Personal Data Privacy and Protection in the Meeting, Incentive, Convention, and Exhibition (MICE) Industry

Introduction

New technologies changed open or private data in personal or business environments. The big amounts of gigabytes and terabytes of disseminated personal or business data across global networks and databases have brought significant advantages for scientific understanding and business opportunities. Since the individuals are more involved in information and communication practices, they produce various forms of distributed and persistent data in different dimensions and structures. Moreover, data about the objects of physical life is also migrated to virtual environments and this data is combined and aggregated with online data (Brown & Marsden, 2013). This reality creates the phases of data lifecycle including the processes of collecting, saving, storing, reporting, sharing, analyzing and interpreting data.

Digitalization has allowed all individuals to contribute to worldwide big data mass. An estimated of 1.8 ZB data is produced in 2011 and this number is expected to reach approximately 90 ZB by 2020 (Jeon, 2012). It is expected that personal data can generate an economic benefit of $387 billion annually by 2020 (Liem & Petropoulos, 2016). Consumers create enormous data stream during their communication, searches, transactions and travels through connected devices. They leave a digital footprint behind that contains their personal information. Seventy five percent of digitally created data is composed of personal data and almost seventy percent of consumers take action to protect their privacies across all industries (Cooper et al., 2016). This reality generates personal data privacy concerns and the need of safeguarding data. Only in the U.S. A., more than $2 billion is spent to assure personal data privacy and protection (Khatibloo et. al., 2016). Despite of increasing volume of data creation every second, traditional systems have failed to resolve issues related to privacy while enabling data to be used effectively (Izu et al., 2014).

The increase in the amount of electronic data makes information systems necessary to evaluate various data types that are not held in relational databases but which contain hierarchical and semantic labels. While structured data following a data model and organized as relational tables are stored in analytically configured databases, unstructured data have a complexity in a dynamic range of its sources that may come from social media shares, mobile data, e-mails and text messages, sensor data and semantic web which are kept in specialized database systems. In order to discover knowledge from structured and unstructured data, the technologies have been developed that can systematically transform data and manage it in appropriate databases and integrate it with other data sources.

Data sharing among individuals, institutions and countries arises the problem of conflict of interest between data owners and holders against data owners. The issue of personal data privacy protection has been addressed in various national and international regulations within this context.

Personal data, as an asset, with its growing financial value has a crucial role in modern economies. Companies invest in data technologies to transform their business processes through digital intelligence for gaining a competitive advantage. According to International Association of Privacy Professionals and EY (IAPP-EY) Report on data privacy governance of 2017, companies spend a mean of $5 million in adaptation of their products and services for personal data privacy protection. Estimated data privacy spent per employee rose from $124 to $147 in 2017 (IAPP-EY, 2017). Moreover, more than three quarters of consumers believe that most companies are not able to handle personal data privacy protection and its related issues (PwC, 2017).