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Human resources data analytics brings metrics to workforce management

Human resources data analytics can help companies get a handle on their workforce and add fact-based reasoning to hiring and firing decisions.

For years, companies have analyzed data of all types – customer, product, financial – with one important exception: human resources data.

In fact, for decades, human resources departments have largely shunned analytics altogether, instead focusing on automating previously manual personnel processes, said Zach Thomas, an analyst with Cambridge, Mass.-based Forrester Research. The result?

"At the end of the day, executives have had a lever on things like marketing spend and product development spend," Thomas said, "but they've never really had a lever on people."

That needs to change, he said -- and fast. Especially during a recession, human resources departments must manage their workers like strategic assets, and one way to do that, he said, is with human resources-based data analytics.

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Among its other benefits, according to Thomas, data analytics can help human resources departments model their workforces and ensure they have the right skills and competencies to meet the organization's business objectives.

Data analytics can also aid in making hiring and succession planning decisions, as older workers retire and new workers enter the workforce, and can help manage employees' careers by identifying their best next steps, like a new certification or transfer to a different department.

Analytics are also important when it comes to layoffs – an unfortunate reality of any recession -- helping to separate top performers from average ones, Thomas said. When layoffs are necessary, companies need to be sure they are cutting the fat, not the muscle, of the workforce, he added.

Making human resources data analytics a reality

The major barrier hindering companies from implementing human resources analytics is not a lack of data on employees – salary information, performance reviews, education level – but siloed employee data, Thomas said.

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Many human resources departments have bypassed IT and use SaaS-based applications to manage different types of employee data, which are difficult to connect to internal data sources like payroll systems. The result, he said, is a mishmash of disparate employee data that is nearly impossible to aggregate for analysis.

Calculating the ROI of HR analytics is also difficult precisely because many HR systems and applications are not integrated with financial systems, Thomas said. A lack of historical data against which to compare HR analytic results also makes measuring ROI tricky, he said, but a change of even a percentage point or two in employee retention or turnover can have a significant financial effect.

Thomas also recommends HR analytics for companies of all sizes, with the exception of very small businesses – those with around 250 or fewer employees. "The cost of hiring someone doesn't change based on the size of your organization," he said, "and the cost of people not performing doesn't change either."

In a recent report on the topic, Thomas outlined a five-step approach for organizations to effectively analyze human resources data to better manage their workforce:

Step 1. First and foremost, companies need to do a thorough accounting of all their employee and human resources data, identify where it resides, and plan for integration. There are numerous data integration tools on the market to aid companies in this effort, and the emergence of Web services has made the job even simpler.

Step 2. Once employee data is integrated and discrepancies reconciled, companies must determine what metrics to measure. This involves a three-step process, Thomas said, "starting with backward-looking metrics; moving toward predictive, forward-looking analytics; and finally resulting in KPIs based on historical data." These metrics should be "linked directly to business outcomes and not laser-focused on cost cutting," Thomas said. "That's what your process-based metrics are for."

Step 3. Next, companies have to choose one of three technology strategies. The first and least effective approach, according to Thomas, is to use widely available but limited tools from the major ERP vendors such as SAP and Oracle. The second and most expensive option is to create a custom human resources data warehouse and leverage business intelligence applications on top. Or, third, companies can tap a best-of-breed talent management vendor -- Taleo Corp., Workscape, SilkRoad Technology Inc. -- for a suite of human resources analytics tools, which Thomas said is probably the best approach for most companies.

Step 4. With analytics in place, the next step is to determine in which format to present the results to ensure maximum effectiveness. "A spreadsheet's not going to work," Thomas said. "You need different delivery models, like graphical reports and interactive tools, so people can actually understand and manipulate and act on the data you're giving them."

Step 5. Finally, he said, companies have to be ready and willing to change and refine their human resources data analytics approach to meet changing demands. "Just six months ago, you could have been evaluating recruiting processes, but given the recent economic challenges, your focus has most likely shifted toward succession and development," Thomas wrote in his report. Data analytics must take these changing goals into account.

Ultimately, effective workforce management through human resources data analytics requires a change in mindset, Thomas said.

"The conversation changes," he said, "from saying, 'I need to invest a bunch of money to automate a process,' to 'I need to invest a lot of money in order to ensure we have the right person in the right job at the right time.' "

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