Data Analytics and Machine Learning for Information Systems: Mathematical Methods and Applications

Dear Colleagues,

Data has become a cornerstone of modern management information systems, and organizations are increasingly using analytics to extract maximal value from their business processes. Companies now view data as a critical asset driving decision-making, which has led to a surge in adoption of business intelligence, big data analytics, and artificial intelligence (AI) tools in the MIS domain. In the era of big data, MIS researchers and professionals must handle massive, complex datasets and employ sophisticated techniques to glean actionable insights. Effective decision-making often requires intelligent systems capable of rapidly analyzing high-volume and high-variety data. These trends make it imperative to develop and apply robust mathematical and statistical methods to ensure that data-driven insights in MIS are reliable, interpretable, and optimal for guiding business strategies.

This Special Issue centers on the quantitative foundations of data analytics and machine learning in the MIS context. The aim is to showcase research that advances data-driven decision support through novel mathematical models, algorithms, and analytical frameworks. We seek contributions that not only apply existing techniques to MIS problems but also extend the theory and methodology of data analytics or AI in ways that are relevant to information systems. Submissions may include new machine learning algorithms or improvements tailored for business data, statistical models addressing specific MIS challenges, or innovative applications of data science in management contexts—provided they highlight a strong mathematical or quantitative component. Both rigorous theoretical studies and insightful applied research are welcome. By collecting work at the intersection of MIS and advanced analytics, this issue will illustrate how mathematical approaches (from statistical inference to computational learning theory) can enhance decision support, optimize processes, and create business value in information systems.

Topics of Interest

We invite topics including, but not limited to, the following:

• Big Data Analytics in MIS—Mathematical and statistical methods for big data management, descriptive analytics, and pattern discovery in organizational datasets.
• Machine Learning Applications—Supervised, unsupervised, and reinforcement learning techniques applied to MIS problems (e.g., customer relationship management, finance, healthcare information systems), with an emphasis on algorithmic innovation or novel modeling approaches.
• Predictive Modeling and Forecasting—Time-series analysis, predictive analytics, and forecasting models for business intelligence (such as sales forecasting, user behavior prediction, risk assessment) using quantitative techniques.
• Knowledge Discovery and Data Mining—Data mining methods, clustering, and association rule mining in enterprise data, including graph analytics and network science approaches for social networks and knowledge management systems.
• Natural Language and Text Analytics—Text mining, natural language processing (NLP), and sentiment analysis in MIS (for example, analyzing customer feedback, social media data, or enterprise communications) with a focus on the underlying mathematical models (e.g., probabilistic language models, topic modeling).
• Deep Learning and AI in MIS—Development or application of deep learning, neural network models, and AI algorithms in information systems (such as recommendation systems, intelligent decision support, or automation in business processes), highlighting the mathematical aspects of model design and training.
• Statistical and Probabilistic Models—Bayesian methods, hypothesis-driven statistical analysis, and probabilistic graphical models in MIS research (for instance, models for IT investment decisions, market basket analysis, or user behavior analytics).
• Data-Driven Optimization—Prescriptive analytics and optimization techniques driven by data (e.g., optimization of marketing campaigns, resource allocation based on predictive models, adaptive decision-making systems) that illustrate the integration of data analytics with mathematical optimization in MIS.

Dr. Giridhar Reddy Bojja
Dr. Loknath Sai Ambati
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.

• data analytics
• machine learning
• predictive modeling
• forecasting
• big data analytics
• business intelligence
• knowledge discovery
• data mining
• natural language processing
• deep learning
• probabilistic models
• bayesian methods
• data-driven optimization
• decision support systems