In the twenty first century, organizations operate in a complex environment consisting of constant development in technologies, digital communications, and generation of a vast amount of data. To remain competitive, organizations are persistently learning to make sense of those generated data and through interpretation, predict the future for their strategic actions and make better business decisions. HR analytics facilitates a better decision-making through systematic identification and quantification of the human sides of the organizational outcome (Heuvel, & Bondarouk, 2017).
Analytics can help HR to solve a variety of workforce challenges and facilitate strategic decision-makings . All advancement in technologies have drawn academics and practitioners’ attentions to the importance of business intelligence, data analytics, and big data as means to collect, manage, and make meaning from the data (Hsinchun et al., 2012). Organizations can access critical insights from the data collected through different systems and use them to advance their business decision-makings. This decision-making also includes human resource management.
Recent studies suggest a data driven HR is necessary for organizations to be more dynamic and future-ready . Understanding of HR analytics and developing the competencies to be able to give meaning to raw data, has a great influence in quality of the services that HR professionals can offer to their clients and their companies. HR analytics has a vast array of application in organizations, which each HR professional must be capable to understand. HR analytics can be used in several areas of human resources such as employee engagement and workforce perception, prediction of employee turnover, prediction of performance, recruitment and selection, to monitor interventions, and even have business application for scenario modeling and business cases (Edwards, 2016). There are several aspects of HR analytics that needs attention:
The decision-making process is involves several steps such as; gathering data, exploring the data, performing the analysis, interpreting the findings, communicating the finding, and decision making. But to go through the process an analyst first must clarify her question and hypotheses. Steps to take during hypothesis testing include; stating the research question, stating the statistical hypothesis, setting decision rules, calculate the statistical tests, decide if the result is significant and interpret the result as it relates to the research question. A proper research question is specific, measurable, achievable, relevant, and has a time bound. HR analysts must understand that if the research question cannot be answered with the limited data in hand, they must either modify the research question and hypothesizes or start collecting new data based on the question they plan to answer.
Diversity and Inclusion
Diversity and inclusion should be integrated into every part of an employee’s life cycle to avoid bias, unfairness, and lack of innovation. In recent years, the government requires organizations to provide data about their fair practice of diversity and inclusion to ensure equality and diversity. Attention to diversity is necessary to avoid legal issues and increase organization outcomes such as innovation and positive public identity. Diversity can reflect itself in several organization practices such as unbiased training, and leadership developments. HR professionals must consider using analytics to perform gap analysis in order to identify where their company fall short in their diversity and inclusion. It can help to explore employees’ wage gap and find out if their job structures are designed in a way that can accommodate disabled and facilitates advancement for minorities. These type of analyses can guide leaders’ decision makings when taking action to improve the diversity and inclusion.
Disparate Data Sources
Many argue that, there are several challenges for HR analysis. For instance, using the data itself is a challenge for HR. HR data are usually kept in different databases which may not be compatible to one another. However, new data analytic software packages have integration capabilities that allows to import data from different sources and with various formatting.
Legal and Ethical Limitations
Legal and ethical limitations also was noted by Cappelli (2017) as challenges of HR analysis. I could not agree more with this point because human resource professionals are constantly dealing with sensitive information about employees. Some of the most sensitive information can be about employees’ age, gender, ethnicity, disability, medical leaves, complaints, and income. HR professionals must follow the regulations involved in handling the data and be mindful of ethical issues concerning employees’ privacy. Scholars recommend that HR analysts must inform the employees about the reason and the type of data being collected; clarify the rights of employees; encrypt all the databases; ensure confidentiality; and build their reports in the aggregated level.
Visualization and Communication
Story telling with data is one of the most important part of data analysis. One of the difficulties with analytics is the difficulties of explaining the results. Therefore, effective analysts must know how to tell a story with data and have strong narratives to show their findings to their audiences. This is crucial because most of the time, target audiences do not have statistical knowledge and probably will not understand if the findings are presented in mathematical terms. Experienced HR analysts use an engaging story to communicate their findings, build a professional visualization to ease audiences’ understanding, conclude with the logical recommendation for actions to take, and convey the predicted consequences. To build a powerful visualization that catches the audience’s attention, one must carefully consider appropriate colors, use the least amount of words, cuts to the chase, starts at the end result, uses relevant chart to demonstrate data, and well presents the finding without too much use of numbers. Furthermore, effective visualizations must use appropriate chart format, color, text and labels, attribution, data richness, data density, scales, readability, and avoid chart junk.
The common mistakes that data analysts make. can happen from the beginning of analysis to the presentation of end results. Some mistakes are using unclean data, using wrong metrics, ignoring outliers, omitting important information, reporting too much information, performing incorrect statistical analysis, and presenting the result through irrelevant graphics. To ensure that reports are reliable and free of error, data analyst must verify the appropriateness of analysis, the accuracy of results, use of correct statistical test, and use of informative graphs and tables. Moreover, in HR analytics, analysts must ensure that the question posed will solve an important and specific business problem.
HR professionals sometimes neglect to capture the strategic context of a problem and have a micro rather than macro perception on organizational issues. HR analysts must have full understanding of the whole business, organization strategy, values and objective in order to assess the problem and analyze the data to provide relevant recommendations.
Data analytics must be an inseparable part of HR in any organization because lack of analytics can raise problems such as decision making by guts rather than facts, solving the wrong problem, and measuring efficiency rather than effectiveness. HR analysis is about understanding the question and forming it properly and then planning to use the relevant data to find the answer and a make decision.
Cappelli, P. , (2017), There’s No Such Thing as Big Data in HR. Retrieved from: https://hbr.org/2017/06/theres-no-such-thing-as-big-data-in-hr
Edwards, M. R. (2016). Predictive HR analytics: Mastering the HR metric. London: KoganPage.
Heuvel, S. V. D., & Bondarouk, T. (2017). The rise (and fall?) of HR analytics a study into the future application, value, structure, and system support. Journal of Organizational Effectiveness: People and Performance; JOEPP, 4(2), 157-178. doi:10.1108/JOEPP-03-2017-0022 Hsinchun Chen,
Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi-org.proxy.library.georgetown.edu/10.2307/41703503