The concept of recruiting and hiring has always come with a certain degree of risk attached to it. All too often, hiring managers are drawn in by an impressive resume, fancy dress clothes, an upbeat and confident demeanor, and a handful of positive interactions.
While the initial meetings and interviews may set high expectations, companies never really know what they are getting with a new hire until months later. A single bad hiring decision can do a lot of damage to a company’s budget and reputation. From a financial standpoint, companies lose an average of $14,900 on every bad hire, according to CareerBuilder.
Fortunately, the rapid advancement and sophistication of big data have made its presence known in the hiring process. The results of this can do a lot to cut down on turnover. However, understanding how to make these algorithms work for your specific organization will require a good deal of time and commitment. Here is how to do it.
Know the data sources
For companies that are new to the whole concept of big data, one of the toughest parts can simply be knowing where to look for the most pertinent information.
In terms of hiring, these algorithms normally work within a relatively narrow scope of information. Many HR departments utilize three major source categories of data. These include:
- Publically available information
- Background information (provided by the applicant)
- Interaction data
Publically available data can come from a wide range of sources. These can include social media, demographic information of the area, pay scale, employment rate, and much, much more.
The background information is what hiring managers see on a resume, or any other credentials submitted by the applicant. These typically relate to skills, qualifications, and experience.
Interaction data refers to the small insights gleaned from how an applicant communicates with a company. These insights come from things like keystrokes, word choice, and answers to questions. The metrics can have a strong correlation with future job performance.
Once you have identified the data sources necessary for the hiring process, your data mining tool will be able to run analyses to find the context you need to make more informed choices.
Understand key variables for each position
Upon finding the ideal sources, one of the biggest data-related challenges companies face is knowing exactly which metrics pertain to their goals, and how to apply them. According to IBM, about 2.5 quintillion bytes of data are created every single day. That being said, locating the right information can seem like finding a needle in a haystack.
Depending on the position you are recruiting for, there will likely be a wide range of data variables that play into the equation. This is one of the areas where there tends to be a high margin of error. Keep in mind, algorithms can only work for you if you have all the information necessary.
Therefore, you need to have a crystal clear objective in mind for the exact variables that pertain to the job, as well as how you can leverage them to eliminate the guesswork. These may include college GPA, certain buzzwords from previous jobs, soft skill proficiency, certain personality traits, etc.
Fortunately, there are plenty of tools to help you with this part of the process. AI-driven “smart” recruiting tools like Harver are designed to automatically screen applications and background information to identify the ones with the strongest correlation to the open position. From here, it runs a number of specialized assessments to gauge the applicant’s interaction data related to problem-solving, communication skills, situation judgment, and more.
Once the candidate has completed the assessments, the system uses smart algorithms to determine the strength of each candidate and how well they fit the mold for not just the open position, but the company as a whole.
Even though big data can work wonders in making smarter hiring decisions, it’s important to remember that there will always be a good amount of human intuition and iteration involved as well. Big data algorithms are simply there to guide you.
Use each interaction as a predictive data point
Big data, in general, can best be described as a constant work in progress. Datasets are continuously building off of each other to become smarter and more precise.
As you begin to develop a bank of data relating to your hiring process, there will almost certainly be a number of patterns that will emerge. These patterns should serve as a reference to how people mesh with your company. For example, in terms of communication, the datasets might show that the best workers in your company were the ones who responded to messages from the hiring managers within one hour. Or, perhaps the ones who sent shorter and more concise emails had a better success rate in the company.
BI tools like Dundas make the concept of predictive analysis simple. The browser-based solution allows you to input any data source and view the trends in customizable, interactive reports.
From here, you can draw on previous datasets to justify decisions for the future. The goal of hiring managers is to stay one step of head of common issues like poor productivity, employee turnover, bad cultural fits, and more. If you use every single interaction as a predictive data point and keep a close eye out for trends, you are in a much better position to avoid mistakes and misjudgments down the road.
Over to you
Turning your company into a data center has many benefits. In regards to the hiring process, managers need to do everything they can to make smarter decisions and avoid the dreaded high turnover rate. In the age of constant-connectedness, a high turnover rate isn’t just bad for your budget; it’s a huge red flag for new talented candidates.
While there are very few guarantees in the business world, one of the safest bets is that big data is here to stay. The sooner you can get the algorithms working for you, the better you will be in the long run. Always remember, a business is only as good as the people it brings on board.