Data-Driven Decision Making Task

A Data-Driven Decision Making Task is an evidence-based decision making task that is a data-driven task.

• Context:
  • It can result in a Data-Driven Decision-Making Act.
  • It can be solved by a Data-Driven Decision-Making System (that implements a data-driven decision making algorithm).
• Example(s):
  • a Data-Driven Prediction Task.
  • …
• Counter-Example(s):
  • a Heuristic Decioning Task.
• See: Empirical Experiment, Big Data.

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References

2011

• (Brynjolfsson et al., 2011) ⇒ Erik Brynjolfsson, Lorin Hitt, and Heekyung Kim. (2011). “Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?." Social Science Research Network (SSRN). [http://dx.doi.org/10.2139/ssrn.1819486
  • QUOTE: We examine whether firms that emphasize decision making based on data and business analytics (“data driven decision making” or DDD) show higher performance. Using detailed survey data on the business practices and information technology investments of 179 large publicly traded firms, we find that firms that adopt DDD have output and productivity that is 5-6% higher than what would be expected given their other investments and information technology usage.

2006

• (Marsh et al., 2006) ⇒ Julie A. Marsh, John F. Pane, and Laura S. Hamilton. (2006). “Making sense of data-driven decision making in education." Evidence from Recent RAND Research
  • ABSTRACT: Data-driven decision making (DDDM), applied to student achievement testing data, is a central focus of many school and district reform efforts, in part because of federal and state test-based accountability policies. This paper uses RAND research to show how schools and districts are analyzing achievement test results and other types of data to make decisions to improve student success. It examines DDDM policies and suggests future research in the field. A conceptual framework, adapted from the literature and used to organize the discussion, recognizes that multiple data types (input, outcome, process, and satisfaction data) can inform decisions, and that the presence of raw data does not ensure its effective use. Research questions addressed are: what types of data are administrators and teachers using, and how are they using them; what support is available to help with the use of the data; and what factors influence the use of data for decision making? RAND research suggests that most educators find data useful for informing aspects of their work and that they use data to improve teaching and learning. The first implication of this work is that DDDM does not guarantee effective decision making: having data does not mean that it will be used appropriately or lead to improvements. Second, practitioners and policymakers should promote the use of various data types collected at multiple points in time. Third, equal attention needs to be paid to analyzing data and taking action based on data. Capacity-building efforts may be needed to achieve this goal. Fourth, RAND research raises concerns about the consequences of high-stakes testing and excessive reliance on test data. Fifth, attaching stakes to data such as local progress tests can lead to the same negative practices that appear in high-stakes testing systems. Finally, policymakers seeking to promote educators’ data use should consider giving teachers flexibility to alter instruction based on data analyses. More research is needed on the effects of DDDM on instruction, student achievement, and other outcomes; how the focus on state test results affects the validity of those tests; and the quality of data being examined, the analyses educators are undertaking, and the decisions they are making.

2001

• (Breiman, 2001) ⇒ Leo Breiman. (2001). “Statistical Modeling: The Two Cultures.” In: Quality control and applied statistics, 48(1).
  • QUOTE: There are two cultures in the use of statistical modeling to reach conclusions from data. … If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.

1999

• (Mitchell, 1999) ⇒ Tom M. Mitchell. (1999). “Machine Learning and Data Mining." Communications of the ACM 42, no. 11
  • QUOTE: … organizations and an attendant increase in efforts to capture, warehouse, and utilize historical data to support evidence-based decision making. …

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