14(3), 244; https://doi.org/10.3390/systems14030244 - 27 Feb 2026
Viewed by 239
Abstract
Large Language Models (LLMs) are increasingly being adopted in business settings; however, there remains a shortage of evaluation tools that account for country-specific regulations, particularly for Romania’s taxation and financial accounting requirements. RO-FIN-LLM is a benchmark designed to test how well LLMs handle [...] Read more.
Large Language Models (LLMs) are increasingly being adopted in business settings; however, there remains a shortage of evaluation tools that account for country-specific regulations, particularly for Romania’s taxation and financial accounting requirements. RO-FIN-LLM is a benchmark designed to test how well LLMs handle Romania-specific regulatory question answering in taxation (including VAT regimes, income/profit tax, microenterprise rules, and other obligations) and financial accounting (including journal entries/monographs, amortization, provisions, and foreign exchange transactions). The benchmark contains questions curated by experts, each including the applicable regulatory time frames and the legal sources for the answers. Evaluation is performed in two protocols: closed-book and open-book with Retrieval Augmented Generation (RAG), using Tavily Search API. Evaluation metrics are represented by rubrics, namely correctness, legal citation quality, and clarity/structure. A subset of answers produced by three models was additionally evaluated by 12 specialists in the financial-accounting domain. In this revision, we also describe a public release plan for the question schema, prompts, and evaluation scripts to support independent reproducibility. Full article
(This article belongs to the Special Issue Business Intelligence and Data Analytics in Enterprise Systems)
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24 pages, 1129 KB  
Article
From Unstructured Text to Automated Insights: An Explainable AI Approach to Internal Control in Banking Systems
by Ya Liu, Xinqiu Li and Congli Su
Systems 2026, 14(3), 234; https://doi.org/10.3390/systems14030234 - 25 Feb 2026
Viewed by 425
Abstract
The complexity of internal control in commercial banks continues to increase, and relevant reports exhibit notable lag and template issues. In response to the demand to transform unstructured disclosures into actionable insights, this study proposes an “augmented Business Intelligence (BI) framework” that integrates [...] Read more.
The complexity of internal control in commercial banks continues to increase, and relevant reports exhibit notable lag and template issues. In response to the demand to transform unstructured disclosures into actionable insights, this study proposes an “augmented Business Intelligence (BI) framework” that integrates a text-based internal control quality assessment system, a dual-validation process, and the resulting Intelligent Internal Control Decision Support System (IIC-DSS). By combining large language models and neural-symbolic models of regulatory prototypes, a quality evaluation system for internal control based on complex text is constructed using a mixed probability mechanism to reduce interference from defensive disclosures. A dual validation process is designed with Partial Least Squares Structural Equation Modeling (PLS-SEM). PLS-SEM verification confirms the construct validity of this evaluation system, while XGBoost verification indicates that internal control quality has incremental predictive ability for asset quality deterioration. The IIC-DSS uses SHapley Additive exPlanations (SHAP) to explain XGBoost outputs, quantifying the marginal contribution of each control factor to the predicted risk. Overall, this study advances internal-control measurement by establishing a neural-symbolic, text-to-indicator representation within an augmented BI architecture and empirically demonstrating its utility in improving predictive power for bank asset quality deterioration and in enhancing decision transparency via explainable AI. Full article
(This article belongs to the Special Issue Business Intelligence and Data Analytics in Enterprise Systems)
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28 pages, 4659 KB  
Article
A Comprehensive Business Intelligence Framework for Diabetes Management in Telemedicine: Advancing Data-Driven Decision Support Through Integrated Visualization and Predictive Analytics
by Emilia-Alexandra Pop, Gabriela Mircea and Claudia-Roxana-Maria Iliescu
Systems 2026, 14(2), 155; https://doi.org/10.3390/systems14020155 - 31 Jan 2026
Viewed by 433
Abstract
Modern telemedicine requires advanced analytical solutions for efficient management of chronic diseases. This study presents the development of a comprehensive business intelligence (BI) framework using Microsoft Power BI, applied to the optimization of diabetes mellitus management. The methodology integrates Power Query transformations, 35 [...] Read more.
Modern telemedicine requires advanced analytical solutions for efficient management of chronic diseases. This study presents the development of a comprehensive business intelligence (BI) framework using Microsoft Power BI, applied to the optimization of diabetes mellitus management. The methodology integrates Power Query transformations, 35 DAX measures organized into five functional categories, and Python 3.14.2. capabilities for advanced statistical analysis. The framework was implemented and demonstrated using a public clinical dataset of 100,000 patient records, generating five interactive dashboards covering epidemiological, demographic, clinical, geographical, and equity perspectives. A global prevalence of 8.5%, exponential growth with age, gender differences (9.75% males against 7.62% females), and substantial connections between metabolic indicators (BMI, HbA1c, and blood glucose) are all confirmed by the results. Heart disease rates are 6.2 times higher in diabetic people, according to comorbidity research. Complete methodological openness through thorough documentation, Python integration for sophisticated visualizations, and interactive multidimensional drill-down features are some of the major additions. The predictive elements are included as interpretable, exploratory components embedded in the BI environment rather than as clinically validated prediction models. This approach provides an affordable and user-friendly approach that makes advanced analytical capabilities accessible to a broader range of healthcare organizations managing chronic diseases. Full article
(This article belongs to the Special Issue Business Intelligence and Data Analytics in Enterprise Systems)
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20 pages, 1324 KB  
Article
Integrating Analyst-Forecasting Indicators into Business Intelligence Systems for Data-Driven Financial Distress Prediction
by Zhenkun Liu, Mu Wang, Dansheng Liu, Zhiyuan Du, Lifang Zhang and Jianzhou Wang
Systems 2026, 14(1), 29; https://doi.org/10.3390/systems14010029 - 26 Dec 2025
Viewed by 525
Abstract
Predictive analytics for financial distress plays an important role in enterprise risk management and everyday business decisions. Most past studies mainly use accounting indicators that come from standard financial reports. This study adds analyst-forecast financial indicators and places them in a data-driven business [...] Read more.
Predictive analytics for financial distress plays an important role in enterprise risk management and everyday business decisions. Most past studies mainly use accounting indicators that come from standard financial reports. This study adds analyst-forecast financial indicators and places them in a data-driven business intelligence setup to improve how companies predict financial distress. We work with seven real datasets to test several predictive models and run statistical checks to see how analyst forecasts work with historical financial data. The results show that analyst-forecast indicators can clearly improve prediction accuracy and make the results easier to understand. From an enterprise systems view, this study pushes traditional financial distress prediction toward a smarter analytics setup that supports real-time, explainable, and data-based risk assessment. The findings provide useful ideas for both the theory and practice of designing business intelligence systems and financial decision-support tools for companies. Full article
(This article belongs to the Special Issue Business Intelligence and Data Analytics in Enterprise Systems)
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27 pages, 1118 KB  
Article
Enabling Intelligent Data Modeling with AI for Business Intelligence and Data Warehousing: A Data Vault Case Study
by Andreea Vines, Ana-Ramona Bologa and Andreea-Izabela Bostan
Systems 2025, 13(9), 811; https://doi.org/10.3390/systems13090811 - 16 Sep 2025
Cited by 1 | Viewed by 2089
Abstract
This study explores the innovative application of Artificial Intelligence (AI) in transforming data engineering practices, with a specific focus on optimizing data modeling and data warehouse automation for Business Intelligence (BI) systems. The proposed framework automates the creation of Data Vault models directly [...] Read more.
This study explores the innovative application of Artificial Intelligence (AI) in transforming data engineering practices, with a specific focus on optimizing data modeling and data warehouse automation for Business Intelligence (BI) systems. The proposed framework automates the creation of Data Vault models directly from raw source tables by leveraging the advanced capabilities of Large Language Models (LLMs). The approach involves multiple iterations and uses a set of LLMs from various providers to improve accuracy and adaptability. These models identify relevant entities, relationships, and historical attributes by analyzing the metadata, schema structures, and contextual relationships embedded within the source data. To ensure the generated models are valid and reliable, the study introduces a rigorous validation methodology that combines syntactic, structural, and semantic evaluations into a single comprehensive validity coefficient. This metric provides a quantifiable measure of model quality, facilitating both automated evaluation and human understanding. Through iterative refinement and multi-model experimentation, the system significantly reduces manual modeling efforts, enhances consistency, and accelerates the data warehouse development lifecycle. This exploration serves as a foundational step toward understanding the broader implications of AI-driven automation in advancing the state of modern Big Data warehousing and analytics. Full article
(This article belongs to the Special Issue Business Intelligence and Data Analytics in Enterprise Systems)
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