Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Identification of Critical Success Factors in Data Analytics Projects
Download
Identification_of_Critical_Success_Factors_in_Data_Analytics_Projecs.pdf
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
25
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
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
N. Demir, “Identification of Critical Success Factors in Data Analytics Projects,” M.S. - Master of Science, Middle East Technical University, 2024.