Why This Sudden Interest in HR Analytics ?

For a long period, HR has been striving to get a seat on the table along with finance, operations and sales and marketing functions to become a strategic function in any organization. In its quest to become "strategic", at the basic level, it has been demonstrating its value-add to business by showcasing metrics focusing on "efficiency" such as lowering HR Cost per employee or reducing cost of per hire, etc.

Some other organisations have gone a step ahead and showcased "effectiveness metrics" such as employee engagement, satisfaction increase, or employee retention increase to highlight HR's value add to business.

However, the C-level has been sceptical of these metrics and these have been generally labelled as metrics for justifying the existence of HR without any tangible link to either top-line or bottom-line performance. So this gap of showing how HR Metrics link to business metrics has always remained.

Predictive analytics will inform C-level about "what will happen", for example who will quit next, while prescriptive analytics will inform the C-level about "what can be done to prevent that attrition". New generation of analytics like cognitive analytics can identify patterns and insights which could not have been seen earlier due to human limitations to construct models. So this kind of HR Analytics, purely based on data, catches attention of C-level and, hence, provides an opportunity for HR to become truly strategic, and this, in turn, will transform how HR is practised.

Google leads in the use of predictive & prescriptive analytics and lot of other large companies such as Shell, P&G, Morgan Stanley, Xerox and GM have started using these analytics. However the number of companies globally using these advanced analytics is very small. Latest study done by Bersin by Deloitte in September 2013 shows that only 10% of Fortune 500 companies are using these advanced analytics and out of this 10%, only 4% are using predictive and prescriptive analytics, while other 10% are using basic statistical techniques for HR analytics ( Bersin, Leonard & Wang-Audia,2013). The major reason why only a small number of Fortune 500 companies are using HR analytics is because HR faces big challenges to scale up for using HR analytics.

Traditionally, Hr has always been collecting volumes of data on various dimensions of human resources, such as:

1.      Demographic

2.      Performance management

3.      Compensation

4.      Educational history

5.      Job Location

6.      Training

7.      Talent Movement

However, this data has been used so far to compute metrics or ratios and do benchmarking. As highlighted earlier, benchmarking helped an organization to compare its status of HR practices with others in the industry and get an idea about various best practices followed by industry. For example, if an organisation had an attrition rate of 17% and the industry attrition rate was 19%, the comparison only gave solace to the organization that it is doing better than the industry but did not inform it about who is leaving, why they are leaving, and what can be done to prevent attrition. Thus, organizations were merely using available data to justify its existence by comparing with industry, whereas board and CEO wanted HR to show evidence of HR investments impacting the top-line and bottom-line.

HR analytics linkage to Business Outcomes

HR Analytics can impact business outcomes such as sales, productivity, profitability, customer satisfaction, etc., either by adopting an HR measurement system covering all HR practises or by focusing on a single HR practice, like recruitment, without a full measurement system.

1.      Adoption of HR analytics as a model using technology : Here, the focus is on how the implementation of technology-or system based HR analytics as a cogent model of some maturity level by any organization impacts business outcomes, for example, use of a human capital management (HCM) reporting and analysis system.

2.      Adoption of HR program/practise: Here, the focus i son how the use of various HR programs such as employee engagement surveys, workforce planning, succession planning, compensation management, performance management, learning and development etc. based on analysis link to business outcomes. For example, based on recruitment data analysis, an organisation identifies certain attributes linked to high performers and then hires those who have those attributes.

Measuring Use of HR Analytics Impact on Business Outcomes

HR function has been collecting various types of employee data for many decades now. Much of this data by any organization has been used to gather inputs on what has happened in the last year and what is happening at the moment on HR Metrics, such as headcount, attrition rates, absence rate, cost per hire, training hours per employee, etc., primarily to get a sense of the current HR temperature in the organization. Increasing adoption of HCM suites by organizations enabled them to play with core HR data and HCM suite's data and, thus, marked the beginning of use of HR Analytics at the organization level. Now an organization is able to track which units or projects are having more usage of training resources or where performance appraisal quality is better than other units or projects. As acquisition of HCM suites came with a substantial cost, management became interested to know how the use is impacting business outcomes.

Aberdeen Group published a study in 2012, based on an extensive study, to find out the impact of deployment of HR analytics on business outcomes by comparing data of organisations which had adopted HR analytics to support business strategy versus those which were not using it. The study found that the use of HR Analytics helped companies to achieve higher results in the range of 8%-15% for customer satisfaction, customer retention, and revenue per employee.