Recruiting in the Digital Age: Putting Big Data to Work for You
By Ann Carlsen, Founder and CEO, Carlsen Resources Inc.

Across all fields and functions, big data has become the new frontier for innovation, greatly increasing competition and productivity in every industry. In the age of social media and big data, there is a staggering amount of information on each candidate available to us as recruiters. Every day, we create 2.5 quintillion bytes of data – so much that 90% of the data in the world today has been created in the last two years alone, according to IBM. The problem facing recruiters now is, how to effectively and efficiently put that data to work. Though it is not, by any means, a replacement for all the other tools in the talent acquisition process, by embracing and harnessing the business intelligence resources available, recruiters are able to make evidence-based decisions and hire better candidates, faster. This approach is quickly becoming a game changer in the field, allowing companies who embrace people analytics to become more efficient and successful in the long run. In fact, Bersin by Deloitte’s “WhatWorks” brief suggests that talent acquisition teams with data analytics strategies in place are twice as likely to improve their recruiting efforts, and two times more likely to improve their leadership pipelines.

Though there are billions of data bytes and hundreds of software analytics programs available to recruiters today, creating, storing, and accessing this information is just the first step, and there are few shortcuts. The groundwork for finding the right skillsets, integrating the data, and building the tools is a continual process that must be invested in. Of course, once you have the right data, you have to actually do something with it. Success depends as much on a dedicated culture of and commitment to measurement as it does having the right tools in place.

Once you’ve established the right culture, process, and tools, data can help support talent acquisition teams through nearly every phrase of the process – starting with forecasting. If a recruiter knew before a position was open how easy or difficult it might be to fill, how would that change hiring practices? What if they knew whether or not other companies were currently competing for individuals with the same skill set in their area, and how their proposed salary lined up with that of the competitors? There are many technologies that pull real-time market data into view so you can get a full picture of your present and future recruiting landscape. For example, some software services can tell you how many job openings there are in the current market for roles similar to yours and competing for the same talent pool. This kind of data, largely captured from job boards, can help answer these questions and give recruiters the answers they need to transform their hiring practices from reactive to proactive.
Furthermore, this kind of data analytics can be a great way to collect competitive intelligence for the business. Knowing when a rival is on the hunt for the same talent pool not only lets you know when your own employees are more susceptible to poaching, it can also let you know when your competitors are expanding, whether it be opening new offices, or when a new hiring spree looks like it’s related to the introduction of a new product line or business division.

The use of data analytics can also be extremely beneficial during the sourcing phase of the recruitment process. Job boards are an excellent way to get your posting out to potential candidates, but as we know, they add up and can be quite expensive. Other than tracking the number of responses to the posting, there is often no way to track how effectively the money is being spent in terms of delivering the highest achievers or reaching your desired targets. Leveraging big data, companies can now tell you not only how many qualified candidates you’ve reached, but how many interviews take place, and how many employees are actually hired based off a posting on a specific job board. This allows you to track the real ROI of postings so you know where your resources are best invested on future postings. This new functionality not only allows recruiters to target sources with the greatest likelihood of increasing candidate flow, it also builds stronger pipelines and saves company resources, as you won't be wasting time and money on boards that don’t perform for you.

Data analytics can also be employed to help your HR teams develop better job descriptions to attract a higher volume of candidates. Big data can uncover trends and patterns based on “click decisions” which can tell you why or why not a potential candidate decides to take the critical next step of applying for your open role. You can then use this data to adjust your description accordingly and attract more candidates. More important than attracting a high number of candidates, of course, is attracting the right kind of candidates. With the right data you can find out which skills, values, traits, and behaviors lead to a hire who is likely to be successful in your organization, and is likely to stay with the company for the long term. Whether this means gathering insights on the best performance drivers in the past and then concentrating your search efforts on a more specific type of skillset, this use of data means recruiters only spend time talking to the most relevant candidates, allowing hiring managers to extend their reach and increase their chance of finding the best talent.

Once you’ve found the right crop of candidates, data analytics can help take the bias out of the interviewing process. While most hiring managers like to spend time getting to know candidates through unstructured interviews, oftentimes that is not the best predictor of success. Companies more advanced in the use of data, like Google, use their talent analytics departments to analyze the data and find out which interview questions are most highly correlated with a candidate’s future success on-the-job. Once they’ve identified which questions are more in-line with future performance, they can weigh candidate responses to those questions accordingly. In the same vein, as some companies shift toward a more structured data-influenced interview process, they are simultaneously moving away from the interview panel. This allows companies to have multiple data points from multiple interviews, which of course, can be more valuable than one data point from one group interview. This can remove some of the noise from the interview process, and allows for a more telling comparative analysis of the candidate. While these more data-influenced comparative evaluations do not remove all the bias from the process, they certainly help level the playing field. 

After you’ve hired the right candidate for a role, data analytics can also be used to inform training programs and talent development strategies. If the market data shows that specific skills are in extremely high demand within a particular talent pool, recruiters and HR representatives can advise management when it might be best to invest in training resources instead of seeking out new hires.

The use of big data doesn’t end when a candidate is in place and succeeding in a role, it can also be used to inform retention strategies. Research shows that high turnover leads to low performance; so all companies seek to retain valued employees and top performers. If your company is unable to recruit and retain the best talent, figuring out WHY is crucial. Exit interviews can be a simple, and incredibly effective way of gathering the data you need to solve retention issues. The problem is, many companies conduct these interviews but few collect and analyze the data, and even fewer use that data to inform their actions. A study on exit interviews conducted by the Harvard Business Review found that while most companies do gather the right data, less than a third of those organizations shared the data with senior decision makers. Those companies that did analyze and share the information were able to uncover valuable insights regarding systemic problems, were able to adjust the company’s practices accordingly. 

Big data is meant to open doors, not to replace the skills and experience your team has developed over the years. The purpose of using data analytics is to strengthen your recruiting and retention practices and add nuance to them. So, even if you’re not an advanced analytics company like Google, who lets their algorithms do some of their recruiting for them, there are many ways to use data analytics to find the right talent to execute on the company’s vision, all the while providing valuable insight back to the business.


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