12 research outputs found
An Empirical Investigation into the Extent of Green IT Practices in Sri Lanka’s Data Centers: A Case Study Approach
A Locally Isolated Entomopathogenic Fungus to Control Tea Red Spider Mites (<i>Oligonychus coffeae<i> Acarina -Tetranychidae)
Microbial control agent containing a Sri Lankan Bacillus thuringiensis isolate for control of Lepidopteran pests
Effect of temperature on local and Philippine isolates of Metarhizium anisopliae and their virulence on larvae of Oryctes rhinoceros, a pest of coconut in Sri Lanka
Data Analytics Project Methodologies:Which one to choose?
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
