Big Data: The Oxford English Dictionary (OED) defines big data as “data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.” However, it is reasonable to add another dimension to this definition of big data: analysis. The science of recruiting depends on big data. Not only is it a complex collection of information; if properly organized and evaluated, it is also a treasure trove of relevant intelligence that can inform decision making and optimize business practices.
In other words: big data can have a big impact.
RECRUITERS – WHY BIG DATA?
Recruiting is a science. Do you know what marketing source is most productive for your agency? Are you aware of where your top candidates are located? How about what skills and experiences form the best managerial staff? The answers to these questions are not a matter of estimation. They are the result of analyzing high volumes of data to uncover correlations, behavior patterns, and other useful knowledge.
IF YOU AREN’T ALREADY LEVERAGING BIG DATA, YOU SHOULD BE.
Insights garnered from your collected information can create a strong competitive advantage. The foresight allotted by big data shifts recruiting from ‘reactive’ to ‘proactive’ practices, allowing you to advertise more effectively, recruit quality candidates, and increase business revenues.
Hiring proactively provides the necessary time to efficiently place the best candidate, rather than being forced to pay a premium for talent during urgent hiring periods. Big data solutions also allow for creation (and execution) of highly strategic recruitment plans, empowering agencies to outperform competitors.
So, now we know big data can reduce placement time, cost per hire, and improve the recruiting process. It can also better align job orders with real market averages by assembling information on candidate expectations and performance metrics. But, how can recruiters harness big data and take advantage of the benefits that it has to offer?
HOW RECRUITERS CAN HARNESS BIG DATA:
Let us revisit our definition of Big Data: data of a very large size, to the extent that its manipulation and management can present significant logistical challenges (OED), but that if properly organized and evaluated can inform decision making and optimize business practices.
Does this remind you of anything?
Applicant Tracking System (ATS): A software application designed to help businesses recruit more efficiently by searching and organizing large quantities of client and candidate information (or data).
Consider this: How many years have you been collecting information in your ATS? How many candidate data points, such as language, salary expectations, years of experience and more, have come through your agency? The answer is probably big.
ATS or CRM systems create a centralized place for recruitment teams to enter and manage important information. When data is clean it can be properly harnessed and interpreted by eliminating the likelihood for incorrect or incomplete records. For as long as recruitment agencies have been in business, they have been amassing big data. This data has enormous potential for enhancing productivity.
A good ATS will keep data organized by providing a simple user interface, with the ability to catch duplicate entries and to store documents and templates in one well-constructed system. It will also allow users to send and track messages, provide access to robust back office features, and have powerful integration tools for job boards and social media.
MAKING THE MOST OF ATS BIG DATA
By tracking detailed information and understanding correlations between the areas where candidates were successful or where they faced shortcomings, recruiters can remain perceptive to the best and worst candidate data matched to particular job orders.
Strong ATS systems provide users with the ability store extremely high-quantities of detailed candidate data. By creating thorough applicant files – tracking communication, documents, skills, and more – a recruiter can establish a comprehensive profile long before a candidate reaches a client for an interview. Organized access to this information eliminates the guess work in staffing.
For example, a recruiter can use an ATS to efficiently source candidates who have a given salary range within a certain commuting time and distance from a position. This will let them know how effective someone will be in a particular role, as we know that a more difficult commute on a less than enticing salary results in higher absenteeism and shorter job commitment.
Top-of-the-line ATS systems also provide advanced search features that can capitalize on big data insights. In many cases, management software becomes a source for big data when it conveniently integrates with outside systems. The ability to source using uniquely defined data sets within job boards like Indeed, and social media sites such as LinkedIn, can provide recruiters with quick access to what they are looking for.
A recruiter may know that a position needs to filled with an applicant who has a very particular set of qualifications. These may include high level Java proficiency, leadership skills, the ability to speak French, and a University degree in Computer Science. While job board and social media integration may help locate eligible candidates, finding an individual that fits all of these requirements is not an easy task. That’s why some Applicant Tracking Software, such as Mindscope, go even further by also organizing results based on the best match for the search criteria, offering a leading method for sourcing top talent.
THE BIG DEAL WITH BIG DATA
The ability to track great sums of information in an exceptionally organized manner allows it to be understood, interpreted and utilized. This is what adds the second dimension to our definition of big data.
Not only is it big data information on a very large scale – it is information that can empower those who take advantage of it to make highly informed decisions that enhance their business practices.
In the words of Daniel Keys Moran, “You can have data without information, but you cannot have information without data.”