An autoethnography of trusted data governance with a focus on food data

dc.check.embargoformatEmbargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo)en
dc.check.infoNot applicableen
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dc.contributor.advisorFeller, Josephen
dc.contributor.advisorSammon, Daviden
dc.contributor.authorCostello, Jim
dc.date.accessioned2018-09-21T09:51:17Z
dc.date.available2018-09-21T09:51:17Z
dc.date.issued2018
dc.date.submitted2018
dc.description.abstractTrusted data is today as topical as it is elusive. Data governance is, or should be the guide to trusted data. However, as the world of data grows at an unprecedented rate, the clarity on its accuracy, appropriateness and authority remains a constant challenge for most users. Research suggests that just 3% of firms have confidence that their data meets basic quality standards. Some frameworks exist for data governance but this study expands beyond the boundaries of those models to include the data community, the data governance processes and the evolving technology governance. It then presents a novel and comprehensive framework for trusted data governance emerging from a food sector research case. Irish produced food, mainly dairy products, beef and lamb and its related consumer products, is amongst the premium food brands in the world and is growing every year to meet the demands of a global population which continues to grow and demand safe and quality food. Ours was a sunset industry from the darkest days of the famine era in the 19th century when our farmers could not produce what our population needed to survive, to a supplier to Europe at war in the early 20th century and primitive production and food chain systems in post independent Ireland from the 1920’s to the 1970’s when Ireland joined the European Union. Now Ireland produces over twelve times what our population needs. The industry is worth over €25 billion annually to the economy, we export €11 billion and the industry employs 230,000 people on the approximately 140,000 farms and the related service industries around it. The average farm size is just 32.5 hectares but it is now a modern food eco-system with some of the leading practices in the world and a leader in sustainable grass based production systems providing high quality, sustainable and tasty produce. At the heart of this great growth story is a well-run and managed industry that depends on data to promote and protect the industry. I ran the company, SWS that helped to build many of these data systems over the last twenty years. This thesis presents an autoethnography of my experience in SWS focusing on how these trusted data systems evolved over the twenty-year period. The research method is underpinned by a strong methods paper in Chapter 2. Chapters 3, 4 and 5 take us through a people, process and technology perspective on the evolution of these systems as Chapter 3 examines the community governance, Chapter 4 researches the data governance and Chapter 5 studies the technology evolution over the programme. Each of these chapters presents a number of significant research contributions. To conclude, Chapter 6 brings the research together and proposes a “New Framework for Trusted Data Governance”.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationCostello, J. 2018. An autoethnography of trusted data governance with a focus on food data. PhD Thesis, University College Cork.en
dc.identifier.endpage245en
dc.identifier.urihttps://hdl.handle.net/10468/6861
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2018, Jim Costello.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectData governanceen
dc.subjectTrusted dataen
dc.subjectFood dataen
dc.subjectFood integrityen
dc.subjectTraceabilityen
dc.subjectGovernanceen
dc.thesis.opt-outtrue
dc.titleAn autoethnography of trusted data governance with a focus on food dataen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhDen
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