Data-Driven Decision-Making

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One of the most important prerequisites for creating impact with data science is the embedding of data science results in decision-making. One could say that for securing data science impact, data science should start and end with an extensive analysis of the related decision-making. The full embedding of data science in decision-making is often labeled data-driven decision-making (DDDM). This includes the use of data and data science concepts in preparing, processing, executing, and evaluating decisions. In this chapter, we describe the most relevant characteristics of decision-making, which are related to the need for, the form of, and the use of DDDM. Furthermore, we define DDDM, we discuss the most important reasons for applying DDDM, and we introduce the available concepts for the use of DDDM in programmed and nonprogrammed decision-making. We also include a brief description of the link between DDDM and successful data entrepreneurship. We conclude by listing some topics for discussion and further research.
Original languageEnglish
Title of host publicationData Science for Entrepreneurship
Subtitle of host publicationPrinciples and Methods for Data Engineering, Analytics, Entrepreneurship, and the Society
EditorsWerner Liebregts, Willem-Jan van den Heuvel, Arjan van den Born
Place of PublicationCham
Number of pages39
ISBN (Electronic)978-3-031-19554-9
ISBN (Print)978-3-031-19553-2, 978-3-031-19556-3
Publication statusPublished - 2023

Publication series

Name Classroom Companion: Business book series (CCB)


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