Identification of Critical Success Factors in Data Analytics Projects

Date

2024-9-5

Author

Demir, Nisa

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

50
views

0
downloads

Cite This

Various data analytics applications are increasingly used by organizations to extract insights from data. There are numerous studies exploring the critical success factors (CSFs) in different data analytics fields including Business Intelligence, Artificial Intelligence, Machine Learning, Data Science, and Big Data. Despite the extensive body of research, there remains a gap in identifying a structured CSF list that offers a holistic view across all these fields. This thesis addresses this gap by identifying and categorizing CSFs in data analytics projects through a systematic literature review and semi-structured interviews. Initially, 50 CSFs were identified from existing literature and categorized into second-order themes and six aggregated codes: Technology & Data, Strategy, People, Organizational Culture, Process Design, and External. To validate and refine this list, 20 in-depth interviews were conducted with experts in data analytics. These experts evaluated the identified CSF list and suggested modifications. The prioritization of the factors based on specific tailoring criteria is also investigated and discussed during the interviews. This research contributes to both academic literature and industry by offering a refined and validated list of CSFs that can guide the successful implementation of data analytics projects in various organizational contexts. The implications for theory and practice, along with suggestions for future research, are also discussed.

Subject Keywords

Critical Success Factors, CSFs, Data Analytics

URI

https://hdl.handle.net/11511/111433

Collections

Graduate School of Informatics, Thesis