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A business team working in an office. (iStockphoto)
A business team working in an office. (iStockphoto)

Forward Thinking

Mining data to find and keep the right people Add to ...

Human resource planning is an ongoing challenge at Stantec Inc., an Edmonton-based engineering, architecture and interior design professional services consulting firm with close to 13,000 employees in 200 offices globally.

But Stantec’s HR team has a handy tool at its disposal, one that the company is using more and more to improve the way it hires, engages and develops employees: predictive analytics. By combing through employee data and sometimes pulling information from the Web and other external sources, Stantec says it is able to gain insight into the issues that affect human resources and create better HR strategies.

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“For example, if we do a turnover analysis, we can do trending in a certain location and see why people are leaving in this economy in this region,” explains Alan Gee, Stantec’s director of organizational development and learning. “Based on this information, we can determine what we need to do to prevent this from happening in the future.”

Stantec is among the growing number of companies in Canada and around the world that are applying predictive data analytics in human resources – essentially using statistical models to identify trends and develop short- and long-term strategies for hiring, retaining and developing talent.

A 2011 IBM survey of 400 North American companies found 33 per cent had a work-force analytics solution in place. In another survey, by Vancouver-based Visier Corp., a provider of cloud-based analytics applications, about 60 per cent of 410 respondents said their company had a work force analytics system in place.

“Analytics is really the Swiss Army tool for the work force,” says Mychelle Mollot, vice-president of business analytics at IBM Canada in Ottawa. “It enables organizations to ask questions concerning every part of the HR function, and to turn to data to find answers to these questions, like ‘Do we have enough resources for this week, or for the next five years?’”

While much of the technology behind work force analytics has been around for a while, the growing interest in using worker-based statistics to create predictive models is more recent, says Dave Weisbeck, chief strategy officer at Visier.

One reason for this growing interest is the emergence of big data – that massive agglomeration of information that has built up in the past couple of years and includes everything from social media posts to instant messages, purchase transactions, and digital videos and photos. At the same time, says Mr. Weisbeck, today’s companies are sitting on mountains of human resource data from their HR systems, which typically include data for recruiting, payroll, benefits, and performance development and management.

This wealth of information, combined with companies’ ongoing need to stay competitive, is driving the imperative for predictive analytics in HR, Mr. Weisbeck says.

Sean Fitzpatrick, president of Ottawa-based TalentMap, a consulting firm that specializes in employee engagement, says HR departments today are expected to be more strategic than they were in the past. Many companies have outsourced traditional HR functions such as payroll and now want to see HR prove its value to the business.

“They’re expecting data,” he says. “It’s no longer good enough for HR to come to the table and say, ‘This is what we’re doing to fill these roles, and this is how we’ve improved the onboarding process.’ Executives want to see statistics and data that support their company’s hiring strategy, and that show how the onboarding process influences employee engagement down the road, which we know has a significant impact on the business.”

The scope and applications of predictive work force analytics can be fairly simple or incredibly complex. For example, it can look for patterns in resignations and terminations and find that rates are highest among workers of a certain age and among those who have been with the company for a certain number of years.

In some cases, employers might also scrape data from the Web to predict if workers might be getting ready to jump ship; the more activity on professional networking sites such as LinkedIn, the higher the likelihood that an employee is looking for work elsewhere.

Armed with this information, an employer can then take steps that might prevent high-risk employee groups from losing interest in the company and seeking employment elsewhere.

For companies planning to launch or expand operations, predictive analytics can help pinpoint the best locations to set up shop, says Scott Ahlstrand, senior vice-president of talent management at Milwaukee-based Right Management, a talent and career management consulting firm.

“We can forecast which particular roles and operations are best to have in which particular geographies,” he says. “We run algorithms based on things like work force population trends, what calibre of skilled workers the educational or trade system produces, and what the career path is for individuals in particular roles in particular geographies.

“What’s great about work force analytics is that it allows you to plan way ahead into the future – not just for the next six months or the next year, but for the next five, 10 or 15 years.”

Just how effective are work force analytics? In the IBM survey, respondents were asked how well their organizations handled five key human resource challenges, including retaining valued employees and determining strategies for reducing the work force, redeployment and retraining. Those who worked for companies that use work force analytics said their employers were significantly more effective than those that do not have work force analytics.

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