How to Make HR Analytics Simple and Human

By Michael "Woody" Woodward Jul 12, 2017
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Organizational psychologist, CEC certified coach and media personality Michael “Dr. Woody” Woodward, Ph.D., recently interviewed Sheri Feinzig, co-author of The Power of People (Pearson FT Press, 2017).

Who is this book for and why did you write it?

Sheri Feinzig: We had two main audiences in mind: HR leaders and business executives. For both audiences, we wanted a book that was practical and easy to understand. For HR professionals, one of our goals was to help them realize that an analytical approach to HR is very much within their reach, and that by embracing analytics they will be able to bring exponentially more value to their organizations. For business executives, we wanted them to see what’s possible for their HR function, particularly for those leaders who want more from HR than they’re getting today. As one of our early reviewers, Tom Davenport, so aptly put it: “Trusting your gut on people issues turns out to be a bad idea.” Our ultimate goal is to help HR leaders have more impact on their organizations and bring more benefits to their people by adopting an analytical approach to HR.

Why does our gut often fail us as HR practitioners and hiring managers?

SF: We all tend to see things through a certain lens, whether we realize it or not. Sometimes our gut is reasonably okay and sometimes it leads us astray. Why take the chance, particularly with decisions that potentially have consequences for an entire organization? I think the tendency to rely on instinct is prevalent in HR, because people-related issues have a common sense element to them. But people-related decisions in business should be handled just as systematically as any other decision – with data to support it.

I’ll also say that I believe there is a place for gut instinct, and that is in forming hypotheses that can be tested with data. In the book we talk about an 8-step methodology for workforce analytics, and the first steps include framing the business question that needs to be addressed, and building hypotheses to test potential causes of business issues. Hypotheses can be informed by several sources (such as research literature and previous experiences). Gut instinct can be another source of input, but let’s be sure we test those ideas and assumptions with data.

How do you change the mindset of a non-analytic and even data-fearing HR departments to one that embraces analytics?     

SF: That’s a great question. In fact, we’ve included an entire chapter in the book on this very topic. What we’ve found in interviewing workforce analytics experts is that most HR departments are staffed with people who fall into one of three categories when it comes to analytics: the analytically savvy, the analytically willing, and the analytically resistant. Our guidance is to understand where the individuals within your organization fall along these categories, and customize your enablement accordingly.

The analytically savvy are those who are already comfortable with numbers and statistics, and tend to view HR through a business lens. They may have joined HR from other functions, or perhaps were formally trained in analytics as part of their education.

The analytically willing are open-minded about analytics and prepared to learn, but lack formal training and experience and therefore may be somewhat hesitant as well. For this group we recommend starting with the basics, such as clarifying what analytics is and what it isn’t, and then working with them using data and tools that are already familiar to them.

The analytically resistant are the most challenging. These are people who simply do not embrace analytics and do not see the need to bring analytics to HR. I think there’s an opportunity with this group: if you can turn the staunchest resistors into believers, you will have some really powerful champions to leverage. And the path to getting there is to understand where the resistance is coming from so you know how best to address it, and work with them on a specific problem they are facing to help make them successful through analytics.

In the era of big data and automation how do your keep HR human?  

SF: Keeping the “human” in HR is a fundamental principle for successful workforce analytics. We strongly believe in always keeping in mind the potential impact your analytics will have on the very people whose data you are analyzing: the workers. Analytics is based on statistical models and probability, and like any modeling it will never be 100% accurate for all cases. Analytics should be used to inform decisions, but not to replace the decision makers. And you should have solid grounding in an ethical approach to data usage: just because you ‘can’ analyze certain data does not necessarily mean you ‘should’ analyze and act on it.

When it comes to implementing analytics in HR, where do you start and what's the best way to get a quick win?

SF: When you’re getting started it’s really important to demonstrate success early on, so you can establish credibility and build support going forward. You are most likely to be successful with a project that is not overly complex, and that will deliver results relatively quickly. As a first project, try to avoid taking on something with too many dependencies and uncertainties. At the same time, you want to select a project with enough impact that it will be noticed beyond the HR function. One thing to keep in mind an easily manageable project for one team might be very challenging for another team with fewer resources or less experience. So choose a project that your team can execute relatively quickly and that will make a noticeable difference for your organization. 


Michael "Dr. Woody" Woodward, currently serves on the board and as a faculty member at the Florida International University Center for Leadership in Miami where he works with high potential talent and C-suite executives. He is also on faculty with the Institute for Management Studies where he travels the country training budding new managers from major companies including BOSE, McDonald’s, Verizon, Liberty Mutual, and NASA.

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