Table 11.

Summary of studies on RegTech applications related to data quality

Data qualityRegTech rolesAcademic and gray literature addressing the issue
ConsistencyFacilitates data normalization, ensuring that information is represented consistently across various sourcesIn total eight studies: A5; G1; G3; G4, G7; G8; G13; G24
CompletenessThe data collected reasonably cover the full scope of the question they are intended to answerIn total seven studies: A5; G1; G2; G4; G8; G24; G30
TimelinessProvides automated data collection, ensures information is captured in real time and eliminates delaysIn total seven studies: A5; A17; G1; G4; G8; G27; G24
AccuracyProvides data governance and accurate data through automated validationIn total 10 studies: A5; G1; G3; G4; G8; G7; G13; G18; G24; G30
ReliabilityAllows massive quantities of ESG data to be processed with AI and MLIn total nine studies: A5; G1; G4; G8; G7; G13; G18; G24; G30
RelevanceDetermines what ESG issues are significant for organization and material dataIn total eight studies: A2; A5; G3; G4; G8; G18; G24; G30
ComparabilityEnhances comparability by eliminating discrepancies arising from variations in data formatsIn total six studies: A3; A5; G1; G7; G18; G24
Source(s): Authors’ own creation

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