Does Big Data Matter in Sourcing? #BigDataHR

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This week on Blogging4Jobs, we are focusing on the theme Big Data sponsored by Jibe. Jibe provides cloud-based recruiting technology solutions that enable talent acquisition teams to strategically identify, attract and engage candidates. Join us April 10th 2014 at 3pm to talk Big Data on Twitter using the hashtag #BigDataHR and join our webinar, “What’s the Big Deal with Big Data in HR & Recruiting” on April 17th at 11a EST. Follow the week by bookmarking us!

With many enterprises using “Big Data” to more efficiently source, screen and select talent, the prevailing feeling within the recruiting space is that if you aren’t talking about “Big Data” and how you and/or your teams can be using it, you are behind the curve… or are you?

As more small to medium size enterprises begin to embrace technology either out of want or necessity, my clients and their peers in the Talent Acquisition space are worried, excited, stressed and everything in-between. Senior leaders want to establish very specific metrics for sourcing success, but many times the source data is either not there or suspect at best.

My hospitality clients use a variety of tools to help track their recruiting processes. They range from truly excellent applicant tracking systems to talent assessments that have applicant tracking components and, yes, even spreadsheets.  These methods provide raw data to help inform sourcing and staffing strategies… but these sources of data are just one piece of the puzzle.

Understanding What Data Matters

One client said to me “how do we say a hire from Job Board A is better than Job Board B just because past applicants from A termed faster?” She was right.

While the data tells us that Source A is more likely to have an applicant stay within the organization longer than Source B, does that mean you stop using Source B?

What about from a strategy side, how do you sell your hiring managers on a source when it drives a large number hires BUT also a very large number of unqualified applicants?

That’s when data comes in very handy. By being able to use pre-defined success metrics (be it quantity of hires, hires per advertisement, hires per location or even cost per hire) the non-believers are often converted into believers.  Sometimes though, those non-believers are right and you have to be able to move quickly onto plan B.

Is your Data good?

One key question I ask my client is “do you trust your data” and, unfortunately, more often than I’d like, the answer isn’t a resounding yes.

When you track hires on a spreadsheet or rely on measuring source effectiveness based on applicant selected sourcing criteria, you are far less likely to be able to make definitive, reliable statements as to effectiveness than if you are working with an Applicant Tracking System that you know does a great job tracking sourcing.

One of my favorite exercises with a client is a deep dive into applicant data. One such end of year analysis yielded data suggesting a strong tilt of a certain job board towards minority candidates. This information allows the client to tailor their messaging on this site towards the target demographic while also maintaining overall branding integrity.

Another client presented the request that, like most, we needed to do more with less. They are growing but were not able to increase overall spend so we needed to aim, more strategically, at the highest quality and quantity sources. We were able to take their applicant tracking system data (which we trusted) and reallocate spending to media which performed better over the trailing 12 months while still coming in under budget.

This was a great example of using “big data” to properly inform decision making. We analyzed hires, applicant traffic and spend to best allocate scarce resources. In addition, we’re able to monitor spend and traffic sources in real time to adjust as necessary. Instead of utilizing a gut feel on applicant quality / quantity we used trustworthy data to more efficiently allocate resources.

However, not every client is lucky enough to be able to easily track and quantify traffic. In instances where you are left without reliable data, you still have to use something as a basis for the decision making process.

Defining (and revising) Success Metrics

The key to most every proposal or RFP I work on is around defined success metrics, be it from a trial where we hope to hit a certain cost per hire to a renewal based on total hires, data-based metrics inform most every decision our clients sign off on.

Many clients like to analyze a metric that measures success per ad (hires or applicant) while some measure advertising effectiveness by looking at the days required to get a hire.  Is one wrong and another right? No; it just depends on where your organization does, and wants to, place an emphasis. Cost Controls and turnover minimization are important poles on the tent that defines success.

The most important success metric, in my mind, is hires. Without them, there is no success and getting to the end game has to be both affordable and efficient. Each situation surrounding the hire is different and requires a targeted approach to get the ultimate goal be it staffing an extremely skilled cook, a server or a Regional Vice President for a multi-billion dollar company.

Ultimately, success metrics are based on many things both internal and external to your organization. You can aim at appeasing Hiring Managers by driving as much traffic as possible or you can aim at ensuring the Finance team is happy by driving the most cost effective traffic, irrespective of quantity.

While I do believe that data based decision-making is both important and impactful, the bigger questions that someone must ask when it comes to making specific data a key component that informs their recruiting strategy are “why should we”, “how do we” and, most importantly, “do we trust our data”.

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