HR Analytics is becoming widely popular as a management domain in the field of Human Resource Management. Competency in HR analytics is highly valued by organizations and their HR departments, and a number of websites, books, programs and courses are available to disseminate knowledge and skills in the domain. However, most of them focus on only certain aspects of HR analytics, leading to erroneous perceptions and inadequate understanding of the subject.
HR analytics has three core dimensions. Understanding their importance and relationship with each other is imperative for getting a wholistic perspective on HR analytics. These dimensions are objectives, metrics and data. These dimensions need to be completely aligned with each other for optimal utilization of HR analytics.
The eventual purpose of analytics is to make the right decisions to improve organizational performance. The best way to improve performance is by defining specific objectives, and then chart the path to meet those objectives. Objectives of HR analytics may be at the organizational, departmental or functional level. They may be related to either a specific challenge (e.g., high employee attrition), or a certain goal (e.g., increase employee engagement). If the objective is not employee related (e.g., reason for customer churn), HR analytics can still be conducted by identifying associated employee related objectives (e.g., job satisfaction, that may be affecting customer churn).
It is imperative that objectives are defined very clearly at the very beginning before analytics is initiated. Objectives help to frame the questions which analytics is to answer. These questions set the path for analysis by indicating the kind of analytics needed: descriptive (what is happening now), diagnostics (why is something happening), predictive (what can happen in future), prescriptive (what should we do about it) and cognitive (how can machine learning and AI help). For example, an objective of investigating high turnover rate associated with the question – Why are employees leaving? (diagnostic analytics) - can be attained by trying to analyse the factors that cause employees to quit an organization.
If HR analytics is undertaken without any clearly defined objectives, it may not be able to direct the organization towards the right course of action despite a plethora of analysis.
Metrics are measures assessing diverse aspects of organizational functions. They indicate how processes or policies (associated with a function) are doing in absolute or relative terms, over a period of time, or in comparison to other organizations. A number of HR metrics are available and used in the functions of staffing (e.g., time to hire), learning and development (e.g., reaction to training), performance management (e.g., net promoter score), compensation (e.g., compa-ratio) and employee relations (e.g., absenteeism rate).
Metrics help to assess, identify gaps in, and consequently improve performance of the function to which they belong. Functional performance has four dimensions: quality, innovation, productivity and/or service. Each metric can be mapped to one or more of these dimensions. For example, time to hire metric is associated with both quality and productivity of staffing function. Lesser time to hire implies higher chance of recruiting competent candidates quickly.
Metrics provide an organized and focused approach to attain the objectives. And established metrics are especially desirable. This is because of three reasons. First, these metrics save considerable time and effort in analysis, that may be spent otherwise in creating meaningful measures from scratch. Second, analysis derived through them is considered relatively more reliable and acceptable by stakeholders. Third, they directly indicate the improvements required in the HR functions to enhance organizational performance. For best results, relevant metrics should be selected based on the defined objectives. For example, some of the pertinent metrics to investigate high turnover rate can be employee compensation cost, job satisfaction rate and commitment ratio. In case such metrics are not relevant, new metrics may also be used after they have been defined in detail and agreed upon.
Data helps to answer the questions associated with the objectives. Data is analysed using metrics. This data largely pertains to employee records, employee attitude and HR processes, but it may also relate to external information like customer feedback, talent pool and IT investments. The process involves identification, retrieval, analysis and presentation of large volumes of relevant, authentic and valid data. Analysis can be conducted using a range of statistical methods, and a spectrum of enabling software tools that may be standalone or integrated with the enterprise application system. Needless to say, data analysis necessitates specialized knowledge and competencies in the areas of data management, statistics, visualization and software application.
It is apparent that data analysis is necessary for analytics. It is also a hard skill which is considered challenging and exciting. Data analysis generates results in a visible manner, and can be both an intellectual and a creative activity. Also. it is measurable and marketable to a certain extent. Therefore, it is highly valued and given considerable attention. Educational institutes and organizations offer a wide range of courses and programs to impart skills in data analysis. Analytics without data analysis is incomplete, for sure.
However, data analysis is just one of the dimensions of analytics. On its own, it cannot solve a problem or help find the best possible solution. It may determine how the data is processed and what conclusions are drawn, but those conclusions may not address the purpose (of analytics) in the most optimal manner, or may not address it at all. Data analysis can lead to meaningful outcomes only when it is aligned to objectives and metrics. Objectives and metrics form the blueprint that not only indicate how analysis should be done, but also suggest what fundamental and sustainable changes should be made (based on the results of analysis), in processes and functions, to meet the long- term organizational goals.
Therefore, all three dimensions of analytics are vital. Unequal attention can make one lose sight of the big picture, resulting in inaccurate or misleading results. Moreover, these dimensions need to be managed well. Objectives have to be clear to everyone and should have buy-in from senior management. Various kinds of metrics and their utility should be well understood. For analysis, some important conditions are suitability, accuracy, completeness, and timeliness of data, and its seamless extraction and integration from multiple sources. Besides, there are other elements that contribute to the effectiveness of analytics, like stakeholder management, visualization and storytelling. It is only when HR analytics is approached and executed in a systematic and wholistic manner, that it would be able to make the intended difference to organizations.
-Prof. Smita Chaudhry, Associate Professor - Human Resources, FLAME University