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Ask your favorite HR person and they’ll most likely tell you that far and away their biggest challenge is recruiting talent, especially for technical roles. Their dilemma: organizations today have deep investments in software, but face a shortfall of staff with the technical skills to take advantage of them. In fact, a recent LinkedIn article predicted a gap of over 4 million tech workers by 2030.
The year 2030 may seem far off to most of us, but to organizations that need to implement technologies like AI, IoT, blockchain and the cloud, it is approaching fast. These technologies require employees with extensive experience and technical know-how. Organizations need to start hiring now in order to build their future bench of leadership.
When the book Moneyball was published in 2003 it was for many a revelation of the value of applying data analytics to sports. In it, author Michael Lewis detailed how the Oakland Athletics management was able to use analytics to identify undervalued players and build a winning team on a budget smaller than the competition. Moneyball described a rivalry of “scout” versus “scorer”. Old school scouts relied on their gut and observational skills to judge a player, while a new breed of number-crunching “scorers” relied on metrics and algorithms. It turns out, for baseball as well as other professional sports, analytics beats instinct. Today, analysts for Liverpool’s soccer club don’t even need to watch a match – the stats tell the whole story.
Baseball and other sports analytics often focus on player performance, but in the case of Moneyball, analytics was used to solve a talent recruitment problem.
That’s very relevant for today’s HR departments. Modern analytics platforms can help human resources solve many of their most critical challenges, starting with recruitment.
For example, analytics can help pinpoint best recruitment methods or identify applicants who are the best fit for an organization. An analytics dashboard can be used to track recruitment campaigns and their outcomes, answering questions like “Does advertising for positions on LinkedIn deliver applicants who are more likely to be hired than college career fairs?” or “What departments are more successful at moving interns into full time positions?”
Another challenge for HR is to meet organizational diversity goals and equal opportunity requirements. Today’s organizations are under increasing pressure to meet diversity goals, not only to protect their brand’s reputation, but also to increase profitability and expand into new markets. HR analytics can help by identifying opportunities to proactively meet diversity goals, and by tracking the success of diversity programs, answering questions like “What departments retain female hires after one year?” and “Where do minority employees move to within the company?” and “How do we assure that our hiring decisions are made without unintentional bias?”
Once an employee is hired, of course, HR analytics can continue to deliver insights. Performance appraisals, for example, are an opportunity for HR to use data from years of reviews to understand what actual skills translate into a valuable employee. An analytics dashboard can answer questions like “How many more mid-level project managers do we need to hire next year?” or “How many database administrators do we currently employ with the skills to transition to cloud engineer roles?” You can’t always quantify the value of an employee. How do you put a number on a senior employee who mentors newer employees? There may not yet be a way to collect data points on these – but most likely there will be, soon.
For HR departments already tasked with a long list of responsibilities, collecting data and building out analytics dashboards may seem a daunting tasks. But modern data visualization tools can help them become better informed about their workforce. Data analytics can help them make decisions faster, so they dedicate less time to crunching numbers, and more time to the people side of their jobs.
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