Big data: An introduction to data-driven decision making

Ekene Nwike Okwechime*, Peter Duncan, David Edgar, Elisabetta Magnaghi, Eleonora Veglianti

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


The purpose of this article is to set the groundwork of data-driven decision making. Currently, there are widespread discussions on how society is shaped and changing due to the increased use of data for decision making in the private and public sector. Central to this form of decision making is big data and open data. We present a critical review of big data: it’s characteristics and sources. We also provide a critical review of open data by delineating its difference to big data, i.e. the types and sources of open data. We argue that if the conditions are right, big data can be open data—and vice versa. Most importantly, we present where and how big data can be used applied in various areas of society, e.g. in smart cities. By carrying out this review, we outline the composition of data and where and how it can be applied in society at large. Ultimately, given the accessibility of data, we critically review a fast-moving ecosystem where end-users and decision makers can be guided by data.

Original languageEnglish
Title of host publicationOrganizing Smart Buildings and Cities: Promoting Innovation and Participation
EditorsElisabetta Magnaghi, Véronique Flambard, Daniela Mancini, Julie Jacques, Nicolas Gouvy
Place of PublicationSwitzerland
PublisherSpringer International
Number of pages12
ISBN (Electronic)9783030606077
ISBN (Print)9783030606060
Publication statusPublished - 2021

Publication series

NameLecture Notes in Information Systems and Organisation
PublisherSpringer International
ISSN (Print)2195-4968


  • big data
  • open data
  • data-driven decision making

ASJC Scopus subject areas

  • Information Systems and Management
  • Information Systems
  • Management of Technology and Innovation
  • Management Information Systems
  • Computer Science Applications


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