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People Analytics Is HR’s GPS

Author - Morten Hartvig Berg, Head of People Insights

Unless your birth year starts with a 2, chances are you have tried using a physical map to get from A to B or that, from the backseat of a car, you have seen your parents attempt to do so. Sure, you generally arrived at your destination, but rarely via the most effective route and often with added frustration. People Analytics is HR’s GPS navigator: a tool that finds the optimal route and alerts you of roadwork on the route you have chosen. 

Most people in HR have some idea of what People Analytics (PA) entails, but with slight variations, depending on who you ask. “It is dashboards,” many will tell you. “It is a kind of artificial intelligence,” others will say. “It has to do with large spreadsheets that make for big headaches.” The first two are correct – the last is an example of poor execution. The truth is there are multiple levels of People Analytics (see figure 1), and they range from manually built spreadsheets to artificial intelligence that predicts the future and tells us how we change it.

 

People Analytics is HRs GPS fig1_us

(Figure 1 - Levels of People Analytics)

 

You must learn to crawl before you can walk, as they say. But crawling is also moving forward, isn’t it? The same is true of PA projects. Even fledgling level-1 projects will result in insights that can support the business’s decision-making processes. In fact, such projects are often those that make the biggest impression, since they are delivered to stakeholders without expectations – kind of like a baby's first few steps. However, I would not necessarily advise anyone to rush over to the board room the instant the first insights have seen the light of day.


Work backwards

Once you have started working with PA, it can be hard to limit the data and insights you want to share with stakeholders in the company. But it is important to reign in this initial enthusiasm, to ensure that PA gets the necessary foothold from the start. What absolutely must not happen is that the company reacts to HR's “eureka insights” with a shrug and a “so what?” Like all the other initiatives HR wants the company to get involved in, they must be linked to the company's goals. That's why rule number one in PA projects is: Start with a business problem. As employees are the primary resource in most companies today, most business challenges and goals will also have a connection to the employees. From a PA perspective, the task consists of identifying this connection and the data points that can provide support. And if we are to shift into high gear in terms of making an impression on the company, HR naturally doesn't just contribute sharp analyses of “what” and “why” – it also provides a list of opportunities we have, together with the company, to do something about it.


Maddening, wonderful data
The journey to PA perfection starts with identifying data that is 1) relevant and 2) correct. Here, some HR teams will lower their heads in defeat and admit that “we don't really have any overview of our data,” or “we use five different systems that don't talk to each other.” This is a very common reaction in HR departments that have not yet taken their first step up the PA ladder. But as we said, HR should not present PA analyses to the company for the sake of the analyses. There must always be a specific business problem or priority. And precisely for that reason, it is not necessary to have a bird’s-eye view of what HR data is available. We work backwards from the question: “What data can help us to identify and measure precisely this issue.” Often, very few data points are needed to gain sufficient insight on which to act.

Equally as important as determining what data is available in relation to a specific task is determining what data is not available and then deciding whether we should begin collecting such data in the future. For organizations on the first PA step, a very common example is historical data. We may have control over who sits where and what they do. But what if we were able to map our high performers’ paths through the organization and thereby contribute to best practice in our talent development? Then we would be able to react on discovering that some of our talent has been in the same role for too long, for example. Even for companies without advanced HR systems, this would not take more than monthly extraction of the master data we want to be able to follow historically.

Read the blog post: People Analytics uncover the optimale span of control

 

Another example of a useful data point is recruitment channels. Do we actually know how our candidates decided that they wanted to apply specifically for this job with us? If we do not have that data today, we could begin collecting it starting tomorrow as part of the recruitment process. In just a few short months, we would be ready for the first evaluation of which channels produced the best candidates and thus where to focus the budget going forward.


An example: Gender diversity

Let’s look at an example from a topic that currently has a high priority among many companies. Despite gender diversity not exactly being a new concept, there are still many organizations today that choose to work actively with inclusion and diversity. And while diversity is much more than just gender, that is often where the work starts, especially with focus on increasing the percentage of women generally among staff – particularly in management roles.
Most HR departments can tell you how many women there are in the organization in general and can probably also divide the number by parameters such as location and management/non-management. But what if we added the manager's gender? Then we would be able to show figure 2, for instance.

People Analytics is HRs GPS fig2_us

(Figure 2 - Gender composition and manager's gender)

 

Here there is a clear tendency for the gender composition of the team to reflect the manager's gender. In fact, the numbers here reflect the situation that exists in many Danish companies, and this simple analysis is particularly useful for showing the existence of unconscious bias.

So here there is insight that can be used, e.g., to argue why an increase in the number of female managers would automatically lead to more women employees in general. But the numbers also support taking a closer look at gender ratios among new hires, including how women are represented among applicants, interviews and job offers. At the same time, this should be held up against the gender of the hiring managers.

The next question could then be: “Why don't we currently have more women in management?” Here it could be relevant to study hiring and promotional data to check for any imbalances. Additionally, it would be a good idea to check job satisfaction among the women in management. We do not create role models among our managers by putting them in roles where they are unhappy and then likely to leave the organization. Figure 3 shows an overview and engagement score by gender and job level.

 

People Analytics is HRs GPS fig3_us

(Figure 3 - Engagement score by gender and job level)

 

For this company, there is a clear imbalance in well-being. While the male engagement score rises sharply with the rise in job level (a very common phenomenon), the situation is almost the opposite for women. There is clearly a need here to take a closer look at the work conditions for female managers before any attempt is made to increase their numbers.


PA is a business tool

Long gone are the days when HR's role was limited to hiring, payroll and dismissals. HR’s potential work areas are expanding all the time, as are the company's expectations. Therefore, one of the HR director's primary tasks nowadays is to prioritize, with a heavy hand, between an unending list of initiatives, all of which have the potential to strengthen engagement, leadership and performance in the company. PA will be a big help in doing this – see figure 4, for instance, where PA methods are used to determine which areas have the biggest influence on engagement for a particular group of employees.

 

People Analytics is HRs GPS fig4_us

(Figure 4 - People Analytics methods used to determine areas with the biggest influence on engagement)

 

Despite the many benefits, a reinforced PA effort would have to fight for resources on the HR director's said list – but it does not have to be like that.

PA exists primarily to provide support in the company's decision-making processes. So although it has to do with staff, HR does not have to own PA. Often, HR will not have the analytical resources needed to begin with, and therefore they will either have to be trained or hired. But there is an alternative. Take your PA wishes and your data and head down to Finance, IT or Sales and Marketing. The likelihood of finding the skill set you need among these teams is high. And since PA is a business tool, it is only fitting to work together on an interdepartmental project that is financed centrally.

While People Analytics may seem like yet another item on the we-should-really-get-started-soon list, I recommend looking at it in the same way as when you (or your parents) got their first GPS. It was a little expensive but, oh, how much easier it made getting from A to B.

First published in the magazine ‘HR Chefen’ no. 4 by Dansk HR

Morten Hartvig Berg, Head of People Insights
Author

Morten Hartvig Berg, Head of People Insights

Morten has solid international HR experience and advises our clients who apply employee journey concepts. He is responsible for our People Analytics concepts and contributes to concept development in general.