Discuss the role of talent analytics as a strategic tool in achieving talent management outcomes and their impact on business performance.
Talent Management is the system which manages the employees in an organization. More specifically it oversees the working ability, conduct and competency of the personnel working in various positions in the company. It is responsible to organize, guide and facilitate in maintaining the work culture, employee allotment with the goal of achieving best performance output. Traditionally this task depends very much on instincts of the leaders in the area and their capability to personally understand and motivate the employees and potential employees (Minbaeva, Dana and Collings 2013).Today many companies have been shifting to a more analytical approach to talent management. This is perhaps supported by the fact that large volumes of “digital trails” of data from social networks and knowledge management systems are now easily available which could be used for analyzing the underlying factors that drive employee motivation or to identify and categorize people as per their talent and potential or even try to identify patterns of employee migration (Douthitt, Shane and Mondore 2014). Such insights have proven to be instrumental in improving talent management capabilities of companies like Google, Harrah’s Entertainment, AT&T among various other well established organizations (Sullivan 2013).
The analytical approach is one which is based on fact and not feelings. This means that it deals in quantification of the potentials and risks. These quantifications are computed through certain mathematical formulae or metrics. Metrics help in tracking and measuring performance and in turn help the leadership make informed decisions (Falletta2014). In the area of human resource it can aid the managers and executive measure various aspects in various areas of talent management. It could range from employee payroll, training, and performance, inter and intra organizational mobility and many more including cross-functional areas. However HR professionals need to consider the intended goals, strategies and the available data to determine the metrics which would suit their purposes. Following are some of the basic HR metrics used across various areas of talent management.
Turnover Rate measures the no of employees that leave an organization within a given period of time. It could be used to measure turnover for different kinds of employees or kinds of turnovers, namely voluntary and involuntary. It is defined as number of employees exiting the job during the period divided by average number employees employed during the same. It could therefore be used to measure employee retention in the organization. If turnover is high then it may raise concerns of the working conditions and additionally raise concerns over the cost to replace that employee. It thus gives an indication of the culture or health of the organization (Falletta 2014).
Average time to fill measures both the time taken to fill a vacancy in a particular set of positions in the organization as well as the efficiency of the talent acquisition team. It is calculated by dividing the total time taken to fill the vacancies with the total no of vacancies that have been filled. The metric values may vary as per the industry and position that are being taken into account however if this metric falls below industry standards then it serves as an indication that the organization may be missing out on the top talents (Falletta 2014).
Career path ratio quantifies the internal employee mobility of the organization. It compares the number of promotions against the number of lateral transfers within the organization. It is evaluated by dividing the number of promotions by total number of job transfers within. The measure lies between 0 and 1. A healthy company is expected to have the value to lie around 0.5.Values greater than 0.5 indicate hoarding of talent whereas lesser than 0.5 indicate that the company has failed to make use of its talent or has been unable to acquire adequate talents (Falletta 2014).
Revenue per employee quantifies the contribution of each employee to the revenue generated by the organization. It is computed by dividing total revenue by total employee count. It gives an insight into the output being generated by the employees. It gives an indication of the overall efficiency and helps to determine whether the business in being over stated or under stated (Falletta 2014).
Thus the aforementioned metrics could be categorized into two groups, namely, metrics which indicate the organization’s performance like turnover rate and career path ratio and metrics which indicate the effectiveness and efficiency of the HR like average time to fill and revenue per employee. These metrics among many others , thus provide valuable insights about the functioning of the organization which aids in the decision making process (Roberts 2015).
Talent analytics can thus be broadly used over six domains, each of which tackles a different kind of management area. These are as follows: Analytical HR deals with collection and organization of HR data in order to gain insights relating to various analytical queries. It integrates HR process metrics with individual performance data and outcome metrics; Human capital facts focuses on monitoring the organization’s overall performance and health by use of relevant metrics; Human capital investment analysis investigates how actions may influence performance of employees and provide insights about how employees could be retained and their talents utilized in the best way given the goals and circumstance; Workforce forecast deals with analyzing employee turnover and business potential data to identify growth areas and anticipate the career moves of employees and new hires; Talent value model is used to understand and model opinion and consequent reactions of employees with regard to the various aspects of the organization such as work culture, infrastructure and policies. These can in turn be used to improve retention and performance and tackle various difficulties; Talent supply chain is an analytical tool to aid the decision makers in real time decision making processes by utilizing insights provided by analytical metrics computed from real time and historical data (Davenport et al. 2013). A prime example of how talent metric drives business through talent management would be that of Harrah’s entertainment which has been using talent metrics to understand how its health and wellness program has been affecting the performance and motivation of its employees and its performance. It has found that there has been an increase in visits to its on-site clinics for preventive measures by its employees which has worked to lower the urgent care costs by a significant amount. It has also built an evaluation technique to gauge their operational performance on the basis of the revenue that is being contributed by each of the involved employee. This is justified on the insight that they have received from their analytics team over how the top-line revenue relates to employee engagement (Davenport et al. 2013).Business insights from talent metrics thus can help companies improve business by supporting and enhancing their understanding of problems and weaknesses and in turn make more sure footed and informed decisions to make changes for the better.
Implementation of these various avenues of analytics however has certain key requirements. Any analytical endeavor to succeed, must have access to high-quality, authentic data, be business oriented, have leadership who are understand analytics, have well defined strategic targets, and experts who can execute those analytical operations (Lal 2015).To ensure the proper functioning of an analytics driven talent management process it is therefore of prime importance to have in place, a system which could help in collaboration of the many functional parts of the analytical process ( Fecheyr-Lippens et al. 2015). Additionally, it is to be noted that big data being a core part of talent analytics requires high level of technological capabilities. Technology is therefore a significant factor to determine the fruitful implementation of talent analytics (Clark et al. 2014).
Talent analytics can be said to have three major levels of operations, namely, reporting, strategic analysis and predictive analysis (Ulrich, Dave and Dulebohn 2013). Each level comes with its own challenges with regard to infrastructure. Needless to say that information systems are of supreme significance for any enterprise oriented analytical venture. Talent analytics however has a long way to go with regard to the implementation front. There are a number of issues which are yet to be addressed, notable among which is issues relating to structure and systems, which is referred together as “Silos”. The structural silo problem refers to functional boundaries between different parts of the organization as well as within the HR itself. It also indicates the lack of collaborative approach between these organizational compartments and to compound all that the discrepancies between management structures, reporting lines and organizational charts disrupt the flow of data to hamper the analytical process. For example, there might be need for data pertaining to finance and marketing. This would call for setting up a system which could facilitate access to data from the respective functional compartments by the different people involved in the analytical process (Ulrich et al. 2013). There is also a problem of lack of skills among the people involved in HR with regard to analytics all together. It is thus necessary to set up a system of reporting which is friendly to the people who are not much familiar with the technicalities. System silos refer to the inadequacy in systems with regard to compatibility and integration. Inappropriate linking and data handling capabilities can be counted as some of the problems arising under this which render systems unable to coherently relay information (Marchand, Donald and Peppard 2013). Many companies nowadays customize analytical dashboards to help tackle some of the inefficiencies as stated under silos. A well-structured database management system can effectively help mitigate some of the data integrity problems (Koriat, Noam and Gelbard 2017). Therefore it can be said that analytics in HR has a long way to go and technology has quite a role to play in making it a success.
It has been seen that companies with a talent management team supported by robust analytical infrastructure have been able to successfully identify their shortcomings in various HR related areas and consequently tackle the issues and improve their employee retention, employee satisfaction and overall performance (Oladapo 2014).A survey carried out by Corporate Leadership Council in 2013 has given an account of how analytics can aid in achieving talent outcomes. It shows that companies using analytics have been successful improving bench strength, employee performance, employee engagement and quality of hire, all of which have a significant contribution to business performance. The impact of analytics on talent outcomes as per their analytics survey has been given in the Appendix.
Effective talent management can help organizations align company goals with that of employee development. Naturally that works to improve the overall performance. Talent analytics has the means to provide meaningful insights to help the HR make surefooted and informed decisions with regard to managing the talent pool and mining talent for the company. However effectiveness of the analytics mechanism within the organization is depends on a number of factors, including ease of data flow, report generation and sharing all defined by the technological infrastructure, skills of the people involved, as well as intra organizational communication. It is still in its nascent stage and has a long way to go.
References
Angrave, David, Andy Charlwood, Ian Kirkpatrick, Mark Lawrence, and Mark Stuart. “HR and analytics: why HR is set to fail the big data challenge.” Human Resource Management Journal 26, no. 1 (2016): 1-11.
Clark, David J., David Nicholas, and Hamid R. Jamali. “Evaluating information seeking and use in the changing virtual world: the emerging role of Google Analytics.” Learned Publishing 27, no. 3 (2014): 185-194.
Davenport, Thomas H., Jeanne Harris, and Jeremy Shapiro. “Competing on talent analytics.” Harvard business review 88, no. 10 (2013): 52-58.
Douthitt, Shane, and Scott Mondore. “Creating a business-focused HR function with analytics and integrated talent management.” People and Strategy 36, no. 4 (2014): 16.
Falletta, Salvatore. “In search of HR intelligence: evidence-based HR analytics practices in high performing companies.” People and Strategy 36, no. 4 (2014): 28.
Fecheyr-Lippens, Bruce, Bill Schaninger, and Karen Tanner. “Power to the new people analytics.” McKinsey Quarterly 51, no. 1 (2015): 61-63.
Koriat, Noam, and Roy Gelbard. “Knowledge Sharing Analytics: The Case of IT Workers.” Journal of Computer Information Systems (2017): 1-11.
Lal, Prerna. “Transforming HR in the digital era: Workforce analytics can move people specialists to the center of decision-making.” Human Resource Management International Digest 23, no. 3 (2015): 1-4.
Marchand, Donald A., and Joe Peppard. “Why IT fumbles analytics.” Harvard Business Review 91, no. 1 (2013): 104-112.
Minbaeva, Dana, and David G. Collings. “Seven myths of global talent management.” The International Journal of Human Resource Management 24, no. 9 (2013): 1762-1776.
Oladapo, Victor. “The impact of talent management on retention.” Journal of business studies quarterly 5, no. 3 (2014): 19.
Roberts, Pasha. “The CFO and CHRO guide to employee attrition.” Workforce Solutions Review 6, no. 1 (2015): 8-10.
Sullivan, John. “How Google is using people analytics to completely reinvent HR.” TLNT: The Business of HR 26 (2013).
Ulrich, Dave, and James H. Dulebohn. “Are we there yet? What’s next for HR?.” Human Resource Management Review25, no. 2 (2015): 188-204.
Ulrich, Dave, Jon Younger, Wayne Brockbank, and Michael D. Ulrich. “The state of the HR profession.” Human Resource Management 52, no. 3 (2013): 457-47
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