In today’s digital age, Human resources department is an outlier. Not in good sense, though. Just look at other departments & observe how data driven they are. Right from sales to products, finance to marketing. Each function has its own set of metrics, surfaced by the data from relevant sources. But HR metrics, in majority of the cases, is limited to presentations & executive briefs.
Just imagine how much data following processes can generate for the HR team.
- Learning & development
- Performance management
- Leave management
- Competency profiles
- Employee background data
- Employee productivity
Amazing thing is, this is just a subset of such processes. Larger the organisation, more diverse processes and a larger dataset.
Given the abundance of data points available, it is imperative to limit scope of this article to one of them. Namely, employee data. By employee data, we refer to the personal (not so personal, actually) attributes . Some of such attributes are listed below:
- Age/Date of birth
- Joining date
- Marital status
- Educational qualification
- Residential address
First & foremost, we are not talking about using this data at an individual level. What we would want to do is, extract a macro level insight from this.
Traditional HR metrics, where performance is measured in hindsight are ‘reactive’ in nature. We measure something, compare that with industry benchmark & decide future course of action. The insights available from employee data can help increase employee engagement ‘proactively’.
Let us jump right into a few interesting examples.
- Gender, Race, Nationality already play pivotal roles in our age’s diversity & inclusion debate. In fact, many fast growing companies religiously publish diversity reports. Each one of us will have different opinions, whether diversity at work helps company grow or not. But keeping ourselves informed is the least we can do.
- e.g. take a look at diversity data from Slack published on Techcrunch
- Diversity & inclusion is just one perspective. Another can be, designing workplace based on gender distribution data.
- Age/date of birth provide us with a well known, but overlooked demographic. If the average employee age is below 30, the company may have high attrition rate. If that average is somewhere between 30-50, chances of burnout shot up. And so on. (Reference) This data can help HR department put in place appropriate incentives, proactively.
- Joining date/stay at current job attribute, at a macro level can help predict employee turnover. US bureau of labor research says, people live with an employer for about 4.2 years. HR department will find this data really handy.
- Insights on residential address can help you make an operational decision. Whether the current office location is convenient for everyone, or whether transport arrangement might be more beneficial. Just look at what areas your employees live in & you’ve got an opportunity to make their lives easier.
One common reason for lack of these proactive measures, is difficulty in gathering such data. When it comes to collecting & analysing personal data, laws & regulations vary across countries. There could be legal implications if this data is not handled properly.
Second, lack of centralised employee records in digital format. Spreadsheets & physical documents still prevail in HR departments. With that, it is difficult to make any meaningful sense out of them.
And lastly, lack of technical knowledge plays a spoiler for the HR team. But with adequate training, this shortcoming can be easily addressed.
As you can see, the possibilities are endless with this magnitude of data. While the rest of the functions are making positive strides in their respective areas, HR is still a bit behind.
With the advent of AI, data-driven decision making is the next big thing in HR. Sooner than later the industry is going to see a shift in how it proactively builds an engaging work culture.