Big data is growing at an alarming rate. For most companies, however, less than a percent of the data collected is actually analyzed and used. A golden moment for HR and talent acquisition, companies are reinventing how they leverage big data to effectively recruit and retain top talent.
Big data analytics is the process of examining large datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
While most prevalent now, the history of big data might surprise you. First known as “information explosion”, the inception of big data stems back to 1941. Then, it was applied to library systems, but current estimations prove data collection “has increased eightfold over the past 5 years.”
Chew on these big data usage statistics:
- Data is growing faster than ever before and by the year 2020, about 7 megabytes of new information will be created every second for every human being on the planet.
- For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income.
- 73% of organizations have already invested or plan to invest in big data by 2016
- And one of my favorite facts: At the moment, less than 0.5% of all data is ever analyzed and used, just imagine the potential here.
Big data is making a big impact across a number of industries. Big data is rattling the world of talent acquisition to its very core. As this Mashable article reveals, it is the golden moment for HR and recruiters.
Deciphering Big Data
To better understand how companies can exercise their big data muscles, we called on resident Senior Operations Analyst, Sarajane Alverson. As a contributing member of Yoh RPO, Sarajane works with clients on how to collect, verify, and analyze raw data collected (mainly from Applicant Tracking Systems).
We asked her a few questions on how companies can easily begin to incorporate big data into their recruiting process, and moreover, how to put insights gained into action.
For companies who have yet to adopt big data, give us an example of how it’s commonly used today?
Data and analytics are used to look for patterns of behavior. In a human-driven business such as recruiting, behavior is a big deal. What are your applicants doing? How are they finding you (or you them)? What kind of recruiting and hiring experience are they having? On our side of things, how many open positions do we have? How many days does it take to fill a position, start to finish? Those are just some of the questions data analytics can answer.
With so many opportunities to look at the data readily available, knowing where to start can be incredibly frustrating. While big data is by definition large and overwhelming, it doesn’t have to be completely daunting. Most Applicant Tracking Systems (ATS’s) are designed to help you look at your data and statistics. It’s a matter of using the tools available to you. “Work smarter, not harder,” as they say.
Data and analytics are used to look for patterns of behavior. In a human-driven business such as recruiting, behavior is a big deal.
For example, I recently worked on a project where the recruiting team would actively work on approximately one hundred job requisitions (open positions) at any given time. Each recruiter or staffing specialist was responsible for providing the operations manager with a weekly status update. However, this task was achieved manually. Recruiters would enter their notes into Excel, send it off to the operations manager, who would then compile the information to analyze, and round and round the Excel sheet went every week, growing larger in both size and the probability for human error.
The first step we took to eliminate extra work was to export a real-time report directly from the ATS. Granted, it took some time and training before the data was clean enough to filter and interpret, but in the end, it proved to save the recruiters an average of three hours per week that they could then apply towards active recruiting.
What do you mean by clean data?
Any database or tracking system is only as good as the information you put into it. “Clean data” is both accurate and available in real time. We have to be able to trust the information we’re seeing; we then have to be consistent with what we’re tracking in order to maintain the data’s integrity over time.
When the data is clean, it tends to tell a clear story. As data analysis become more of a cultural norm, it not only helps to hold everyone accountable, but moreover improves recruiting metrics which leads to better organizational decisions.
Do you have any tips or best practices on how to analyze raw recruiting data?
Before you can interpret data, there needs to be an agreement on what metrics to focus on. Similar to a reverse engineering process, you start with the end goal and work your way backwards through the steps.
Let’s say for example, time to fill is a key recruiting metric that you want to monitor. First, you’ll want to examine how this data is collected, examine whether it is clean, and confirm that all of the recruiters are entering it on a consistent basis. From there, you can begin to parse through the raw data. If you find it takes one hundred calls to acquire two quality candidates, that’s a red flag.
Lastly, I would say don’t be surprised if in solving one issue, you discover three others. Analysis and process efficiency can often feel like a game of “Whack-a-Mole,” but as long as you’re whacking different moles each time, with an eye on your larger goal, you are, in fact, making progress.
 No actual moles were harmed during this interview.
About the Interviewee: Sarajane is a Senior Operations Analyst in Yoh’s St. Louis office. She has a Master’s degree in English literature and a growing collection of action figures on her desk. When she’s not saving the world one spreadsheet at a time, she is also a professional actor in the St. Louis metropolitan area.