Data Analytics Project Methodologies: Which one to choose?

Jeroen Baijens, Remko W. Helms, Rob Kusters

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

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Developments in big data have led to an increase in data analytics projects conducted by organizations. Such projects aim to create value by improving decision making or enhancing business processes. However, many data analytics projects still fail to deliver the expected value. The use of process models or methodologies is recommended to increase the success rate of these projects. Nevertheless, organizations are hardly using them because they are considered too rigid and hard to implement. The existing methodologies often do not fit the specific project characteristics. Therefore, this research suggests grouping different project characteristics to identify the most appropriate project methodology for a specific type of project. More specifically, this research provides a structured description that helps to determine what type of project methodology works for different types of data analytics projects. The results of six different case studies show that continuous projects would benefit from an iterative methodology.
Original languageEnglish
Title of host publicationProceedings of the 2020 International Conference on Big Data in Management (ICBDM 2020)
PublisherACM Digital Library
Number of pages7
ISBN (Electronic)9781450375061
ISBN (Print)978-1-4503-7506-1
Publication statusPublished - 15 May 2020
Event2020 International Conference on Big Data Management - The university of Manchester, Manchester, United Kingdom
Duration: 20 Nov 202022 Nov 2020


Conference2020 International Conference on Big Data Management
Abbreviated titleICBDM 2020
Country/TerritoryUnited Kingdom
Internet address


  • data analytics
  • information systems
  • modern technologies allow organizations
  • project characteristics
  • project methodologies
  • to generate collect and
  • Project characteristics
  • Project Methodologies
  • Data Analytics


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