Source | LinkedIn : By Kinjal Choudhary
There is an old saying, “if you want something to get done, you need to start measuring it”, Unfortunately, for the Human Resource Function measurement has eluded most aspects of it ever since the Function came into existence. While the name of the Function has undergone several changes over the last few decades from being called Personnel to Human Resource Department to being called the Human Capital Function, what has remained unchanged is the lack of universally accepted measurement practices.
There are essentially six fundamental issues with the use of data for Human Resources Function. First is the lack of data on a uniform basis across organizations and across time to make it universally comparable. Unlike the financial metrics which have a common definition and is universally understood, metrics in HR function do not have universal definition and hence comparison across organizations and across time within an organization often becomes irrelevant and hence is not used. The second issue is that the metrics that are used, more often than not, the metrics are only descriptive and not predictive in nature. In other words, they only describe events in the past and does very little to throw any light as far as the future is concerned. For instance, attrition rate in the year gone by is rarely any indication of the attrition in the coming year. In a world where the present and the future were a linear extension of the past, this kind of data may have been useful but in a world where everything changes so rapidly that can hardly be correct. The third issue is with lack of interpretation of the data that is captured. Even if the data is available unless the same is interpreted and inference drawn from it, the data is of very little relevance. For instance, if the attrition percentage is 10% in 2013, 12% in 2014, 8% in 2015 and 7% in 2016, one needs to interpret the same and draw an inference out of it. Otherwise these are merely numbers and describe an event over the last few years. The fourth is the absence of effective benchmarking. Data per se is nothing but numbers unless it is seen in relation to others. Again, the attrition numbers mentioned above have very different meaning if the comparable organizations have double that attrition rate or they have half that attrition rate. Unless one is able to benchmark and compare much of the data may be quite meaningless. Fifth is the classification of data between Lead and Lag Indicators. In order to understand the reporting and use of data in a holistic sense, we need to distinguish between what are Lead Indicators and which are Lag Indicators. The former typically help in predicting what is about to come whereas the latter describe what has already happened. Any holistic approach to data would require the use of both. Some of the examples of Lag Indicators would be, say, Cost Per Hire. This describes the average cost that the organization has incurred for every hire. However, this data is describing the event after it has occurred and does not help in predicting anything for the future. Although this data is very valuable but one also needs to look at a Lead Indicator to predict what this metric would look like in the next Quarter or next year. The Lead Indicator for that would be the proportion of hires being done through on-line job portals versus being done through head-hunters. Typically, the latter is likely to increase the cost per hire as compared to sourcing candidates through on-line job portals. In this example, unless an organization is able to increase its dependence on job portals as compared to head-hunters, it is unlikely to be able to make any significant difference on cost per hire. Sixth and the last point pertains to our ability to measure the impact of people on business. Are we actually measuring the return on investment on people? If not, which I am afraid is the case more often than not, then it is very difficult to justify increase or decrease in investment on people related initiatives. It would be left to the discretion of individuals within the organization and the general financial condition of the organization. When the going is good, there would be a higher learning & development budget and when the going gets tough, the first casualty would be on investment on people.