Data-informed decision-making

Data-informed decision-making (DIDM) gives reference to the collection and analysis of data to guide decisions that improve success.[1] Another form of this process is referred to as data-driven decision-making, "which is defined similarly as making decisions based on hard data as opposed to intuition, observation, or guesswork."[2] DIDM is used in education communities (where data is used with the goal of helping students and improving curricula) and is also used in other fields in which data is used to inform decisions. While "data based decision-making" is a more common term, "data-informed decision-making" is the preferred term, since decisions should not be based solely on quantitative data.[1][3] Data-driven decision-making is commonly used in the context of business growth and entrepreneurship.[4][5] Many educators have access to data system for analyzing student data.[6] These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system, making key package/display and content decisions) to improve the success of educators' data-informed decision-making.[7] In business, fostering and actively supporting data-driven decision-making in their firm and among their colleagues may be one of the central responsibilities of CIOs (Chief Information Officers) or CDOs (Chief Data Officers).[8]

Assessment in higher education is a form of data-driven decision-making aimed at using evidence of what students learn to improve curriculum, student learning, and teaching.[9] Standardized tests, grades, and student work scored by rubrics are forms of student learning outcomes assessment. Formative assessments, specifically, allow educators to use the data from student performances more immediately in modifying teaching and learning strategies. There are numerous organizations aimed at promoting the assessment of student learning through DIDM including the National Institute for Learning Outcomes Assessment, the Association for the Assessment of Student Learning in Higher Education, and, to an extent, the Association of American Colleges and Universities.

References
  1. U.S. Department of Education Office of Planning, Evaluation and Policy Development (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. United States Department of Education (ERIC Document Reproduction Service No. ED504191)
  2. "Data-Driven Decision Making: 5 Essentials". Ohio University. 2019-12-02. Retrieved 2022-02-28.
  3. Knapp, M. S., Swinnerton, J. A., Copland, M. A., & Monpas-Hubar, J. (2006). Data-informed leadership in education. Seattle, WA: Center for the Study of Teaching and Policy.
  4. "Data-Driven Decision Making: A Primer for Beginners". Northeastern University Graduate Programs. 2019-08-22. Retrieved 2022-02-28.
  5. "Three Examples of How Companies Make Data-Driven Decisions". Utica University. 2019-11-25. Retrieved 2022-02-28.
  6. Aarons, D. (2009). Report finds states on course to build pupil-data systems. Education Week, 29(13), 6.
  7. Rankin, J. (2013, March 28). How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help. Archived 2019-03-26 at the Wayback Machine Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.
  8. Delort P. 2012. ICCP Technology Foresight Forum - "Harnessing data as a new source of growth: Big data analytics and policies" . OECD, 2012
  9. Flaherty, Colleen. "Large-Scale Assessment Without Standardized Tests". Inside HigherEd. Retrieved 24 February 2017.
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