Data Analytics and the Future of HR

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Data analytics has been named one of the top HR trends we’ll see in 2020, and for good reason.

As candidate and employee information becomes increasingly available, HR professionals across all industries need to equip themselves to address more complex decisions around recruiting and workforce planning. Being ‘data literate’ allows us to benchmark against industry standards, diagnose problems, and come up with actionable solutions.

But before you run out and sign up for that ‘Intro to Python’ course, it’s important to take a step back and look at what you’re hoping to achieve through data analytics.

You Can’t Fix What You Can’t Measure

The more digital touch-points present within an employee’s life cycle, the more opportunities HR teams have to be proactive instead of reactive with their workforce. This includes abilities like predicting turnover, measuring ROI on training, measuring team productivity, and even measuring engagement. 

Imagine your team has access to an employee’s data from recruitment, onboarding, ongoing training, performance reviews, coaching interactions, promotions and lateral moves, all the way to when the employee leaves the company, and what they end up doing afterwards. With access to this level of data, HR can gauge how employees perform in certain types of roles, and use that data to optimize roles, work environments, and training accordingly.

What Is Analytical Thinking?

I’ve always enjoyed statistics, so when colleagues ask me how they can improve this skill for HR, I usually probe them with this question:

When your work gives you an assignment with multiple variables, relationships, and a blank spreadsheet, which of the following is likely your reaction?

  1. Not sure where to start… 
  2. I can articulate in words what I want, but can’t translate this into functions
  3. I’m able to build formulas, but they’re not always the most elegant  

The type of training you pursue depends on how you answered this question.

For those in the first camp, to gain confidence with analytical thinking you’ll need to start at the bottom (of Bloom’s Taxonomy that is). This includes becoming familiar with concepts like sampling, variance, and correlation. Without a basic understanding of how to effectively collect, filter, and present data, HR cannot be an active player in truly insightful discussions.

What if you can intuit the problem, but don’t have the numerical skills to back up your assumptions?

Then you’d likely benefit from training which is more focused on the applications of Excel. Yes, there are plenty of HRIS solutions that can do this work for you and skip right to the beautiful graph. But if you’re not able to apply your understanding of how the analysis was built, you’ll have a hard time tracing back errors when automated technology makes faulty assumptions.

How To Become Data-Centric

Integrating analytics into HR will result in smarter teams and better alignment with the rest of the business.

But in order to get started, management needs to invest in their HR teams, giving them the tools they need to understand how to work with data and build the confidence to push the conversation further. 

As HR teams shift their focus from compliance to forecasting, reskilling opportunities are necessary to give members the knowledge they need to effectively use these new tools. Luckily, a lot of resources have become available which can support HR teams in their mission to become data-centric.

In an increasingly digital world, technology is beginning to permeate every industry and profession. HR teams have the ability to use new tech to create impactful and permanent change to ultimately help their companies forward as a whole. 

 


Van Le is director of HR for Lighthouse Labs. She assists professionals seeking training and reskilling opportunities. Lighthouse Labs’ new Intro to Data Analytics course provides HR professionals with the skills needed to analyze and interpret data, allowing them to get the most out of their existing data, and make better business decisions.  

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