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Big data is getting a great deal of airtime, and a lot of people seem to be worried about it. There are many uses of big data, one that gets a great deal of attention is marketing. But it also features, or is starting to feature in Human Resources – yes, your employer is potentially making decisions based on the data it holds on its employees. So this is my take on big data from an HR perspective, we’re not interested in you, we analysis groups. We establish probabilities, and we use these probabilities to inform business decisions. So what does this all really mean, what probabilities, what business decisions?

An example of using organisational data is turned over if we can predict turn over trends in an organisation that’s actually really useful. I’ve actually done this myself and introduced it to an organisation. My client organisation wanted to increase the percentage of a specific equal opportunity group, indeed they had a number of programs to assist in realising this. One of the programs was geared towards entry-level entry positions, with the idea being that members of the EEO group would join the program and at the end of it be placed in a job within the organisation. An issue with the program however was that the employer had to hold the entry-level role vacant for quite some time while the potential employee completed the training – three to four months. Not many of the divisions within the organisation were willing to do this, so the program didn’t reach the potential it actually had. Then I came along and looked at the issue in another way. The problem wasn’t that the training took too long, nor was the problem that divisions within the agency didn’t want to hold positions vacant for months on end – these rather than being problems are simply realities of business. The problem was that we couldn’t predict when vacancies were going to come up.

Through analysing month by month turnover with a couple of years of data we had, and isolating this to a specific remuneration level, we could reasonably predict when vacancies were going to come up. It turns out that typically a very similar number of people were leaving the organisation a on a month by month basis across years. For example in June 2013 25 employees left, while in June 2014 22 employees left – the longer the period you’ve collected data and the larger the sample you’re working with the better these probabilities become. So with some degree of certainty, we can estimate that no fewer than 15 people will leave in the month of June each year – most of us data guys are conservative by nature. So this means we can recruit and train potential employees through the EEO focused program in advance.

You may be thinking so what, 15 employees seem a bit pathetic. Except we can then build on that, by incorporating time to recruit data – and increase the training intake over and above 15. Time to recruit is a pretty common metric, is simply from when you advertise to when you make an offer – it can differ for different areas (IT, HR, Legal, sales, etc). Let’s say in this example that time to recruit was 12 weeks. So now we can look at our turnover across 12 weeks, or in our case as the training program concludes in mid-June, let’s look at May, June, and July. So we know there is a high probability that in May a minimum of 12 people will leave, while a minimum of 20 will leave in July. All of a sudden we can increase the training group to an intake of 47 and at most managers will need to leave their vacancy open for 6 weeks – half the time it usually takes to recruit. Funds permitting you could repeat this training program three to four times a year and graduate over a hundred participants depending on your own organisational numbers. This is what big data in a pretty simple example can do, we can reframe problems or initiatives and create scalable solutions from simply asking some pretty basic questions.

Not notice from this there was nothing about ‘you’ in my analysis, there was no ‘I think Jerry’s going to leave because of these data points’. In big data you or I don’t feature, we are a simple part of a larger group that forms trends. And through identifying those trends, we can inform the decisions that organisations are making.

I hope this example informs the way you consider big data in HR, it's really an exciting and upcoming area of HR.

By Brendan Lys

Operating at the intersection of Human Resources and Data Science, I leverage extensive specialist experience within Human Resources, with the methodologies and approaches of Data Science. This focus on the discovery of actionable insights from data, has been applied to areas such as: remuneration & benefits, workforce planning, recruitment, health & safety, diversity, and training. But what does the application of data science to HR challenges and opportunities actually look like. Within an HR framework the data we work with typically comes directly from our HRMIS, an advantage of using data science methodologies is that we can bring in additional data either held within the organization or from external sources - data which is out of reach from a pure HR analytics approach. Consider for example position descriptions, these contain a wealth of data that we typically ignore as its not in a analysis ready format. A side project I'm working on currently (April 2019) is using text mining on job descriptions to provide insights into which job family the position may fit into. The insights of my work have been enjoyed by organizations across a diversity of sectors including: Government (Australia and New Zealand), ASX and NZX listed companies, utilities, not for profit and higher education.