Privacy Concern, Data Quality and Trustworthiness of AI-Analytics
Alamäki, Ari; Mäki, Marko; Ratnayake, R.M. Chandima (2019)
Ratnayake, R.M. Chandima
Ketamo, H. & O'Rourke, P
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The present study investigates the role of trustworthiness of data analytics from the data quality and privacy concern perspectives. In addition to the privacy concern of users, we investigated conceptually the requirements and impacts of data quality to the business processes. The goal of the conceptual analyze was to gain more knowledge about the factors affecting to the data quality, its accuracy and business impacts. The privacy concern is a part of data quality. The behavior of users is closely related to the data that they insert to the software systems. The research approach is the case study, that allowed to develop a new understanding of the relationship of privacy concern, data quality and trustworthiness of machine learning. The case study used the abductive qualitative research method, as the study aims to build a new conceptual understanding trustworthiness of AI-based data analytics. Using the iterative research process allowed for developing a deeper understanding while contributing to the conceptual models. The contribution of this paper is to show that data quality affects the trustworthiness of results. The privacy concern is a factor that influences indirectly to the trustworthiness. For the managerial implication, this paper suggests to put special emphasizes to the very first phases of data collection processes where human factors or sensor technological shortages might corrupt the data quality. To sum up, the present study underlines the importance of data quality, reliability and validity in different data categories. Data trustworthiness and data quality evaluation should be included to all marketing and business operations where data is utilized.