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The Basics of Data Normalization

What is data normalization, and why are we dedicating an entire blog post to it?

We recently processed 62 million candidate data points and found the proof we’ve been waiting for. Recruiting has a data problem; an unstructured, messy, and ineffective data problem.

Here’s what we found:

When you break down those percentages, out of 62 million candidate records:

  • Over 31 million records didn’t have a resume
  • About 22 million records were missing any recorded education data
  • Over 15 million records were missing any recorded work experience
  • And over 3 million records had absolutely no contact information

After a decade of experience in the HR technology space, we knew that data was messy, but this just confirmed how truly inadequate those databases often are. But they’re not insufficient because they don’t function. They’re ineffective because the data is out-of-date, messy, and unstructured.

And without accurate data, you can’t effectively implement the technology you need to be successful.

What Is Data Normalization?

Data normalization is the process of structuring a database to reduce data redundancy and improve data integrity and usability.

The data that lives in a candidate database, like an ATS or CRM, comes from any number of sources: past applicants, outside sourcing efforts, existing employees, manual entry, etc. There’s often no standardized process for adding that data, resulting in human and machine error and messy, unorganized data.

For example, let’s say your online application process allows applicants to fill out their state names in an open text box (meaning it’s not a drop-down or limited to initials). Some people spell out their state (Montana, California), and some people abbreviate it (MT, CA), while some misspell it (Montna, Cailfornia).

This leads to you missing out on effectively building a search by state location because no matter what you search, you will be missing out on data that wasn’t structured correctly.

Why Is Data Normalization Important?

If your team has clean, normalized, and structured data, they can make hiring suggestions and decisions that are backed by quality data. In short, the quality of your output will match the quality of your input. Beyond that, there are a few key places where data normalization will make a significant difference.

Technology Implementation and Usage

According to an survey, 40% of participants stated that their ATS lacks features. It’s natural to want to implement tools that can support your organization’s needs. But let’s be blunt: Tools like artificial intelligence, machine learning, and automation cannot work correctly without good data quality. You’re wasting money and time if you apply tech and don’t take care of your data first. 

As our founder, Shon Burton, said in our recent webinar, Data Health and Talent Acquisition:

GIF of Shon Burton saying, "Data enables or denies the capability of automation and artificial intelligence."

Unfortunately, teams often look at an AI tool and see it as ineffective or unsuitable for them because they made the mistake of laying it on top of messy data and expecting the tool to fix everything. AI won’t fix your bad data, but data normalization can.

Talent Intelligence and Insights

Without structure, your data can’t deliver the talent intelligence and insights of which it’s capable. As a reminder, talent intelligence is key workforce data that can be collated and contextualized to give organizations practical and actionable steps.

Your systems have that data already within them; the problem arises when you need to contextualize that data to make it usable. Data is cleaned up through the normalization process and deduplicated, allowing experts to find gaps and goldmines within the data.

When you can contextualize those insights, your database becomes the foundation for your success. If you know that you have a treasure trove of recently graduated college students in your database, that’s great. Your entry-level positions are ready to fill! But do you have that same level of a treasure trove for people with 20+ years of experience? Talent intelligence and data normalization show those gaps so that your recruiting team can take the reins to create change.

Compliance and Regulation

The new SEC regulation requires knowledge of your internal database. And it’s highly likely that the future will require even more insight into your database. As we see more and more people become invested in where their data is, what is collected, and how it is used, data holders will need to become more transparent about that information.

After all, how many times have you reached out to a candidate only for their first question to be, “How did you hear about me?”

If regulation and compliance needs come knocking at your door, data normalization will allow you to understand what data you have and how it can be used.


All of these reasons for why data normalization comes down to one central theme: Efficiency. Organized systems will always be more efficient. Whether that system deals with data like an ATS or not, quality organization will lead to efficiency.

When we’re finally working towards a post-COVID world and facing down “The Great Resignation” and an incredible number of open positions, your team needs efficiency. What efficiency you need will differ depending on your team’s goals, but whether you are looking to fill roles faster, understand the insights of your existing data, or even be able to search more accurately, data normalization will be able to help.

But this is just the start…

The basics of data normalization tend to apply across the board. But how long the process takes and what the result looks like varies depending on the needs of each individual team.

Here at HiringSolved, we do Data Quality Reports to see what state your data is in before we begin normalization. We start off by meeting with team leaders to identify which systems need to be reviewed and get access to a read-only pipeline of your data. From there, our team will organize, process, and visualize the data across 50+ unique metrics and then review our findings with you.

If you want to learn more about the role of clean and structured data, here are some further resources:
Webinar: Data Health and Talent Acquisition
What Is Data Normalization and Why Is It Important?
What Is Talent Intelligence?