HR analytics is helping companies to delve into new insights about business-related issues.
There are many applications you could use it for, but one that is commonly being used is measuring performance. For many companies calculating FTE (Full-time Equivalent) employees is a relevant piece of information for measuring the performance of a team or of an individual. The question is, how do you calculate an FTE accurately?
Like many aspects of data and analytics, the answer is somewhat complex…
How simple is the FTE question?
FTE refers to the number of working hours that one full-time employee completes within a fixed time period (for example, one year). Workload hours can then be calculated in order to figure out the number of people who will be needed to complete the work. This helps companies to plan ahead for scheduling.
A calculation of FTE also helps for forecasting and budget planning. For example, calculations can be made for the cost of labour, as well as whether it is worth hiring on anyone extra (especially if the company is paying out a lot in overtime). This is a solid definition of what FTE actually means.
Or is it?
A bit of digging uncovers that the above description is just one definition of FTE – there are actually several you could potentially use and any of them might be valid. What appears to be a simple question on the surface actually involves a lot more than at first glance.
Here are some other potential definitions you could begin with:
- The number of contracted working hours a full-time employee is supposed to work each week.
- The average number of hours per week a full-time employee works.
- The number of theoretical hours worked, that is, excluding non-contractual periods and leave entitlements.
- The number of hours worked minus any meeting or training times (the number of “productive hours” has different definitions depending on the company, too).
The calculation of FTE can get very complicated, and this is one very good reason to have clearly agreed-upon definitions. Consider how many different categories of staff exist too. Some work the agreed definition of full-time, while others work part-time.
Besides that, not all employees will be working the calendar year from 1st January to 31st December. Your employees may start or finish at any time during the year. You might find that for some analysis, it’s acceptable to just pick staff members who have worked the full year.
Another factor impacting FTE calculation is any leave entitlements staff members have. This often involves different numbers of days and types of leave. Some may have maternity leave, paternity leave, sick leave or annual leave. How do you account for these in your calculation? Each company tends to have a different method, depending on their business model.
At least, the difference between the headcount and the FTE is not so difficult. The headcount of staff members is the number of staff that are employed during the year, or across a period at any point in time. I also include people who have started and ended during the daytime period, as they need to be counted individually.
FTE is an HR analytics calculation that sounds simple, but that’s only the surface of the story…
How do you explain your FTE calculation?
The problem with defining FTE so that it makes sense to your organisation is just an introduction to a much bigger and very typical problem: explaining your data and conclusions to a non-technical audience.
In fact, this is a communication dilemma that HR analytics often faces, no matter what the calculation is. Usually, you will have to explain to executives who don’t have the same level of knowledge about managing data and making calculations. The expert practitioner often wonders, is it better to explain things to them in complex detail, including the information that complex internal accounting mechanisms can provide? Or, should the HR analytics analysis be explained in a more easily digestible way?
You’ll often find that senior managers want to cut to the chase. They don’t care so much for definitions and complex explanations, they want to see results. It’s a fine balance between explaining so as to convince them of the validity of your analysis, and providing the high-level conclusions they ask for.
How do you tell your story?
Focusing on FTE, let’s say you’re making a case to hire more people. You’ve done your research and some relatively complex calculations and have reached the conclusion that there is a gap in the workforce.
You job now is to convince someone else (probably a senior executive or group of executives) of why they should add that expense to the labour budget. You could talk to them about data and numbers, going in-depth into what lead you to that conclusion and you certainly should be able to back what you’re saying. However, it’s how you tell it that’s important.
Storytelling is a key skill to develop. A good HR analytics practitioner needs to be able to balance handling the tedious definitions of data analysis along with presenting things clearly and convincingly. You need to be able to explain HR analytics, otherwise no action is taken by the company and the entire analysis becomes fruitless.
Unfortunately for many HR analytics experts, the skill of being able to tell a story is completely different from the skill set required to analyse the data. This is something to learn and practice if you are not a skilled storyteller.
Establishing those agreed-upon definitions matter very much in HR Analytics as they speed up the communication from the analyst back to senior management. They add weight to your story and help improve the chances that action is taken as a result of your analysis.
Communicating your story clearly
Data has been an important part of many game-changing stories. There’s a famous example of Florence Nightingale, who upon analysing mortality rates from the Crimean War, realised that death in combat wasn’t the cause for the majority of soldiers. Most had succumbed to diseases due to unsanitary conditions in the hospitals.
Saving the lives of soldiers and winning battles were priorities for the British Parliament and Queen Victoria. Under the expensive climate of war, Nightingale now had to convince them to invest more in better sanitary conditions for soldiers.
What did she do? She told a data story. Her “Diagram of the Causes of Mortality in the Army in the East” is considered a significant early example of data journalism. By clearly articulating the data and why it mattered, Nightingale successfully convinced the Queen and British Parliament to make the investment in sanitation, saving countless lives.
What can we take away from the success of Florence Nightingale? In telling a story, she tapped into the priorities of those whom she had to convince – save more lives, win more battles. She backed up her claims by presenting the data in a way that was easy for them to digest and reach a conclusion from.
These are two important things to consider when you’re telling your data story: How will you relate FTE (or whatever the case may be) to key business priorities? How will you share the data in a simple, yet convincing way?
Final thoughts
As a reader, you form part of this story too. You first came here looking at the problem of “calculating FTE,” but it turns out there is a wider story to tell. Having those agreed definitions helps us to communicate clearly and tell the story of our data better.
Your own FTE calculation will be based on your organisation’s “story” and structure. However you define it, you will need to identify business priorities and be able to highlight how what you’re saying matters.