The way hiring teams manage the data in an applicant tracking system (ATS) can have a big impact on the story the data tells and the strategy companies implement from it.
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.
Contact Us At:
https://datapeople.io/
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.
Contact Us At:
https://datapeople.io/