Keyword Search HCX for your Favorite Author / Content

HireCentrix - ViewPoint

Big Data for HR Doesn’t Have to be Rocket Science

AddThis Social Bookmark Button

Last week I wrote about growing HR into something big, and how Big Data can play a key role in helping you 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 have emerged from those conversations. The first is the reminder that any analytics endeavor will be pointless unless there are knowledgeable people interpreting the data, knowing how to make the essential connections between what the analysis is revealing and how it relates to a real business challenge. The second theme is that there are many HR organizations that are already feeling overwhelmed by the accelerating needs of their business to have the HR function join the leadership team in making data supported decisions and recommendations. The perceived complexity of employing a Big Data strategy to support their analytic efforts only heightens the anxiety level – and in some cases, brings HR to a standstill 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 supported a customer in jump starting their Big Data analytic efforts. In previous posts, I’ve discussed the importance 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 with them to identify their key talent acquisition objectives. Armed with this information, we analyze our Big Data repository and come back to the customer with a detailed roadmap for their recruitment marketing efforts.

For this particular example, our customer is a large Financial Institution based on the East Coast. After reviewing their talent acquisition objectives, what became clear is that they did not have a good understanding of how their recruitment marketing spend was performing. Over the years, the number of job boards they were utilizing ballooned to 48. They were spending roughly $175,000 per year for those. 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 delivery service. Not only did this provide the customer with the productivity advantage this service provides, but it 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 revealed that only 3 of their current boards were producing any reasonable candidate response rates. With our Big Data analysis capability, we were then able to identify 4 other boards that they should use and recommended they drop the 45 that were not performing. Last, based on our analysis of the words and phrases candidates were searching on, we provided guidance on how to improve their job posting titles and descriptions.

In the end, we helped our customer boost their candidate traffic by 175%. Not only that, the customer reported that the quality of those candidates increased as evidenced by the increase in the number of candidates that were brought in for interviews. Last, we were able to negotiate preferable posting contracts for the customer for their remaining 7 boards, reducing their annual spend by 50% for that portion 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 about applying key fundamentals, as you would with any business strategy, to arrive at data-driven decisions you can back up.

In my example, the organization leveraged data to budget smarter, forecast smarter, spend smarter, and measure smarter. These are not new concepts, just fundamentals applied to sourcing candidates, which represents one of the most critical components of the talent acquisition cycle. Not coincidentally, it is also 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 the organization. Doing this well, creates competitive advantage. Blend that with being able to accelerate the pace 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 proactively support your business in a key area, such as candidate sourcing. By keeping it simple, you will derive both short-term results and long-term value.

 

 

-----------------------------------------

BIOGRAPHY

David Bernstein is the head of eQuest’s new Big Data for HR/Predictive Analytics Division, which enables organizations in making better-informed decisions about their recruiting and hiring strategy. He is responsible for developing consulting services and custom business solutions that leverage eQuest's Big Data. David’s focus is on yielding critical insights for HR departments to drive talent acquisition and workforce planning strategies. He writes the blog, “Floating Point” and recently presented a webinar on how data analytics can predict future behavioral patterns of candidates and create a competitive advantage in candidate sourcing. Recently, David has been a featured speaker for SHRM, HRO, and HRIQ.  In years past, David has also presented at the International Association for Human Resource Management (IHRIM), International Quality & Productivity Center (IQPC) and PeopleSoft conferences and company-wide events. David earned a Bachelor’s degree in Psychology from the University of California at Berkeley.

Blog: www.equest.com/news/floating-point
LinkedIn Profile: www.linkedin.com/in/davidsethbernstein
LinkedIn Group: http://linkd.in/VMZzqm
Twitter: www.twitter.com/dbernste

##

 

 

Addthis
blog comments powered by Disqus

HCX Facts

Growth in women's share of science, technology, engineering and mathematics (STEM) occupations declined to 27% in 2011from a high of 34% in 1990. While women make up nearly half of the workforce, they were 26% of the STEM workforce in 2011.

Who's Online

We have 198 guests and no members online

Advertisements