Human Resources

It is time Human Resource starts focusing on Data

There is an old proverb that says: If you want something done, you need to start measuring Unfortunately, for the HR function measurement is the most aspects of it has since the function has emerged. While the name of the function has undergone several changes over the last decades, personal invitation to the personnel department to be called the role of human capital, which has remained unchanged, the lack of practice is generally accepted measurement.
There are basically six key questions when using data with human resources. The first is the lack of data on a unified foundation for organizations and to make comparable across the time universally. Unlike financial figures that have a common definition and generally understood measurements in human resources have no universal definition and thus the comparison between organizations and over time in an organization is often irrelevant and therefore not used. The second problem is that the measurements are often used for measurements is not only descriptive and predictive in nature.


In other words, only describe events in the past and do little light to throw the future. For example, last year’s sunk is rarely any indication of desertion next year. In a world where the present and the future were a linear continuation of the past, this kind of data has been useful, but in a world where everything changes so quickly that it can hardly be right. The third problem is the lack of interpretation of the collected data. Although the data is available when the same and drawn from the interpreted data conclusion is of little importance. If the default rate in 2014 is 10% in 2013 of 12%, 8% in 2015 and 7% in 2016, is to be interpreted for example in the same and draw a conclusion. Otherwise, these are just numbers and an event in recent years. The fourth is the lack of an effective benchmark. The pro data are only numbers when you are looking at relationship to others. Once again mentioned hard numbers has a completely different meaning when comparable organizations has twice the wear or half the wear rate.

Unless one is able to compare many of the data and compare it, it can be quite useless. The fifth is the classification of data between lead and lag indicators. To understand the information and use of data in a holistic sense, we must distinguish between what are indicators of lead and lag indicators. The former are usually predicting what is coming, while the latter describe what happened. All data, holistic approach would require the use of both. Some examples of lag indicators would be, say, cost per rental.


This describes the average costs incurred by the organization for each rental. These data describe the event after it has occurred, and helps not predict anything in the future. Although these data are very valuable, but we also need a leading indicator in order to see how that would predict this metric in the next quarter or next year. The leading indicator for this would be that the percentage of contracts made by online portals compared to the work done by headhunters. Typically, the latter is likely to increase the cost of rent compared to care providers through online job portals. In this example, unless an organization compared to increase the dependency on job exchanges is able to make headhunters, it is unlikely to make a significant difference in the cost per lease. The sixth and final point refers to our ability to measure the impact of people in the business world. Are we really measuring the return on investment in people? Otherwise, I am afraid that this is the case more often than not, it is very difficult to justify the increase or decrease in investment in initiatives to refer to people. It would be left in the organization for individuals and the overall economic situation in the organization. If it’s going well, it will be a bigger learning and development budget, and if it gets tough, it would first invest in people’s sacrifice.


Therefore, data can be used universally and authentic human resources, particularly the need for universally accepted in organizations and time indicators. It is time that there is a generally accepted standard for reporting data.

For example, you should use the collected data from almost any organization, they are based on a standard, and reported in the annual report. Should not be left to individual organizations, and what is worse, human resources functions individual to decide how the dropout rate was calculated. Although there is no generally accepted standard for calculating and reporting a net profit after tax, there is no such standard for the percentage of wear, for example. More than anything, it would require discipline collection and reporting of data on human resources in a rigorous and transparent functioning human resources road.
The second point is that the HR function be predictive data analysis goes far beyond merely describing data. This requires a thorough analysis and understanding of the parameters that affect a particular metric and how you can change these parameters in the next period, and thus influence these parameters have on the relevant parameter. For example, while growth in the country to the rhythm of a parameter that has an impact on GDP external market. The critical question would be to identify the factor affecting wear and then the basis for predicting what will probably be in the foreseeable future the percentage of desertion. Of course there are several parameters that affect one of the metric, and therefore must be able to identify the influence of each parameter is determined in this metric, and therefore the expected change in each parameter predict this. For example, while GDP growth is one of the factors affecting desertion, the company’s market position in relation to other organizations on the front compensation may be another parameter. Now, if the organization plans to increase its relative position in compensation before other organizations, although there arises GDP growth is still not superior wear.
The third point is the ability to interpret data, even for the last period. One thing is to report the numbers and very different result can. Most HR functions report only numbers without interpretation. This must change. One thing is that the dropout rate is x, and quite another to be able to reach a conclusion there. The end is always based on trends and it is important that we begin to see trends to interpret the data. Interpretation of the data is what would make the information relevant and useful.
The fourth aspect is the ability to refer to other relevant. I deliberately use “other relevant” rather than industry or region. It is time that we focus on benchmarking move the industry or region to identify other organizations relevant to it. There are few organizations that lose talent within the same industry or hire talent in the same industry. Well, if talent is lost and rent from various sectors or regions, why limit benchmarking within the industry or region? Organizations, companies need to identify where the flow of talent (inside and out) and mark them as “significant other” for all types of benchmarking. Basic benchmarking is critical because everything is relative. For example, a score of engagement / organizational commitment of 65% of a health survey of the organization is irrelevant, except in relation to similar results to other relevant organizations. Of course there is a challenge in producing resembles a similar comparison, given the abundance of studies in this particular example; But in each survey measures the value of their salt more or less the same dimensions and therefore should not be afraid of comparison because the investigation is not quite the same in organizations.

Fifth point advantage and lag indicators are very critical because we need to be able to distinguish the data between the two and use accordingly. Scores of organizational health surveys, for example, is an important indicator of the maintenance or decline of the organization. If the guest is bad or deteriorate in the previous period, usually it reflects greater dis-satisfaction of the employee and thus is a sign that the waste is likely to increase in the near future. Or if they have improved the feedback survey manager in the previous period, a gate, usually this means that employees are more satisfied with their leaders now than they were in the past and therefore likely to be less wear.
The last point is our ability to measure the ROI of people in an organization. While there are prescribed methods available to the public, there are very few organizations that I know that use the same. The simple formula that I find very useful is the ratio of net income, plus the cost of wages and social benefits for the personnel costs. This gives returns for every dollar spent on employees. If the ratio is greater than one, it means that every dollar spent employees will give more than one dollar. Higher the ratio, the greater the return on investment in personnel costs. In this case, the personnel costs include all costs of the rental costs with the costs, including wages and social benefits. Perhaps there are better ways to capture the ROI of the personnel costs, and if not, they should be used, but the jumping point is to change the concept of measuring them. No, you can not just measure, because now there is no generally accepted method to measure is charged or not charged by the law of the country to report immediately.
It is time that human resources begins as a function with a focus on data in a more comprehensive manner, not just reporting numbers. It is important because it would accurately measure the impact of surgery in business and will go a long way in understanding the relative importance of various initiatives regarding the people in society as such. It would have an impact on the development of the business over the mere feeling of the belly, and which would not.

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