Digital privacy GDPR: a proposed digital transformation framework

Russell, Kenneth D.; O'Raghallaigh, Paidi; O'Reilly, Philip; Hayes, Jeremy

Digital privacy GDPR: a proposed digital transformation framework

Files

AMCIS_2018_Camera-Ready_Submission_976.pdf(398.56 KB)

Accepted Version

Date

2018-08-16

Authors

Russell, Kenneth D.

O'Raghallaigh, Paidi

O'Reilly, Philip

Hayes, Jeremy

Publisher

Association for Information Systems

Abstract

The art and science of successful digital transformation lies in the organization’s ability to implement change at pace, across people, processes, and platforms. While many enterprises begin with brave ambitions, just a few manage to plan, implement and deliver those initial aspirations. The GDPR privacy challenge is symptomatic, being digitally substantial, and oftentimes underestimated. Time-critical transformation demands maintaining change-momentum through employee involvement earlier in the initiative. Our objective in this paper is to evolve a framework to guide engagement, and to assist in the identification and measurement of propensity for change in digital privacy. This paper describes a design science approach to developing the trusted framework to identify and measure resource gaps, both organizational and individual. The resulting Gap-Map framework evolved over several multinational transformation programmes, evaluated through workshops, interviews, surveys, and advanced use-cases. The artefact was found to enable informed decision-making resulting in comprehensive implementation of GDPR (General Data Protection Regulation), through building a shared understanding of those gaps.

Keywords

GDPR , Digital transformation , Privacy , Shared understanding , Internal intelligence , Gap-Map framework

Citation

Russell, K. D., O'Raghallaigh, P., O'Reilly, P. and Hayes, J. (2018) ‘Digital privacy GDPR: a proposed digital transformation framework’, Proceedings of the 24th Americas Conference on Information Systems (AMCIS 2018), New Orleans, LA, August 16-18. Available at: https://aisel.aisnet.org/amcis2018/DataScience/Presentations/36/ (Accessed: 14 November 2018)