HR

The Next Wave of Predictive Analytics in HR: 5 Tips for Success in 2017

Darren Shimkus

VP & GM, Udemy for Business

Blog

  

HR

January 26, 2017

Just like computers and smartphones transformed today’s workplace, the next wave of predictive analytics and machine learning is drastically altering functions across organizations from finance and marketing to HR. As outlined in our report 5 Workplace Learning Trends and 5 Predictions for 2017, we’re beginning to see some organizations leverage huge employee datasets and predictive analytics to make much better strategic decisions. Talent is the capital of the modern economy, and we are entering an era in which human capital is measured, quantified, and managed as diligently as financial capital was last century.

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Using predictive analytics to apply HR data to enhance employee retention is an example of how machine learning is disrupting HR. By sifting through survey data from exit interviews as well as employee data on compensation, performance reviews, and engagement, HR professionals can now identify the root cause of systemic departures and focus on retaining vulnerable groups of employees. In the long run, this saves companies the cost of hiring replacements—something the C-Suite cares about.

Today, the power of machine learning and people analytics is becoming mainstream. According to Bersin by Deloitte’s Human Capital Trends, in 2016, 51 percent of companies correlate business impact to HR programs, up from 38 percent in 2015. 44 percent use workforce data to predict business performance, up from 29 percent last year. However, only 8 percent rate themselves as doing an ‘excellent’ job, and there’s still much more work to be done.

In 2017, I expect predictive analytics in HR at organizations will mature further as companies get better at measuring, cleaning up, and analyzing the data. Although organizations understand the importance of having a predictive analytics strategy, HR professionals are hitting roadblocks along the way as they attempt to implement data management and analysis across departments.

What are some of these barriers and how can you overcome them?

As you enhance your HR data capabilities this year, here are a few tips to consider:

1. Get buy-in and hire the right people

Predictive analytics has matured over the last few years and there are plenty of good vendors out there for HR. However, the challenge lies within companies. Getting buy-in from all departments from finance to legal is essential to starting out on the right foot. Organizations need to determine where they draw the line on what data they will use to make decisions (for instance, performance reviews or compensation brackets), and which pose potential privacy concerns (correlating employee web browsing behavior with departures). Once ground rules are set, the lack of strong analytics talent within HR departments is also an obstacle to effectively making sense of all the great data out there. Finally, getting your CHRO and C-suite leaders on your side is essential to ultimate success—if they see the business value, you’ll get all the resources and political capital you’ll need to solve the other roadblocks.

2. Garbage in, garbage out: data quality is a big issue

As companies rush to measure everything and churn out all kinds of charts and metrics, the quality and accuracy of all this data is becoming a critical issue. Unfortunately, metrics are only as good as the raw data that goes into them. The problem arises when various departments measure the same thing differently which is like comparing apples to oranges. All this data will need to be sifted through and scrubbed for inconsistency, duplication, or incomplete and inaccurate entries.

The scarcity of analytics talent within HR departments is also a challenge for cleaning up complex data sets. HR teams also need someone who is specialized in analyzing unstructured data like internal social media comments, images, or videos that can provide insights into individual employee engagement and sentiment. Leveraging data science talent in other departments within your organization may be a short-term solution to addressing your talent gap.

Moreover, despite all the rage around quantitative analysis, all these numbers mean nothing without context. Qualitative research in terms of surveys can provide meaning as you interpret what your data actually means. For example, correlating employees that leave with compensation data isn’t going to tell you a whole lot unless you combine it with qualitative feedback on why those people chose to leave.

3. Think big, but start small

Don’t try and implement people analytics to solve every issue in your organization all at once. Answer one problem first and do it well. At BuzzFeed, their HR department distilled key traits of successful BuzzFeed managers and focused leadership training on these specific skills.

A common mistake for HR venturing into data analytics for the first time is churning out lots of interesting data without really knowing whether the information is actually adding any value. Don’t measure for the sake of measuring. Map your metrics to your goals. Be very clear what you want to find out and why. Then follow-up with a playbook to actually solve the issue uncovered by your data insights—whether it’s better leadership training or a targeted employee retention program.

4. Democratize the data to build grassroots support

It seems obvious that you have to get your CHRO and C-suite on your side from the beginning. However, what’s less obvious is creating grassroots support throughout the organization at every level. By democratizing your new data and ensuring everyone can access it, you can create excitement and support across many different teams. Build the infrastructure so that teams throughout the company can self-serve and use this new data themselves. After all, if employees aren’t applying people analytics to make meaningful business decisions, then you haven’t succeeded in your mission.

5. Have a roadmap to build momentum in phases

Design a solid roadmap to roll out people analytics in key phases across your organization. For example, start with compensation data, and then build in benefits data and so on. It’s important to start small in the first phase with a pilot and get it right. This helps to manage expectations and build momentum for your program. Your people analytics program isn’t going to solve everything all at once, but piece by piece.

Mapping people analytics to business goals is your ticket to the C-Suite table. (See 4 Ways HR Can Win a Seat at the C-Suite Table). As more companies sharpen and enhance predictive modeling capabilities this year, here’s to a more powerful data-driven HR in 2017.

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