Hint: It’s all about the data.
Data is changing how every industry works. It is changing how big companies operate – it’s affecting their processes and the tools their employees use.
Many talent acquisition teams are using data to better source, screen, and hire candidates. Gone are the days when recruiters could find top talent by waiting for job seekers to send in their resumes. With the unemployment rate at a 50-year low and with all the available data posted by candidates themselves, companies can (and should) actively search for potential new hires on social networking sites, such as LinkedIn. But even reviewing these so-called ‘relevant’ search results can be time consuming.
Today, companies are investing more in technology to speed up their time to fill. Many recruitment software solutions are compiling candidate-posted data and claim to be using Artificial Intelligence to provide recruiters with the best candidates. However, if the available data is incomplete or vague, the results of this AI will be misconstrued.
The Director of the Center for Enhanced Analytics at the US Government Accountability Office, Vijay D'Souza, said, “Regardless of the goals, it’s important to understand the quality of the data you have. The quality determines how much you can rely on the data to make good decisions.” In other words, when it comes to data, having an enormous database means nothing if the data is low quality. A company can have the biggest database in the world, but their Artificial Intelligence will still struggle to produce quality insights if the data is inaccurate, not valid, unreliable, irrelevant, incomplete, or inaccessible.
When put into a recruiting context, the number of candidate profiles in an agency’s database will not be relevant to an employer’s search if the profiles are lacking crucial information. Most candidate databases today have incomplete candidate product profile information, incomplete candidate roles and skills, and an inability to identify every possible title per position across companies. This incomplete and inaccurate data can lead to an incomplete pool of candidates and can also lead to misinterpretation by the ‘Artificial Intelligence’ applied to this data.
At headhuntr.io, our data is first enriched with additional data sources to give us the visibility into the Candidate Intelligence that enables a successful searching process. The data is enriched with department information, product expertise, roles & skills, company information, domain expertise, and more. We then apply data science to categorize and tag the data, and then build predictive components on top of that. Finally, we utilize human insight to leverage our business insights and ensure that our tags are accurate and will not be misinterpreted by our predictive components. The final result of our processes is a Candidate Intelligence database that allows us to find candidates who are the best of the best.
Looking to incorporate data into your hiring processes? Get in touch by sending us an email at email@example.com.