Critical success factors for data governance: a theory building approach

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dc.contributor.advisorSammon, Daviden
dc.contributor.advisorDaly, Maryen
dc.contributor.authorAlhassan, Ibrahim
dc.contributor.funderSaudi Electronic Universityen
dc.date.accessioned2018-06-22T10:47:00Z
dc.date.issued2018
dc.date.submitted2018
dc.description.abstractThinking about data strategically is a challenge for many organisations today. Governing data has become vital in running a business successfully. In recent years, the volume of data used within organisations has increased dramatically, playing a critical role in business operations. The implementation of data governance remains problematic for the majority of organisations. Data governance is considered to be a relatively emerging subject, and several researchers have proposed different models that help in understanding the concepts related to it. Reviewing the literature, however, reveals a lack of research into the critical success factors (CSFs) for data governance, which shows a need for further studies aimed at understanding the success factors in governing an organisation’s data. This research study aims to identify the critical success factors for data governance that enable organisations to introduce an effective data governance programme. The research follows the building theory from case studies approach by conducting two in-depth case studies in Saudi Arabia. To gather the data, a CSF approach is employed in order to conduct the interviews. The data are then analysed using open, axial, and selective coding techniques in order to inductively identify the CSFs for data governance along with the recommended actions associated with each CSF. This study contributes to data governance research by providing nine CSFs for data governance, as well as identifying a list of recommended actions for putting the CSFs into practice. In addition, follow a rigorous inductive research approach, two theoretical models emerged: 1) a data governance activities model, which helps in better understanding the activities related to data governance that are reported in the literature; and 2) an open, axial, and selective coding framework, which helps in understanding how to use these coding techniques when analysing qualitative data.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAlhassan, I. 2018. Critical success factors for data governance: a theory building approach. PhD Thesis, University College Cork.en
dc.identifier.endpage234en
dc.identifier.urihttps://hdl.handle.net/10468/6377
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2018, Ibrahim Alhassan.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectData governanceen
dc.subjectCritical success factorsen
dc.thesis.opt-outfalse
dc.titleCritical success factors for data governance: a theory building approachen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhDen
ucc.workflow.supervisordsammon@ucc.ie
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