In HR Function, Analytics Need to Delve Beneath Attrition Data

by   |   October 25, 2012 4:19 pm   |   0 Comments

Mary Ann Downey 150x150 In HR Function, Analytics Need to Delve Beneath Attrition Data

Mary Ann Downey

When it comes to data analytics, attrition is one of the most common metrics used by human resources professionals today. By calculating the percentage of employees who have left a company over a particular period of time, whether voluntarily or involuntarily, HR leaders believe they can better gauge the overall health of a workforce, as well as the impact employees are having on a company’s bottom line.

But keeping tabs on who’s walking in and out the door isn’t always an accurate reflection of a company’s talent management, no matter how sophisticated the data analytics tool. Mary Ann Downey is all too familiar with the potential and challenges of using attrition as a metric. Co-founder of HR Metrics Coach, an Atlanta-based HR consultancy, Downey discusses with Data Informed the most common mistakes HR leaders make when examining a workforce’s comings and goings, and how to avoid the same pitfalls.

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Data Informed: Why is it a mistake for companies to measure attrition only by the number of people who leave the organization in a given period of time?

Mary Ann Downey: When someone leaves your organization, it doesn’t really tell you what the problem was. Companies need to be asking, ‘Did we hire the wrong person? Did we select the wrong person? Did we onboard them incorrectly? What in the process broke down?’ Part of measuring quality of attrition is using data analytics to diagnosis where we have inefficiencies in our hiring practice.

The other tragedy of measuring just how many people left the organization is that it doesn’t tell us if they’re our most valuable employees. For HR professionals, this is really hard to hear but the reality is sometimes it’s OK if we are constantly churning through certain positions, especially if they come with low training costs and there’s a plentiful supply of these workers. On the other hand, when it comes to valuable positions, if those people are leaving, that’s something that should really get a manager’s attention.

DI: How can a company determine who are its most valuable workers?  

Downey: There are a lot of different models you can use but you should choose the model that works best for your business. For example, some companies may concentrate on revenue-generating roles while others might want to focus on customer-facing roles, or roles that create a competitive advantage for your organization. An HR leader needs to separate these out and look at the attrition rates differently for each employee segment rather than overall. That’s where you can gain some real insight and help business leaders improve efficiencies and meet their business goals.

DI: What sources of data should HR rely on to analyze attrition rates?

Downey: That’s one of the greatest challenges that I’ve found. Our HRMS (human resource management systems) tools are designed to look at transactions and they’re very value-neutral about roles. For this reason, an HR professional needs to sift through the data, analyze different roles and make some value judgements about what data is important and what can be discarded.

DI: What is the most common mistake HR leaders mistake once they’ve identified critical roles and data sources?

Downey: Sometimes we fall in love with our processes and how we’re collecting information and we forget that if we don’t do anything with this information, it doesn’t really matter in the end. Sometimes we need to do a gut check—a so-what test. When I’m meeting with a new client, the first thing I ask them is what are their hypotheses about the future? Which workers are most important and what are the things that concern them regarding attrition? Then we can go through the data analytics process to try to either prove or disprove these hypotheses. By proving or disproving them, we can gain new and actionable insights that can actually help you make better HR decisions.

Cindy Waxer is a Toronto-based freelance journalist and a contributor to publications including The Economist and MIT Technology Review. She can be reached at cwaxer@sympatico.ca  or via Twitter @Cwaxer.





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