Why is it So Hard to Break Into HR Analytics?

I’ve been asked this question a few times; why is it so hard to break into HR analytics?

My own journey to get there involved the steady development of new skills in the HR and data analysis fields, a lot of which I had to learn in my own time. I have been fortunate to be able to use and prove the value of these skills in my work, but I still often hear that people find it difficult to break into the field.

Here are a few of my thoughts on why it can be hard to get into HR analytics:

There’s a shifting paradigm

We’re seeing a paradigm shift in how we build our reputations and careers. Once upon a time, it was sufficient to pass a severe entry test or do an apprenticeship to gain your professional role. Perhaps you could publish a scientific paper, or patent a new invention and you’d be set up for a lifetime in your career of choice.

In short, the old paradigm was, first you jealously and secretly acquire knowledge and then you share your finished products (but not your knowledge) at the end.

These days, things have changed. There’s an expectation of lifelong learning for any career and the pace of innovation is staggering. There are many scientific papers that can be studied for verification and reproducibility of results.

Our paradigm shift is seeing an expectation that the fruits of your learning be shared immediately. These newly acquired skills must be made available to others so that they might replicate your results. In HR Analytics, that means sharing your methodologies, your findings and your ideas for further research.

In line with this, you will see many well-known data scientists – David Robinson, Hadley Wickham, Max Kuhn, Julia Silge – share their knowledge. In fact, they have gained strong followings for doing so.

So, one reason that breaking into HR analytics is difficult is because developing that knowledge and building platforms for sharing it is not easy. In fact, it can be downright time-consuming. How do you teach others your skills? Do you have a blog on which you share information? Do you tweet about new and valuable ideas? Do you contribute to the open source community? Do you give talks or write books? Increasingly, the people who get farther into the field do some or all of these things.

David Robinson gave a great presentation at the 2019 Datacamp on the idea of building a public face for your work, entitled The Unreasonable Effectiveness of Public Work. Why spend time on public work, he asks? Because you can advance your career, practice good habits and contribute to the community.

For Robinson, starting a blog meant that he could build a public portfolio, practice writing and visualisation, and deliver a scalable way to teach others what he knows.  He uses Twitter to both promote his work and encourage discussion. He also uses it as a platform to share the work of others in the HR analytics field.

The steps are basic, but difficult

The steps leading to usable HR analytics predictions seem quite basic at face value. It’s usually four steps that look like this:

1. Gather raw data
2. Clean the data
3. Build a model to process the data
4. Make predictions based on your results.

It’s quite a well-defined process, but each step involves multiple tasks and choices, and a significant amount of learning and practice to master. Many people have to work a day job while learning these skills (I did that myself).

The fact is, it takes time to build up the necessary knowledge and practical skills. Gathering data isn’t a simple step – you need to understand R or SQL, have knowledge of relational databases and understand exactly what makes data useful or not.

For data processing and modeling, you need to learn a language such as R. This is a huge (but rewarding) undertaking. I’ve been using R for a few years now, but I still continue to learn new things about it.

You need multidisciplinary skills

HR analytics requires multidisciplinary skills. At the core of data science in the workplace, you need to be able to understand and translate business questions or problems into data-based questions. It certainly helps if you have skills and understanding in the HR field too.

The bar is set quite high for entry, although once you’re in, you will probably never struggle to find an HR analytics role again. Companies tend to have few data science roles, so they usually want to hire people who are already highly competent with multidisciplinary skills. Unlike programming where there are often junior roles, HR analytics doesn’t tend to offer them.

You will need strong statistical skills, computing skills, programming skills and business knowledge or skills. Each of these things on their own can take a lot of time to develop. As the field of data science as a whole is still relatively new, learning institutions are really only beginning to create curriculums around it. In this sense, for many people who would like to get into HR analytics, developing those skills will happen in stages.

Companies are still learning

The field of HR analytics is still very new to most companies. According to analysis from Deloitte, while 71 percent of companies see people analytics as a high priority in their organizations, progress has been slow.

Readiness of companies remains a serious barrier despite years of talking about it – only 8 percent report they have usable data; only 9 percent believe they have a good understanding of which talent dimensions drive performance in their organizations; and only 15 percent have broadly deployed HR and talent scorecards for line managers.

There are many factors contributing to this. One that has been noted is that companies want experienced data scientists for which they need to pay top dollar. At this stage, getting an experienced scientist is highly competitive, while many companies have been unwilling to invest in training current staff to do the job (this has been particularly noted in Silicon Valley).

Deloitte identifies eight factors important to creating a successful people analytics program, including prioritising clean and reliable data, and increasing analytics fluency across the organisation.

On the behalf of HR analytics practitioners, I always say that it’s important to be clear when we’re talking about and advocating for the role of data analysis. You have to speak the language of the company so that they understand the value they are getting from the investment. They’re not hiring for “data analysis” per se, they’re hiring to have their problems solved. Every company will look at data analysis differently because their problems are different.

Final thoughts

Why is HR analytics difficult to break into? There are multiple reasons why, but the bottom line is that it takes time and effort to build up the different skills and the reputation or “proof” that you’re the right person for the job.

There’s also the fact that companies tend to want to hire very experienced data scientists, if they’re hiring at all. The field of HR analytics is growing, but there is a disconnect between the number of organisations that state people analytics is a priority, and the number that actually have a good program in place.

I would encourage you to stick with it though. Keep practicing, be a lifelong learner and build a reputation for sharing what you learn. It might not happen right away, but you can get there, into the HR analytics role that you aspire to.