Talent analytics: a strategic tool for talent management outcomes.
| Author | Sharma, Anshu |
| Position | Report - Abstract |
Introduction
The past few years have seen a major shift in the way technology impacted business and management processes (Stone & Dulebohn, 2013). During the World Economic Forum (WEF) at Davos in 2016, much was discussed and debated about the fourth industrial revolution. It was said that developments in artificial intelligence, robotics, nanotechnology, 3D printing, genetics and biotechnology are converging towards a new world (CIPD, 2016). Some call it the "Analytics Revolution" which can transform organizations and societies; however, research on analytics is still in its' nascent stage with many organizations finding it difficult to understand how, where and when to use analytics for their advantage (Kiron, Ferguson & Prentice, 2013). As compared to other departments such as finance, marketing and/or supply chain management (Waller & Fawcett, 2013) HR department has not been able to leverage analytics to its full potential even though it collects large amount of employee data (Harris, Craig & Light, 2011). Even today the major focus of analytics is seen in the information technology (IT) domains, with few organizations realizing the importance of analytics for HR intelligence (Xavier, Srinivasan & Thamizhvanan, 2011). Organizations still lack consensus on how to use analytics to take talent related decisions (Douthitt & Mondore, 2014). There is a need to address this lack of talent intelligence in organizations where HR has hard data facts to develop talent strategies (Snell, 2011). One question that intrigues researchers in the field of HR is: how to leverage the power of analytics on the huge employee data for strategic decision making, more specifically for talent management (Douthitt & Mondore, 2014).
Collings and Mellahi (2009: 305) defined talent management (TM) as "activities and processes that involve the systematic identification of key positions, development of a talent pool of high potential, development of a differentiated human resource architecture to facilitate filling these positions with competent incumbents and to ensure their continued commitment to the organization." Since proclamations of a "War for Talent" in the late 1990s, talent management has become one of the most common terms in managerial and HRM practitioner lexicon (Minbaeva & Collings, 2013). In times of global competition and rapid changes in the business environment, TM has become the biggest HR risk faced by organizations with analytics required to be the key component for an effective TM strategy (Leisy & Pyron, 2009). In a recent survey on human capital trends in 2014, Deloitte revealed that globalization has impacted both talent management (TM) and analytics, making them some of the urgent global trends requiring attention, taking talent analytics among the top capability gap index. On the other hand, the annual survey report by the Chartered Institute of Personnel and Development (CIPD, 2011), pointed to TM to be a critical human resource (HR) activity that needs attention as most of the organizations find their TM strategies as ineffective. The present paper, answers the call enumerated by Tsui (2013a), where she stated, "management research, however, focuses on refining theories or methods and pays much less attention to solving important empirical puzzles in management practice" (Tsui, 2013a: 137; Hambrick, 2007). Strategic talent management and analytics pose a theoretical puzzle in management practice. In this paper we tried to solve this theoretical puzzle by examining the literature on analytics and talent management and unraveling how the former relates to the latter. Hence, this paper explores how organizations can leverage analytics for strategic TM by exploring the antecedents and outcomes of talent analytics, with its impact on business performance.
HR & Analytics: 'Data' as a Strategic Asset
Technology has played a significant role in changing the way organizations work (Deloitte, 2014). With the introduction of technology in the HR function, there is a significant improvement in the quality of HR services being offered to the employee(s) and employer(s) which enhanced HR efficiency (Stone & Dulebohn, 2013). Giving an analogy between the customer relationship management (CRM) and HR, Haworth & Whitman (2004) stated that technology may help improve employee experiences such as identifying, developing and retaining talent just as technology affects buyers' experiences. It has been identified that employee engagement levels can be improved by using technology (Haworth & Whitman, 2004). Study on Indian organizations revealed that only a few organizations are using their HRIS (Human Resource Information System) for decision analytics purposes while others used it in a more transactional manner (Bhatnagar, 2007a). Not only has the use of technology improved HR service or reduced its administrative load but it has also helped HR play the strategic partner role by providing employee information for strategic decision making (Stone & Dulebohn, 2013). Just as technology made a significant impact on HR service delivery, analytics is also seen as the next enabler for HR and business outcomes (Fitz enz, 2009).
The analytics vary from low value steps such as recording data and relating it to organizational outcomes to high value steps such as benchmarking, descriptive and predictive analysis (Fitz enz, 2009). Analytics may be descriptive, predictive or prescriptive in nature which depends on how the data is used for related insights using the applied analytics disciplines such as statistical analysis to arrive at decision making (Kiron, Ferguson & Kirk Prentice, 2013). Most of the HR metrics are lag metrics that measure either efficiency or effectiveness, and few lead indicators to predict future business strategy (Coco, 2011). Predictive analytics can help HR predict events based on patterns of the past behavior (Fitz enz, 2009). Hence, along with people, data has also emerged to be a strategic asset for organizations (Miller, 2014). Organizations are using massive data that they can capture and may be used for competitive insights (Harris, Craig & Light, 2011). The high volume socioeconomic data gathered from a large number of sources, public or private, is termed as big data (George, Haas & Pentland, 2014). "What make most big data big are repeated observations over time and/ or space" (Jacobs, 2009: 40). According to Gartner, any data is big data when it is characterized by the three Vs: high Volume, high Velocity and high Variety which requires new forms of analytical tools for insightful decision making (Laney, 2012). Being more dynamic and real-time, big data has complemented other archival sources of data which are more static in nature (George, Haas & Pentland, 2014).
Analytical Capability & Talent Management
The analytical ability of the organizations is a differentiating factor and a source of competitive advantage (Davenport, 2006; Wixom & Watson, 2010; LaValle, Lesser, Shockley, Hopkins & Kruschwitz, 2011). Organizations have started making employees the focus of their analytical activity (Davenport, Harris & Shapiro, 2010) as they realize that along with customer information, employee data is also a relevant data for strategic decision making insights (Xavier, Srinivasan & Thamizhvanan, 2011). Analytical capability of the HR function can help apply sophisticated analytics on employee data to find hidden meanings and patterns in data, predicting future events and so on (Fitz enz, 2009). Such analytical capability be utilized for challenging business problems rather than simple quantitative analyses (Harris & Craig, 2011). With the help of analytics...
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