19 September 2016
Time and time again, studies show that data-driven decisions are more effective than following human intuition. Debate around data-driven decision-making is finally turning a corner: a recent study shows that even U.S. manufacturing -- one of our most traditional industries -- increased their use of data in the decision-making process from 11% to 30% during the past five years. 

Good, reliable decision-making is critical to the hiring process. There's just too much at stake! Making the wrong hire is a costly mistake that hurts both the organization's bottom line and employee morale.

One way to avoid bad hires is to choose applicants based on their ability to do the job and their track record of success. You'd think that was a no-brainer, yet between "trusting your gut" and hiring sorority sisters or friends of friends, many companies squander opportunities to add truly valuable team members to their roster. 

It's too bad, because Gallup analysis suggests that when companies select the top 20% most objectively talented candidates, they see incredible results: a 10% increase in productivity, 20% increase in sales, 30% increase in profitability, 10% decrease in turnover, and a 25% decrease in unscheduled absences. 
Making the wrong hire is a costly mistake that hurts both the organization's bottom line and employee morale.
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The most common comparison I hear is a baseball metaphor. Back in the early 1990s, the Oakland A's decided to stop relying on recruiters' gut feelings and instead focused on a range of player metrics -- on-base percentage, batting average, RBIs, etc. -- to build a team. Since they couldn't afford to hire a whole squad of superstars, they chose players whose capabilities complemented one another. The end result was a championship.

Great comparison, right? Just like an effective team at any company, the A's built a group that leveraged a variety of strengths based not on personal bias or instinct, but on good data and thoughtful analysis.

Not so fast, though. A recent article in the Guardian points out a fundamental flaw in the system. Let's say the A's pass on a player whose stats don't look promising and that player goes on to be a huge star. The A's can then go back to their model, see what they missed, and improve.
Platforms hosted on the cloud will finally enable companies to share data on hiring outcomes.
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A company, on the other hand, will likely never learn what happens to an applicant they don't accept. HR departments don't communicate with their competitors, so even if they use data to guide their hiring process, the data they're using is critically incomplete.

Enter the cloud. As data-driven decision-making and hiring technologies continue to evolve, the cloud has a major role to play in addressing this pitfall. Platforms hosted on the cloud -- rather than on user-owned closed systems -- will finally enable companies to share data on hiring outcomes. 

This is especially exciting for small organizations who are otherwise excluded from data-driven decision-making due to their tiny sample sizes, ensuring that all applicants, no matter where they seek employment, will be judged and assessed fairly and accurately. When companies can make decisions with a view of the whole field, we'll really start to see the full impact of data-driven hiring. 
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