When applied to the hiring process, data analytics can help you strategically grow and manage your team with greater accuracy and success. More companies are using big data to create a stronger company culture.
50% of business owners consider big data to be the most effective hiring method, a Global Recruiting Trends survey reveals. Moreover, big data can also improve talent retention by 56% and better clarify skills gaps by 50%. Indeed, a business is only as successful as its people and, by adopting data analytics, you can quickly find the most suitable job candidates, speed up the recruitment process, enhance the outsourcing process, and avoid the expensive and unwanted problem of employee turnover.
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Finding the best fit for an open position
Although widely used, keyword scanning software alone simply doesn’t generate sufficient success metrics when sifting through candidate resumes. Predictive analytics, in contrast, goes further than keyword scanning software by highlighting a plethora of valuable metrics like experience, job titles, qualifications, skills, industries, and businesses, and compares these to the open job description and even existing employee data. It then ranks a candidate’s suitability and for the job and likelihood of success in the role, in turn making the hiring process quicker and more efficient and increasing fill rates. By datafying this early stage in the recruitment process, you’ll also no longer need to rely on gut feeling, which can sometimes end up being a mistake if not balanced with logic and strategic decision making. Even though you may hit it off with a candidate in an interview and be impressed with their resume, that doesn’t necessarily mean they’re the right fit for your business. Fortunately, recruitment software and tools allow for data-driven decision-making that eliminates human bias and uncertainties, ultimately helping you make better decisions during the hiring process with greater accuracy and peace of mind.
Speed up the recruitment process
Top talent is typically hired by recruiters within ten days. If your recruitment process takes longer than this average, data science can help you speed it up while providing better results. To do this, you can begin by collecting data on your entire hiring process, including the average time taken to hire an employee, specific systems used (job boards or agencies), and average length of the vetting process and individual steps involved. With this data, you can then determine whether certain steps take longer than necessary and if these steps can either be automated or cut out completely. For instance, you may come to the understanding that the candidates who tend to reach the final interview stage are typically underqualified or too inexperienced for the role. So, in this situation, you may devise and implement an online test designed to assess candidates on their basic skills and knowledge of their field of work. Candidates who simply aren’t up to scratch can therefore be disqualified early on.
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Enhancing outsourcing
Outsourcing tasks to a third party facilitates business growth. In the US, 300,000 jobs are outsourced every year with saving money being the main motive for nearly 60% of businesses. By freeing up time, outsourcing allows in-house employees to take care of the work they do best, thereby enhancing efficiency and productivity. Moreover, implementing big data into your outsourcing model allows for a more precise analysis of important data generated throughout the entire project. You can therefore quickly identify any obstacles or problems and ensure a more flexible, effective, and productive outsourcing arrangement. With the accurate data and insights, you can make strategic decisions to ensure success. Ideally, you want project management software that features SLAs, benchmarks, metrics, along with real-time, easy-to-read reports that don’t take too much time out of your day to digest and respond to.
Retaining staff
It costs an average of 6-9 months’ salary each time a business replaces an employee. So, if you make $60,000 annually, for example, recruiting and onboarding fees cost $30,000-$45,000. Keeping staff happy increases company loyalty, keeps them with you for longer, and therefore helps avoid costly new hires. Here’s where data analytics can help. By monitoring worker efficiency and productivity, data analytics provides you with a clear picture of each individual employee’s interests, skills, strengths, and expertise. In turn, you’re in a better position to offer your employees more engaging and challenging roles just right for them. Data analytics essentially helps you get the most out of your staff while increasing job satisfaction and retention rates at the same time.
Big data has the potential to greatly improve the hiring process for our business. By using data science to find the best job candidates, speed up the recruitment process, and improve the outsourcing process and staff retention rates, you can better grow and manage your team and ensure business success.