INFORMS Journal on Data Science

IJDS cover

INFORMS Journal on Data Science (IJDS) is a peer-reviewed journal, aiming to publish top innovative and potentially impactful data science methodologies contributing to decision making in business, management, and industry. By curating and publishing state-of-the-art generalizable knowledge, IJDS provides a dedicated focal point for important data science research in sociotechnical aspects of business, engineering, management, and industry, to benefit the scientific community, industry, and society at large. IJDS strives to create an outlet that is aligned with data science authors’ and readers’ expectations in terms of focus, style, and language. With an emphasis on decision making, IJDS will be an open-minded and inclusive outlet fitting the cross-disciplinary nature of data science. Authors and readers will have backgrounds spanning a range of areas such as statistics, machine learning, operations research, engineering, econometrics, and other computational disciplines.

Our Member Authors have the opportunity to work with INFORMS PR to highlight their work through press releases and media placements.


An IJDS paper will have four ingredients -

  1. Data: real-world or simulated
  2. Models/algorithms: innovative data science methodology (model/algorithm/approach)
  3. Managerial/industrial relevance: decision-making motivation and potential/actual impact
  4. Implications: consideration of relevant practical implications

The INFORMS Journal on Data Science audience includes the data science research community in business schools, industrial engineering departments, and industry research groups.

Frequency: 4 issues/year (quarterly)
ISSN: 2694-4022, eISSN: 2694-4030
First Year Published: 2021

Web Feed: IJDS