Data-driven hiring is a process of using data and analytics to inform and guide the hiring process. This can include using data to identify the most qualified candidates, assessing candidate fit with the company culture, and even predicting future job performance.

Data-driven hiring often involves the use of quantitative metrics and tools such as applicant tracking systems, pre-hire assessments, and predictive analytics. The goal of data-driven hiring is to make more objective and informed decisions with the ultimate goal of identifying the best candidates for the job.

In the long run, this results in better hires, greater employee retention, and more. Here’s how the data-driven hiring process works, and the ways it can benefit your business.

The Data-Driven Hiring Process

What does a data-driven hiring process look like and how is data used at each step in the process? The process may look different for your company, but the following essential elements generally define the process:

1. Define the job requirements: What does the job require and what skills are needed for the position? This data can come from past successful hires, job analysis, and industry standards.
2. Collect and analyze data: This step involves collecting data on candidates, such as resumes, application forms, and pre-hire assessments. The data is then analyzed to identify patterns and trends that can help identify the most qualified candidates. For example, analyzing resumes can provide valuable insight.
3. Screen and shortlist candidates: Using the data collected and analyzed, the next step is to screen and shortlist candidates for further consideration.
4. Interview and assess candidates: The next step is to conduct interviews and assessments to gather more information about the candidates and to determine their fit with the company culture. Data is collected through interviews and assessments.
5. Make a hiring decision: Once all of the data has been collected and analyzed, a hiring decision is made.
6. Track and monitor: The last step is to track and monitor the process, evaluating the effectiveness of the data-driven hiring process and making any necessary adjustments.

What Types of Data are Typically Used?

The data used in a data-driven process falls into a couple of categories, primarily designed to protect candidate privacy and prevent bias. When Amazon implemented machine learning for hiring, the data input actually resulted in continued bias. As a result, companies have altered the kind of data such programs can learn from.

Demographic data:

This includes information such as a candidate's name, address, education, and work history. Note that gender, race, religious affiliation, and other similar data are not included in this dataset.

Behavioral data:

Information such as a candidate's work style, problem-solving abilities, and communication skills make up behavioral data. This information can be collected through interviews, pre-hire assessments, and other means and it can be used to assess a candidate's fit with the company culture and to predict future job performance.

Skill-based data:

A candidate's qualifications, skills, and experience are considered skills-based data. It is usually collected through resumes, application forms, and pre-hire assessments, but can also include degrees or certifications earned, and increasingly digital credentials. The key is a common taxonomy related to skills, and this can be achieved using Rich Skills Descriptors.

Performance data:

This includes information such as a candidate's past job performance, as well as data on the performance of current employees. This information can be used to predict future job success, but can also be used to make better hiring decisions.

Diversity data:

This includes information such as a candidate's gender, race, ethnicity, and other information. Diversity data can be used to identify and eliminate unconscious biases in the hiring process and to improve diversity and inclusion in the workforce. This information must be carefully handled so bias is not unintentionally introduced.

Network data:

This includes information about a candidate's professional network and connections, which can be used to identify potential candidates through referrals and to assess the candidate's reputation with their peers.

All of this data informs the hiring process, but how does that benefit your business?

How Can a Data-Driven Hiring Process Benefit Your Business?

A data-driven hiring process can benefit a business in several ways:

1. Increased accuracy in identifying the best candidates: This can lead to better hires in the first place and improved job performance over time.
2. Improved diversity and inclusion: By identifying and eliminating unconscious biases in the hiring process, more objective hiring decisions can be achieved.
3. Reduced time-to-hire: Data helps managers identify the most qualified candidates quickly, helping a business reduce the time it takes to fill open positions.
4. Reduced hiring costs: By using data to identify the most qualified candidates quickly, a data-driven hiring process can help a business avoid costly recruiting and hiring mistakes.
5. Improved candidate experience: Candidates who are not a good fit for the job can be identified early on in the process, which can help reduce frustration and disappointment for both the candidate and the business. Qualified candidates benefit from a streamlined and efficient process.

Implementing a Data-Driven Hiring Process

Implementing a data-driven hiring process can be a complex and multi-step process, but it doesn’t have to be. Here’s how you can get started.

1. Identify the data sources you’ll use: Determine what types of data will be used in the hiring process and where that data will come from.
2. Set up data tracking and analysis tools: This step involves setting up the tools and systems that will be used to collect, track, and analyze data.
3. Train hiring managers and recruiters on how to use the data: It is important that hiring managers and recruiters understand how to use the data and tools and how to interpret the results.
4. Continuously monitor and improve the process: Once in place, the data-driven hiring process should be continuously monitored and improved over time. This includes tracking the outcomes of the process, such as the quality of hires, time-to-hire, and costs, as well as making any necessary adjustments to the process.

The future of hiring is data-driven, and likely includes things like machine learning and artificial intelligence. Those will never replace the human element of gathering and assessing data and facilitating the hiring process. But they can make it faster, more efficient, and reduce bias. And that’s a win for everyone.