Jeroen Baijens
Remko Helms
ABSTRACT
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.
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Index Terms
- Data Analytics Project Methodologies: Which One to Choose?
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