Enterprise Personal Analytics: Research Perspectives and Concerns

Enterprise Personal Analytics: Research Perspectives and Concerns

Trevor Clohessy (J.E. Cairnes School of Business and Economics, National University of Ireland Galway, Galway, Ireland) and Thomas Acton (J.E. Cairnes School of Business and Economics, National University of Ireland Galway, Galway, Ireland)
Copyright: © 2017 |Pages: 18
DOI: 10.4018/IJBIR.2017070103

Abstract

Modern enterprise technological landscapes are being impacted by the increasing individuation of information systems (IS). Consequently, the end-user computing phenomenon is being extended to incorporate a multitude of nascent possibilities for organizations. One promising avenue encompasses the use of business analytics. Common categories of enterprise intelligence analytics are traditionally derived from activity patterns and collaborative routines. The scope of this article focuses on another emergent category of analytics which is referred to as “enterprise personal analytics”. This topic has been only minimally analysed in IS and business intelligence research. This article therefore extends understanding by presenting a grid framework which comprises various combinations of research stakeholder perspectives and concerns. This framework can be used to guide and coalesce future research on illuminating how personal analytics can be used effectively in an enterprise setting.
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1. Introduction

Evolution is happening when you are not watching. (Baskerville, 2011)

As our lives “…become immersed by powerful digital devices and services, questions of implications for individuals’ lives as well as their social interactions and structures arise… this emerging fully digitized and connected environment implies changes to the development, exploitation and management of personal information and technology systems…” (Matt et al., 2017). One promising technological trend in this regard will be the use of personal analytics which first appeared in the innovation trigger category in the Gartner hype cycle for technologies in 2016. According to Ingelbrecht and Herschel (2015) “personal analytics empowers individuals to analyse and exploit their own data to achieve a range of objectives and benefits across their work and personal lives”. Personal data can relate to biometrics, personal finance, social media activities, health status, behaviours, emotional states, mobility, interest areas and so on. In an increasingly data driven society, the emergence of personal analytics has been catalysed by the convergence of mobile (new and emerging ambient user experiences), cloud computing, business intelligence and social technological advancements.

Organizational interest with regards to enterprise personal analytics is also beginning to gain traction. Extant evidence highlights how “top-performing organizations use analytics five times more than lower performers” (Lavelle et al., 2011). Personal analytics can empower individuals within organizations to manage their digital working lives from descriptive, diagnostic, predictive and prescriptive points of view. While traditional organizational “intelligence metrics deliver a big picture of structures, processes, and roles, more detailed and personified analytics provide individuals with a mirror view of their actual versus desired way of work and the resulting personal productivity” (Dobrinevski, 2013). This personal analytics phenomenon has been catalysed by a multifaceted and amorphous concept known as information technology (IT) consumerization in conjunction with a number of personal learning analytics trends (See Section 3). IT consumerization in its broadest sense refers to the “phenomenon of more and more employees bringing their own IT into the work place and using these tools for work purposes” (Harris, Ives and Junglas, 2012). This “consumerizing of the previously sovereign territory of the IT department” has led to a multitude of benefits in terms of innovation, employee satisfaction and productivity (See Harris, Ives and Junglas, 2012). In order to leverage these benefits enterprises are responding to IT consumerization by introducing a number of proactive strategies. For example, in an effort to reduce employee medical insurance costs, organizations are providing their staff with Fitbit health technology wristbands in an effort to promote corporate wellness and motivate healthier employee lifestyle behaviours. Health insurance providers such as Unitedhealthcare currently offer this service for their organizational clients. This employee data is analysed by a third-party company called Qualcomm Life. Based on how active the employees are, as measured by the Fitbit, they can receive as much as $1500 towards their health care services. Similarly, enterprises are using employee’s personal analytics in order to enhance operational efficiency, strengthen employee retention and relationships, enhance decision making and provide actionable reports. Companies such as DATIS provide a cloud based talent management software solution which provide managers with dashboards which enable them to analyse employee data such as timesheet submissions, pending workflow requests, corrective actions and so on.

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