How do data-driven, data-based and data-informed decision making differ?

by Ciara Heavin
and Daniel J. Power

Managers are preoccupied with making better use of data – internal organizational data, customer data, supplier data, and market data to name a few major data sources. Major data challenges include the increasing volumes of varied data, mixed data quality, data security, generating insights from data, using analytics better and identifying new opportunities to derive value from existing data. At the heart of these data challenges is the question of how data can be used to help improve managerial and organizational decision making processes and hence business outcomes. Some advocate for data-driven decision making, others for data-based or data-informed decision making. It is important to understand these differing approaches to using data in decision making.

Decision makers are confronted with evolving and expanding data resources and a pressing need to ask better questions about the data to help solve problems. One can read a variety of case studies about using data to make decisions and assist in decision making in many domains including education, retail, healthcare and financial services. Using more data and analytics is often identified as the key to success in these case study situations.

A number of phrases have been used by authors and consultants to describe the increasing use of data to improve decision making in organizations. The word data is modified as data-based, data-driven or data-informed decision making. These buzzwords are often used interchangeably to refer to an improved organizational decision support capability. While the terms are related, there are differences that are important. After reviewing the usage of prior authors, we find it useful to consider each of these concepts mutually exclusive and complementary. For managers to meaningfully engage with an organizations’ data opportunities and challenges they need to understand how these decision making approaches can be formulated, managed and exploited as part of an organizations’ data strategy. Let's examine the three approaches to using data in decision making.

Data-based decision making refers to an ongoing process of collecting and analyzing different types of data to aid in decision making (Power, 2017). Decision are based upon data facts. Education researchers often refer to the need for data-based decision making to inform teaching practices, student intervention processes and resource allocations. In the field of education, the use of summative and formative data means that teachers and parents receive a more holistic view of student performance (Skalski and Romero, 2011). Data-based decision making usually incorporates many diverse data types from a variety of sources including quantitative data balanced with “softer” data that is more descriptive in nature.

The term data-driven decision making or data-driven management is widely used in articles, consultant reports, white papers and more recently in academic research papers to characterize a particular type of decision making. Data-driven decision making refers to the collection and analysis of data to make decisions. Data "drives" the decision making and decisions are made using verifiable data or facts. Provost and Fawcett (2013) define data-driven decision making as “the practice of basing decisions on the analysis of data rather than purely on intuition." Organizations that use “data-driven decision making” are more productive and more profitable than their competitors (McAfee and Brynjolfsson, 2012; Frick, 2014). "Data-driven, data-informed or fact-based decision making means managers use and evaluate data to make decisions. Data is useful, providing more data is not necessarily the way to improve decision making effectiveness."(Power, 2014). The perceived quality of the data influences when and how it is used to drive enterprise decision making. Notably, data driven decision making as a concept is often used in conjunction with big data, data analytics particularly quantitative/statistical analytics. Indeed, data-driven decision support systems (DSS) help managers process "hard" or numeric data.

Data-informed decision making is a term used when data and facts are an influential factor in decision making, but not the only factor. According to Maycotte (2015), decisions are complex phenomena that require significant human input in terms of experience and instinct. Indeed, he believes that decisions should not be purely “driven” by data but data may be used to support experienced decision makers to be faster and more flexible in their decision making. Maycotte advocates that decision makers need to “strike the balance between expertise and understanding information”. The U.S. Department of Education prefers the term data-based or data-informed decision-making over data-driven decision making asserting that decisions should not be based solely on quantitative data.

Using data in decision making should be contingent on the decision situation. Figure 1 "Using Data in Decision Making" suggests a continuum of decision situations ranging from highly structured and routine to highly unstructured and nonroutine. Data-driven decision making can be used effectively in highly structured situations when appropriate data and analytics are available. As a decision situation becomes more unstructured, the best one can do is data-based decision making because more qualitative content and subjective assessment is needed. It is appropriate and recommended to base a decision upon quantitative and qualitative data in these semi-structured decision situations, but other factors like data quality, data relevance and data timeliness must be resolved. Finally, in highly unstructured decision situations the best one can expect is data-informed decision making. Examine available data and try to see how it informs your understanding of the situation. In a highly unstructured decision situation, an effective decision maker needs both knowledge and facts. A subjective assessment and assumption analysis becomes especially important.

Organizations need to develop and embed data processes for collecting, storing, maintaining, and analyzing data to help answer important decision questions. Creating and managing a modern approach to quantitative and qualitative data usage requires a sophisticated information system. Also, a mix of people skills, technologies, and managerial procedures are necessary to operationalize information and adequately support the flow of information to the right people at the right time for data-driven, data-based and data-informed decision making. Using data and information appropriately in decision making is the key to more effective decisions.

using data

References and Resources

Chen, A. "Know the difference between data-informed and versus data-driven". Accessed July 23, 2017 at URL

Frick, W. (2014). "An introduction to data-driven decisions for managers who don’t like math". Harvard Business Review.

Kanter, B. (2013). "Why Data Informed VS Data Driven?" Beth's Blog, Accessed July 23, 2017 at URL

Maycotte, H.O. (2015). "Be Data-Informed, Not Data-Driven, For Now". Accessed July 21, 2017 at URL

McAfee, A. and E. Brynjolfsson (2012). "Big Data: The Management Revolution. Harvard Business Review, October.

Mosseri, A. (2010). Data Informed, Not Data Driven Video. Accessed July 23, 2017 at URL .

Power, D. J. (2017). "What increases success for data-based decision support?" Accessed July 21, 2017 at URL

Power, D. J. (11/09/2014). "What is data-driven decision making?" Decision Support News, Vol. 15, No. 23, Accessed July 21, 2017 at URL

Provost, F. and T. Fawcett, “Data Science and its relationship to Big Data and Data-driven Decision Making,” Big Data, Vol. 1, No. 1, March 2013 at URL .

Skalski, A. K., & Romero, M. (2011). "Data-based decision making". Principal leadership, 11(5), 12-16.

Last update: 2017-07-24 02:50
Author: Daniel Power

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