From Intuition to Algorithms: Re-Inventing Management in the Age of Big Data

Dear Colleagues,

The rapid advancement of big data technologies and data-intensive methodologies is fundamentally transforming management and decision-making processes across private, public, and hybrid organizations. Managerial practices are increasingly evolving from experience-based intuition toward algorithmic, data-driven, and evidence-based approaches, driven by the growing availability of large-scale, heterogeneous, and high-velocity datasets.

This Special Issue aims to provide an international scholarly platform for high-quality research focusing on datasets, data-driven methodologies, and data-related processes that support modern management, strategic decision-making, and organizational governance. In line with the journal Data, the emphasis is placed on the description, collection, processing, analysis, management, and application of research and experimental data in managerial and organizational contexts.

This Special Issue welcomes contributions that present novel datasets, data acquisition and data processing methodologies, and advanced data analytics techniques (e.g., descriptive, predictive, and prescriptive analytics) applied to management and organizational decision-making. Particular attention is given to reproducible data-driven methods, transparent data workflows, and well-documented experimental or empirical data.

Topics of interest include, but are not limited to:

• Datasets and data descriptions related to management, organizations, and business processes;
• Data collection, data acquisition, and data preprocessing methods for managerial and organizational data;
• Data processing, data analysis, and data modeling techniques for decision support systems;
• Predictive and prescriptive analytics based on empirical and experimental data;
• Research data and experimental data in business, economics, and organizational studies;
• Data management systems, data curation, data integrity, and data governance in organizational contexts;
• Applications of data analytics and artificial intelligence based on well-defined datasets;
• Ethical, security, and governance challenges in the use and management of large-scale organizational data.

Interdisciplinary contributions, empirical studies, case-based datasets, and reproducible data-centric frameworks are particularly encouraged.

By combining selected contributions inspired by the International Conference on Modern Management based on Big Data (MMBD2026) with open submissions from the broader research community, this Special Issue seeks to advance scientific knowledge on data-centric management research and to promote high-quality data sharing, transparency, and reuse in management and organizational studies.

Dr. Otmane Azeroual
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data 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 1600 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.

• big data analytics
• data collection and processing
• data analysis
• management datasets
• decision support data
• predictive analytics
• modern management
• data-driven decision making
• business intelligence
• artificial intelligence in management
• digital transformation
• decision support systems
• data governance
• predictive analytics
• smart organizations
• experimental and empirical data