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Moneyball for HR: Applying Data Science To Managing People

Moneyball for HR: Applying Data Science To Managing People

Much like the Oakland Athletics baseball team featured in the book and movie Moneyball, businesses face a complicated task in evaluating talent and deciding if a person is the right fit for the team.

Certainly, there are tried and true measurements used by human resources managers. A personality that fits with others in the organization, for example. Or, a resume that shows success in past endeavors. Even, a college degree that shows a willingness to persevere and see something through to its completion.

But much like the old baseball scouts in the Michael Lewis Moneyball book, HR professionals are discovering that those tried-and-true methods might not produce the desired results. Especially when it comes to hiring from without or promoting from within.

Instead, data analytics is increasingly giving them insight into how to manage employees. For those earning a degree in the data analytics field, it has opened up a whole new area of opportunity.

Medieval Thinking, says Moneyball

Much like the famous “medieval thinking” speech from Moneyball, human resource directors have decided a change is needed in how they evaluate and manage personnel.

That feeling is widespread. The 2017 Global Human Capital Trends report, conducted by analytics firm Bersin by Deloitte, found that 90 percent of HR managers feel they need “to re-engineer their entire organizational model. That involves rethinking leadership, management, careers and jobs,” according to company founder Josh Bersin.

Known as “people analytics,” the movement in HR to incorporate analytics into a company’s personnel management seeks to refine how jobs are filled. It also seeks to replace older methods such as intuition or networking with measurable metrics.

Writing about the topic, Washington Post columnist Steven Pearlstein used Baltimore entrepreneur Michael Rosenbaum as an example. In choosing software programmers for his company, Rosenbaum paid no attention to whether his students had graduated from big-name schools.

The reason? Statistics showed no correlation between job performance and where a person got a degree.

The Need For Change

In that same column, Pearlstein referred to surveys from the Corporate Executive Board and Gallup. They found, respectively, that a quarter of new hires leave within a year. And furthermore, that of those who stay, 50 percent report not being engaged with their job.

Some of the new, data-driven ways of thinking have shown that conventional wisdom hasn’t been all that wise. This was true in both hiring practices and in promotional decisions. Using quantitative metrics – the number of sales closed by a salesperson, for example, or the number of projects successfully completed – data analysts’ discovered the following:

  • Time on the job does not necessarily translate into success in the future
  • Statistics do not support the notion that it’s a bad idea to hire a convicted felon, someone who changes jobs frequently or someone who is unemployed
  • Other factors prove more useful in predicting a person’s performance, such as how far they have to drive to work

According to the Society For Human Resource Management (SHRM), HR managers and business executives are often not asking the right questions.

For example, SHRM suggests questions such as “what part of a training program led to business success” and “what words appear the most on the resumes of successful employees” as examples of inquiries that, though analyzing large amounts of data, can give a company a better idea of future performance.

What HR Needs

For those interested in applying their data analytics degree to human resources, it’s helpful to know more about what companies want. SHRM listed the following qualities HR leaders would want if they could create their own data analysts. They included the following:

Statistics and storytelling. Data analysts need to know the finely grained details of extracting useful information from large data sets. But, they also need the skills to communicate that information to executives.

Business sense. Data analysts need a strong foundation in how a company operates and the factors that led to making a profit. Or, as the case may be, not making a profit.

Collaboration. Extracting and interpreting data is one thing, but analysts also must have the people skills to collaborate with others.

HR knowledge. Data analysts will never know how to ask the right questions if they don’t have a sound knowledge of the drivers behind attracting and retaining employees. They also need a general knowledge of performance management.

Opportunities await skilled data analysts who have received a quality education and can commit to working in the relatively new area of “people analytics.” Clearly they have a role to play in a new approach to managing human resources.

As Lewis wrote in Moneyball, people “operate with beliefs and biases. To the extent that you can eliminate both and replace them with data, you gain a clear advantage.”

2017-08-08T14:10:04+00:00
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