It is good to see that the number of job postings for data analytics positions is increasing. However, when I check the details of the job definitions I see that most of the companies are looking for people who will be responsible from creating metrics. Although working with metrics is a good starting point, analytics is much more than that. In this blog post, I want to give you a clear definition of HR Analytics and explain the difference between those two.

Actually, we are all familiar with metrics. For a long period of time we have been working with tens of metrics like turnover rates, cost per hire or time to fill, and honestly speaking, we are very good at following them. So what happened and all of a sudden analytics became the buzzword for HR? In order to answer this question, first of all, let’s take a look at the definition of analytics:

In their book ‘Investing in People’, Cascio and Boudreau make the following definition:

‘Analytics is about drawing the right conclusions from data. It includes statistics and research design, and it then goes beyond them to include skill in identifying and articulating key issues, gathering and using appropriate data within and outside the HR function, setting the appropriate balance between statistical rigor and practical relevance, and building analytical competencies throughout the organization.’

Here is another definition from Martin R Edwards and Kirsten Edwards –authors of the book ‘Predictive HR Analytics’:

‘The systematic application of predictive modelling using inferential statistics to existing people-related data in order to inform judgements about possible causal factors driving key HR- related performance indicators.’

I think it is obvious that analytics is an ongoing process which requires a long-term perspective while metric is a one-time piece of data.

Long story short, if working with metrics is taking the snapshot of a moment, working with analytics is shooting a movie to tell the story of that moment.

Let me try to make it more lucid with a turnover example. Let us say that for 2016, the turnover rate of our company is %5. This piece of data alone is not worthless; however, we need some more to elicit the value in it. When we add the information that average turnover rate of the industry is %7, it might be possible to conclude that we are making better than the industry. Although this conclusion is true, we have to be careful because it masks some of the questions that we should ask: Why is the turnover rate 5%? What are the effects of %5 turnover rate on the basic financials of the company? What is the optimal turnover rate for the company? What will be the effect of 5% turnover rate on the long term strategic plans? Could it be hi-po’s leaving the company and creating %5 turnover?

Metrics help you in supporting status quo, while analytics force you to challenge it.

It is the analytics allowing us to drill down the metrics, ask the right questions, find out the truths and support the strategic decisions regarding the HR management.