TY - CHAP
T1 - Data-Driven Decision-Making
AU - Buijsse, Ronald
AU - Willemsen, Martijn
AU - Snijders, Chris
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-031-19554-9_11
DO - 10.1007/978-3-031-19554-9_11
M3 - Chapter
SN - 978-3-031-19553-2
SN - 978-3-031-19556-3
T3 - Classroom Companion: Business book series (CCB)
SP - 239
EP - 277
BT - Data Science for Entrepreneurship
A2 - Liebregts, Werner
A2 - van den Heuvel, Willem-Jan
A2 - van den Born, Arjan
PB - Springer
CY - Cham
ER -