Big Data for HR Doesn’t Have to Be Rocket Science - From eQuest’s Floating Point Blog
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Big Data for HR Doesn’t Have to Be Rocket Science - From eQuest’s Floating Point Blog

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    Big Data for HR Doesn’t Have to Be Rocket Science - From eQuest’s Floating Point Blog Big Data for HR Doesn’t Have to Be Rocket Science - From eQuest’s Floating Point Blog Document Transcript

    • Big Data for HR Doesn’t have to be Rocket Scienceby DAVID BERNSTEIN on OCTOBER 19, 2012Last week I wrote about growing HR into something big, and how Big Data can play a key role in helpingyou become a more valuable partner to your organization by engaging in data-driven decision making.Over the past few weeks, I’ve been talking with both customers and industry analysts. Two themes haveemerged from those conversations. The first is the reminder that any analytics endeavor will be pointlessunless there are knowledgeable people interpreting the data, knowing how to make the essentialconnections between what the analysis is revealing and how it relates to a real business challenge. Thesecond theme is that there are many HR organizations that are already feeling overwhelmed by theaccelerating needs of their business to have the HR function join the leadership team in making datasupported decisions and recommendations. The perceived complexity of employing a Big Data strategy tosupport their analytic efforts only heightens the anxiety level – and in some cases, brings HR to astandstill on the idea.In the spirit of trying to reduce the anxiety, I’d like to offer up a real case study of how eQuest supporteda customer in jump starting their Big Data analytic efforts. In previous posts, I’ve discussed theimportance of starting small, identifying critical areas where HR can provide immediate value—the low-hanging fruit. This is exactly how eQuest works with our customers. First, we start by working withthem to identify their key talent acquisition objectives. Armed with this information, we analyze our BigData repositoryand come back to the customer with a detailed roadmap for their recruitment marketingefforts.For this particular example, our customer is a large Financial Institution based on the East Coast. Afterreviewing their talent acquisition objectives, what became clear is that they did not have a goodunderstanding of how their recruitment marketing spend was performing. Over the years, the number ofjob boards they were utilizing ballooned to 48. They were spending roughly $175,000 per year forthose. All in an effort to hire an average of 350 people per year in just that one part of their business.Our first key objective was to work with the customer to fully utilize our job posting deliveryservice. Not only did this provide the customer with the productivity advantage this service provides, butit also enabled us to be able to analyze their candidate activity across those 48 boards. Astonishingly, we
    • found that 45 of their sites showed no response within a reasonable time frame. The analysis revealedthat only 3 of their current boards were producing any reasonable candidate response rates. With our BigData analysis capability, we were then able to identify 4 other boards that they should use andrecommended they drop the 45 that were not performing. Last, based on our analysis of the words andphrases candidates were searching on, we provided guidance on how to improve their job posting titlesand descriptions.In the end, we helped our customer boost their candidate traffic by 175%. Not only that, the customerreported that the quality of those candidates increased as evidenced by the increase in the number ofcandidates that were brought in for interviews. Last, we were able to negotiate preferable postingcontracts for the customer for their remaining 7 boards, reducing their annual spend by 50% for thatportion of their recruitment advertising budget.As this example shows, Big Data does not have to be rocket science, nor should it be. It’s simply aboutapplying key fundamentals, as you would with any business strategy, to arrive at data-driven decisionsyou can back up.In my example, the organization leveraged data to budget smarter, forecast smarter, spend smarter, andmeasure smarter. These are not new concepts, just fundamentals applied to sourcing candidates, whichrepresents one of the most critical components of the talent acquisition cycle. Not coincidentally, it isalso one of those “low-hanging” areas through which HR can quickly impact the business.People are the core to any business. It’s HR’s responsibility to find the best talent for theorganization. Doing this well, creates competitive advantage. Blend that with being able to accelerate thepace of the candidate flow into the talent pipeline and you have competitive advantage on steroids.The moral of the story is simple. Don’t let Big Data scare you. See it as an opportunity to proactivelysupport your business in a key area, such as candidate sourcing. By keeping it simple, you will deriveboth short-term results and long-term value.