How Recruiting Operations Teams Can Keep Hiring Data Clean

How Recruiting Operations Teams Can Keep Hiring Data Clean

The first four steps of this data cleaning checklist concern your approach to data as a hiring team. The next five deal with your data directly. 1. Teach Data Management Basics: Whoever is using your ATS needs some training on the basics. They should understand what a literal creature your ATS is (e.g., it doesn’t know that ‘LinkedIn’ and ‘LI’ are the same thing).

2. Attach Candidates to Jobs: Your ATS has to be more than just a database for data cleaning to work. Including complete information for every candidate provides a full pipeline picture of a job. So you can assess each job’s pipeline and address any issues in your hiring stages. 3. Incentivize Data Cleaning: To understand why a recruiting effort didn’t yield a hire, you need data on that recruiting effort. Therefore, all of your hiring team members have to update each candidate’s status in real time, even dropouts.

4. Use Thoughtful Reasoning Finding meaning in data is finickier than it seems at first. You have to look at the right metric. For example, there’s no point in using percentage of qualified candidates to unqualified candidates because number of qualified candidates is the metric that matters. 5. Collect End-To-End Data on Every Job: End- to-end data is crucial to data cleaning because it’s the only way to get a full view of every hiring effort. It means staying on top of data cleaning best practices like updating candidate status in real time. It also means getting rid of evergreen jobs.

6. Use Enough Data To Be Significant: It takes a certain amount of data for analytics to be meaningful. Rather than comparing individual jobs or single- digit numbers against each other, use comparison groups of at least 10 or more jobs. 7. Separate Your Data Into Thoughtful Buckets: Every situation poses its own unique challenge to the hiring effort. By segmenting your data into thoughtful buckets such as location, seniority level, and job type, you can see what’s happening on the ground in each situation.

8. Remove Outlying Pieces of Data: Some jobs behave differently than others because of their unique nature. For example, evergreen jobs, internal hires, internships, and new-grad hires. It’s important to remove these regular outliers from your talent pipeline data so they don’t skew your analytics. 9. Use Median Calculations Over Mean Calculations: Applicant pool data can contain lots of outliers. Given that a single outlier can muddy the picture of your talent pipeline, it’s important to use median calculations instead of mean calculations.

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