On the face of it, the words “HR” and “Data” are pretty far away from each other.

But like every other function, HR has been impacted by the cultural moves towards using data which we’ve witnessed in the past decade.

Forget AI for a second, and forget that nightmare Mathematics course you took at school which left you scarred for life – let’s talk about a simple example of how leaders who aren’t data experts can use data really powerfully, to help them make better decisions in the area of People and HR strategy.

Tenure. However poor the IT and systems are, or have been, in your business, you probably have access to these two data points for each employee:

The date they started and the date they left (if they’re not still employed).

For this exercise, the further back you can go, the better, although if your business has changed systems, been bought or sold in recent times, then it’s likely that it will you will hit a challenge at some point, the further you try to go back. Include at least recent leavers in your analysis, as this will help you spot any trends over time.

Build a basic table like the one below, in Excel or equivalent (Note – all this data is fictional!) – download this data sample here (.xlsx file, 15KB).

• Column 1: Unique reference number (Employee/payroll number etc)
• Column 2: Employee Name (or if you want to/ have to anonymise the data, then don’t include this);
• Column 3: Team/Department (Finance, Sales etc – the further back you go, the more likely the business will have undrgone a restructure, so be sensible about how granular you go here);
• Column 4: Level of Seniority. Record their seniority today, not their seniority when they joined. Again, be sensible here, don’t try to capture too much detail;
• Column 5: Start date
• Column 6: End date. Put N/A in any lines where employees haven’t left
• Column 7: Today’s date (if using Excel, you can get this by simply typing into the cells: =TODAY()
• Column 8: Calculate historical or actual tenure. There are various ways to calculate this – the way I’ve done it is: =IFERROR([@[Departure date]]-[@[Start date]],[@TODAY]-[@[Start date]])
• I’ve added Columns 9 and 10 which are the same as Column 8, but just showing Tenure in months and years – it’s sometimes easier to see this data in these time periods rather than days.

Once you have the raw data, here are some ideas of what you might look at as an initial analysis:

Look at tenure sliced by Department: compare how Departments are faring; benchmark against typical expectations in various functions. If there’s a Department which is trending way lower than the others, it’s worth at least exploring whether there’s an issue with the way the team is being managed, or checking their remuneration is competitive.

Sliced by Seniority, you can see how long people at different levels of your organisation are hanging around. Are there any signs that there’s a problem at any particular level of Seniority? That might prompt a decision about where to invest in more training or other engagement tools.

One I’ve used when joining a turnaround situation – look at number of leavers by year. This helps understand the history of the organisation – was there a significant exit in a department, or en masse, at some point in the company’s history? In this fictional data, there seems to have been a big exit of people in 2022 – what happened? And how/why were the numbers of departing people so low in 2017/18 (don’t forget to look at years where there was ‘no data’, like 2018 in this case). This might prompt you to ask some specific questions from ‘Old Timers’ to better understand the company’s history.

Another reason this is a great exercise to do is that old maxim – knowledge gives you power (authority may be a better word for this context!). When you’re walking the shop floor and you hear one of the team lamenting how people aren’t hanging around as long as they used to – if you have looked at the data and it’s telling you something else, then it’s a great way to have a discussion. The same at the next Board meeting – if there’s a discussion about engagement or the cost of recruitment, it’s great to be able to show any particular issues using data to help make your case.

Limitations aplenty with this exercise – for example, many employees may have been promoted over time, so watch out for extrapolating too much specific insight into a particular department/level of Seniority – especially if it’s an organisation with a relatively small number of employees. That all said, there are countless other bits of insight you can get just from this simple dataset.

Please share any ideas of how you might use data to better understand a business from a people perspective, and make better decisions on the back of it.