Datakvalitet

Datakvalitet refererer til kvalitative eller kvantitative egenskaber ved information. Der er mange definitioner af datakvalitet, men data bliver generelt betragtet som at have høj kvalitet hvis det "egner sig til den tilsigtede brug i drift, beslutningstagning og planlægning".^[1]^[2]^[3] Derudover er data af højere kvalitet hvis det korrekt repræsenterer den virkelige verden, som den omhandler. Udover disse definitioner er spørgsmålet om intern datakonsistens blevet mere signifikant i takt med at antallet af datakilder stiger. Der er kan også være uoverensstemmelser imellem folks syn på datakvalitet, selv hvis det omhandler det samme sæt data, der anvendes til det samme formål. I disse tilfælde bruge datastyring til at danne et aftalt sæt definitioner og standarder for datakvalitet. Datavask, inklusive standardisering, kan være nødvendig for at sikre datakvalitet.^[4]

Referencer

^ Redman, Thomas C. (30. december 2013). Data Driven: Profiting from Your Most Important Business Asset. Harvard Business Press. ISBN 978-1-4221-6364-1.
^ Fadahunsi, Kayode Philip; Akinlua, James Tosin; O’Connor, Siobhan; Wark, Petra A; Gallagher, Joseph; Carroll, Christopher; Majeed, Azeem; O’Donoghue, John (marts 2019). "Protocol for a systematic review and qualitative synthesis of information quality frameworks in eHealth". BMJ Open. 9 (3): e024722. doi:10.1136/bmjopen-2018-024722. ISSN 2044-6055. PMC 6429947. PMID 30842114.
^ Fadahunsi, Kayode Philip; O'Connor, Siobhan; Akinlua, James Tosin; Wark, Petra A.; Gallagher, Joseph; Carroll, Christopher; Car, Josip; Majeed, Azeem; O'Donoghue, John (2021-05-17). "Information Quality Frameworks for Digital Health Technologies: Systematic Review". Journal of Medical Internet Research (engelsk). 23 (5): e23479. doi:10.2196/23479. PMC 8167621. PMID 33835034.
^ Smallwood, R.F. (2014). Information Governance: Concepts, Strategies, and Best Practices. John Wiley and Sons. s. 110. ISBN 9781118218303. Arkiveret fra originalen 2020-07-30. Hentet 2020-04-18. Having a standardized data governance program in place means cleaning up corrupted or duplicated data and providing users with clean, accurate data as a basis for line-of-business software applications and for decision support analytics in business intelligence (BI) applications.

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[Redman2013-1] Redman, Thomas C. (30. december 2013). Data Driven: Profiting from Your Most Important Business Asset. Harvard Business Press. ISBN 978-1-4221-6364-1.

[Fadahunsi_e024722-2] Fadahunsi, Kayode Philip; Akinlua, James Tosin; O’Connor, Siobhan; Wark, Petra A; Gallagher, Joseph; Carroll, Christopher; Majeed, Azeem; O’Donoghue, John (marts 2019). "Protocol for a systematic review and qualitative synthesis of information quality frameworks in eHealth". BMJ Open. 9 (3): e024722. doi:10.1136/bmjopen-2018-024722. ISSN 2044-6055. PMC 6429947. PMID 30842114.

[3] Fadahunsi, Kayode Philip; O'Connor, Siobhan; Akinlua, James Tosin; Wark, Petra A.; Gallagher, Joseph; Carroll, Christopher; Car, Josip; Majeed, Azeem; O'Donoghue, John (2021-05-17). "Information Quality Frameworks for Digital Health Technologies: Systematic Review". Journal of Medical Internet Research (engelsk). 23 (5): e23479. doi:10.2196/23479. PMC 8167621. PMID 33835034.

[SmallwoodInfo14-4] Smallwood, R.F. (2014). Information Governance: Concepts, Strategies, and Best Practices. John Wiley and Sons. s. 110. ISBN 9781118218303. Arkiveret fra originalen 2020-07-30. Hentet 2020-04-18. Having a standardized data governance program in place means cleaning up corrupted or duplicated data and providing users with clean, accurate data as a basis for line-of-business software applications and for decision support analytics in business intelligence (BI) applications.

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