Have you wondered about getting started with HR Analytics and what the future holds for the field?
Given that it’s a relatively new field, these are the sorts of questions I get asked often about my work. HR Analytics is catching on, but it’s still a growing area with many companies yet to embrace the opportunities it can offer.
Here, I offer my answers to some of those common questions:
My background and entry to HR Analytics
I enjoyed dealing with people in a business environment from very early in my career. I love the idea of organising the work of others. I graduated from the Bocconi University in Milan with a degree in Business Administration in 1988 and enjoyed specialising in Human Resource Management.
It was toward the end of my studies that I discovered I was quite good at applying numbers and data in the Human Resource environment. I may have been one of the first using Microsoft Excel for calculating cubic regressions of salary payments in my country. At the time I started using Excel, we were using the Hay Method, a point-factor job evaluation methodology which enables HR practitioners to provide valid pay comparisons between organisations, nationally and internationally.
In my first position, I enjoyed interviewing former classmates and colleagues from my university days. These days, I chair selection committees and often write the tests which candidates have to undergo.
Collecting evidence, and calculating and providing unbiased advice on Human Resource issues continued to be a core part of my work through various workplaces over my career. In my role now, I look after a complex Human Resources Information System, based on SAP HCM. I love my work, however I’m always looking at new paths and challenges.
As the leader for the Human Resources Information system, I am constantly implementing new business processes, solving problems and cleaning HR data. Around four and a half years ago, I started to hear about John Bersin’s revolutionary predictions about HR Analytics. I thought, that is exactly the stuff I have been doing for a while, so I became more interested in furthering that work. After having written a couple of LinkedIn articles on the subject, I was invited to speak at an HR Analytics conference four years ago.
Another important step in my career with HR Analytics is that I have spent a lot of my personal time training myself to learn R and statistics. R is a leading tool for HR Analytics and opens up a world of possibilities. Having a strong passion for HR Analytics helps too!
My biggest learning curve with HR analytics
All-around data scientists do not exist (or are at least, very rare!). I am no exception to this – my strengths lie with understanding the possibilities enabled by technology.
There is a tool that helps you to understand your own “data scientist profile.” Data Science Radar is a conceptual framework that allows users to explore their character traits in more detail and uncover their own profile. For me, that is “the technologist.” I have had to work hard to develop the data science skills to be useful for HR Analytics.
A contemporary data scientist has six core attributes, according to the Data Science Radar. Your skill set will always be a mix of these, with some areas stronger than others. Those six attributes are:
- Communicator – takes complex data science and explains concepts to a non-technical audience.
- Data wrangler – integrates data from multiple sources, solving common transformation problems.
- Modeller – gets to the bottom of the mechanisms behind the data
- Programmer – uses R to build automated workflows and makes work more efficient.
- Technologist – applies the knowledge appropriately as the opportunity arises.
- Visualiser – helps to make the final HR Analytics results easy to understand. This is vital for communicating the final message and having business impact.
So, you don’t need to start out as a data expert, but you do need to work on the areas you’re not as strong in.
What people entering HR analytics need to know
In a world where we must adapt every five years (or even less) to new technologies and new paradigms of thinking, new developments are happening at an incredible pace. This presents immense opportunities for those who wish to take advantage of them.
Having an attitude of being a “lifelong learner” and making the effort to keep yourself informed is a good place to start. The ability to keep learning is your most important career skill. Technological advances are inevitable – being aware of what’s happening and how that might impact your career helps you to stay on top.
Learning coding with HR data, even if it is something that is completely new to you, will keep you ahead of the game. Take part in regular coding exercises in order to stretch yourself and stay current. Coding gives you massive productivity gains professionally, and still today, will often set you apart. Only the very few who have the ability to code can address their message to mass audiences. I believe that eventually, everyone will need to be able to code just like how everyone needs to read and write.
As you will know, HR information, just like other types of information is subject to confidentiality, privacy and data protection laws. To realise the full benefit of workforce analytics, genuine employee data is the key. This means that it is critically important to acknowledge employee concerns around data privacy. Trust is one of the most important issues to deal with in people analytics.
When it comes to trust, I like the way Jouko Van Aggelen has highlighted four key elements for people analytics:
- Trusting the value in people analytics is worth sharing data for
- Trusting the quality and integrity of the data
- Trusting the insights that are generated
- Trusting the motives of those using the data.
If you don’t have trust across all of those key areas, people will be unwilling to give you their data, or may simply give you inaccurate data.
Those in the people analytics space should focus from early this year on producing truly compelling and peer-reviewed evidence of the utility and validity of their products. Without this, survival of the analytics product is unlikely.
HR analytics has a high hygiene factor. When you are good at data cleaning, no one seems to care. However, if you are not, you can be in big trouble, contributing substantially to the loss of credibility of your HR analytics function.
Ethics is something that should always be on your mind. Who do you think is responsible for considering the potential impacts and risks of data science and AI? Think about the last time you discussed questions of data ethics or impacts of your work (or your organisation’s work) with your colleagues. How long ago was it?
How to get the experience required for jobs in HR analytics
My number one answer for this is to participate in competitions. These help you to develop your skills, challenge yourself and build experience on your resumē. You can test out your new ideas and you may even get noticed by employers or recruiters in the field.
The skills required to be successful in HR Analytics are multi-faceted. HR Analytics is at the intersection of three bodies of knowledge – a concept known as the Venn Diagram of Data Science, invented by Drew Conway.
Those three bodies of knowledge are:
- Domain knowledge in HR Management, which sets the context for the analytics
- Computer programming skills, such as knowing how to process and store data efficiently, automating collection and cleaning of data
- Statistical analysis, interpretation and presentation. This helps in translating an HR question into an appropriate analysis and communicating the results.
For every task or project you are given, try to fix it for good, such as advocated in the Geek vs. Non-Geek Approach:
Final thoughts
One thing to remember if you’re new to or looking to enter the HR analytics field is that there is no perfect, all-around data scientist. You need to identify the areas that you are strong in, as well as those that you could work on developing further.
As an HR analyst, it’s important to remember that all the core attributes work necessarily together. You won’t get good insights without developing data skills, you won’t get results if you’re not able to explain it in layman’s terms, and you won’t get good data if you don’t adhere to critical HR privacy and trust requirements.
Look out for Part II, where I’m looking more at the future of HR Analytics and the role it will play in companies.