Big Data, which has been used in marketing and e-commerce for almost a decade now, can lend its power to organizations for recruitment purposes. It can offer answers to problems including candidate pipeline quality, best talent sources and employee retention. Relying only on experience and gut feeling is not only slow but can be wasteful. Listening to the story data tells is a more scientific and reliable way. No company would invest 40% of its revenue without sound financial analysis. Why take the risk with HR?
HR: from Reactive to Proactive
The traditional approach to hiring was a belated, reactive one. As soon as additional workforce was necessary, the heavy recruitment machine was set in motion. Newspaper advertisements, hard-copy resumes, interviews, and orientation, all time and resource consuming steps with an unclear success rate. This is no longer an effective way to get the best talent unless you want a setting straight out of the 1950s. Now, competitive organizations use internal data to anticipate the need for talent long before it arrives and external data to source the best candidates. The role of HR is no longer administrative, but strategic.
The Candidate Funnel
Getting the best talent is like fishing in an ocean. You need to cast your line in the right spot and put the right type of bait on your hook. Stocking the candidate funnel resembles stocking the sales funnel. Therefore, big data consultants can just adapt existing algorithms.
Big Data can highlight market trends and identify areas where talent will be needed in the next 12-18 months to trigger growth. This forecasting method removes the urgency from recruiting, protecting the organization from rushed decisions. Anticipation gives the hiring manager time to prepare a healthy pipeline of candidates and a clear profile for the expected employee.
Mathematical methods can also provide an accurate estimation of the number of candidates that need to be reached initially to ensure filling all the positions with qualified personnel. These computations take into consideration the large percentage of applicants that don’t meet the criteria.
Locating Talent with Big Data
Modifying big data algorithms used in e-commerce can help recruiters answer questions like: “Which schools/companies produce the best candidates?” and “What is the best medium to find and engage those candidates?”. By combining data already existing in the organization with external sources, organizations can create talent maps. Such a representation is also valuable when opening a new branch or when the company is thinking about business process outsourcing.
From the applicant’s point of view, some companies seem more attractive than others. Organizations can use big data analysis to measure the brand perception and determine why qualified candidates choose a competitor. Sometimes it is a branding problem.
Filtering Talent with Big Data
Once an organization has topped the funnel with enough candidates, it is time to start the selection process. Data has previously been used in individual studies to determine the factors that would predict a lucrative working relationship. The advantage of using big data, especially the unstructured type, is that the results can shatter long-held beliefs and impose new ways of selecting people. An organization can look at the way the applicant writes their resume and perform sentiment analysis on the candidate’s public social media profiles. Mastering grammar and not being part of radical groups are good predictors of a desirable employee. This analysis also reveals the best days and hours to post a hiring ad online for maximum impact.
Building a Success Profile
The set of promising candidates will go further down the funnel for a head to head comparison with a predefined “ideal candidate.” Before, this step was the duty of an experienced hiring manager, but now it is just a suitable distance function. The winner will be the one who is closest overall to the desired profile. This “ideal” is, in fact, a highly distilled set of traits of high performers.
One of the major issues organization face as the Boomer Generation is retiring is a leadership crisis and adjusting management models for Millennials and Gen Z. By studying the motivational factors of these generations and the causes they care about, companies can select leaders with a convergent vision that can appeal to the employees’ set of values.
Big Data after Employment
Another parallel with Big Data for retail is appropriate after a candidate is selected. As it motivates a returning customer, the company must find ways to get the employee to continue the relationship. Each person hired must absorb and replicate the values of the organization.
Performance Measurement and Improvement
Companies can aggregate readily available data in models that predict future performance. Think of records in any application like past jobs, academic performance, candidate background and even current performance. Even details like mood analysis or social media updates can be useful in showing an organization red flags about employees or signaling top performers, even before it happens.
With the help of big data consultants, your organization can create sophisticated evaluation scorecards. These combine professional outcomes and personal interactions within the company to evaluate the employee holistically. Such an approach would penalize the high performers with a bad attitude and give an incentive to the helpers who underperform because they want to help others too.
Talent acquisition and training are expensive, and a high turnover rate can be costlier than launching a bad product. Companies are not only after getting the best people but keeping them. Big data analysis can improve retention rates by creating an actual motivational model.
A New “V” for Big Data
Until now, Big Data consisted of 3 Vs: volume, velocity, and variety. In relation to growing the HR department of an organization, a new V has emerged and has the added value of using this kind of information to answer problems proactively. To access this 4th V, a company needs to harness the power of Big Data for talent acquisition. Adapting existing algorithms is the first step, followed by creating dedicated AI tools for HR.