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Enforcing ATS Data Hygiene with AI: A Guide for Staffing Agencies

In this article

Paul Beglinger
Head of People & Operations, Carv
Close to a decade of experience crafting success stories, from startup to global presence.

For staffing agencies, the Applicant Tracking System (ATS) is supposed to be the holy grail – a central hub for all candidate information, the key to efficient recruiting.

But the reality is often far less glamorous. Many agencies struggle with a dirty secret: inaccurate and incomplete data lurking within their ATS.

A messy ATS is a productivity killer, a resource drain, and a major roadblock to building a growth engine and scaling an agency. It's the elephant in the room that many firms try to ignore, but its impact is far-reaching and undeniable.

To tackle this pervasive issue, we need to understand its roots.

Why do talent acquisition teams end up with such messy data environments? And more importantly, how can they clean up this digital clutter and ensure data integrity going forward?

Let's dive deeper into this topic.

Why your ATS data is a mess

The pressure to meet quotas and the high turnover rate of recruiters often create a perfect storm for outdated and inconsistent information.

As a result, the ATS - golden source of information, meant to be the lifeblood of the entire team, is frequently neglected. Updating the system is a data entry task dreaded by many, and it often ends up rushed, delayed, or skipped altogether.

Sure, if your team is tight-knit, meeting daily to look at recruitment dashboards, share insights and discuss hiring decisions, especially when your recruiters are doing 360 recruitment, you might get away with incomplete data, as everyone’s on the same page and has the same big picture.

But this is just theory. In reality, this is rarely the case.

So why does this happen? If the ATS is such as important source of information - or the only source of candidate data in many agencies, why aren’t recruiters taking the time to add the right data to the tool?

Two main reasons for bad data

The first culprit is the relentless focus on KPIs and speed over data quality and thoroughness.

Agency recruiters are under the gun to meet targets – add 50 candidates to the ATS by the end of the week, and contact 250 hiring managers to keep those pipeline numbers up.

So what happens is that the time-consuming admin work gets pushed to the end of the week, resulting in a rushed job and a flood of incomplete information or bad data into the ATS.

Next to this, staffing agencies are battling another demon: high turnover.

The average tenure for an agency recruiter is 1-2 years. Seems long enough to learn the best practices and not leave a mess behind, right?

But the reality is that new joiners, especially if juniors, spend their first few months just finding their feet, more concerned with onboarding and learning the ropes than meticulously updating the CRM or Applicant Tracking System.  

And those on their way out? Let's just say accurate data isn't top of their farewell to-do list.

Of course, there are exceptions, and more seasoned recruiters might not introduce such issues. On the contrary, they might be the only ones trying to clean up the outdated data and fighting for accurate candidate and customer data.

But in most agencies, this toxic combination of short tenure and focus on sales numbers leads to a host of problems.

The impact of poor ATS data hygiene

The talent pipeline looks beautiful, number-wise, but few of those candidates make it to the end stages of the hiring process. With such poor data hygiene, it’s no wonder conversion rates in agency recruitment are so low.

The big picture gets lost in a haze of incomplete data points. Those secret lists every recruiter keeps, with top candidates and reasons to hire them? They’re completely useless if the recruiting data doesn’t make it to the ATS.

With each departing recruiter, a wealth of unrecorded information walks out the door. Without proper documentation in the ATS, the nuances of client relationships, candidate preferences, and market insights are lost.

So the next poor soul has to pick up the pieces, often redoing work that's already been done. They're forced to re-source candidates, re-screen applicants, and re-qualify clients simply because the information wasn't properly recorded in the first place.

The result?

Time and resources vanish into thin air, costing agencies not just money, but their reputation too.

But don't despair – there's hope on the horizon. These issues can be tackled head-on with the right strategy.

How AI can ensure data quality

I think by now we all agree that he consequences of poor data hygiene in agency recruitment are far-reaching.

Inaccurate information hinders efficient workflows, weakens client and candidate relationships, and ultimately leads to lost revenue.

Artificial intelligence offers a powerful solution to ensure data quality and combat data decay, as it brings about a capability that previous recruitment technology didn’t have: working with unstructured data.

In short, AI can take information in any format - written, audio, or video, and turn it into structured data sets that can be further used by other systems.

In agency recruitment, artificial intelligence can join candidate interviews and intake calls with hiring managers, listen in to conversations and take notes in real time, turning them into structured data sets afterwards.

Unlike human recruiters, AI doesn't forget information and doesn’t take messy notes - it just transcribes conversations word-by-word, and structures this information based on whatever templates it is asked to follow.

For example, AI can read a CV and extract contact information such as the candidate’s name, email, phone number, LinkedIn profile, and so on. After this, the AI can join the phone screen or video interview with the same candidate, and write down the skills, experience, and preferences of this candidate.

Through API integration, all these data points can be automatically pushed into ATS fields, with or without human intervention.

Of course, if it’s your first time using an AI workmate for automating your recruitment lifecycle, you might prefer keeping some control and reviewing or potentially editing data before it gets synchronized between systems.

All in all, AI can help with data enrichment, ensuring your ATS becomes the true goldmine of candidate information it's meant to be. This fosters better relationships with candidates and clients, leading to higher placement rates and a boost to your bottom line.

But how do you get there?

Implementing a data management policy

While AI is a powerful tool for combating data decay, it needs a strong foundation to work from.

This foundation is your data quality policy – a set of clear guidelines that ensure consistency and accuracy within your Applicant Tracking System.

Why is a data quality policy crucial for proper data management? For a few reasons:

  • Data format standardization: A well-defined policy dictates how data should be entered and structured within the ATS. This ensures all candidate information, from contact details to skills, follows the same format.
  • AI guidance: Your data quality policy acts as a framework for your AI assistant. By outlining specific data requirements and formats, the AI can effectively automate data population and cleaning tasks within the ATS. This frees up your recruiters' valuable time and minimizes the risk of human error.

All right, so how do you get there?

Follow the steps below to get to a reality where ATS data is properly managed and maintained with the help of AI.

  1. Launch a data clean-up initiative: Before implementing new policies for your recruitment process, it's essential to get a handle on your current data state. Conduct a thorough clean-up to identify and address any existing inconsistencies or inaccuracies.
  2. Develop a solid data quality policy: Work with your team to create a clear and concise data quality policy. This document should outline:
    • Data entry guidelines: How information should be entered and formatted (e.g., consistent date formats, standardized job titles).
    • Data ownership: Who is responsible for maintaining specific data sets within the ATS.
    • Data retention: How long to retain candidate information and the process for archiving or deleting outdated data.
  3. Integrate AI into your recruitment workflows to automate the process: With your data policy in place, leverage AI to streamline data management. Automate tasks like adding new candidates, updating existing profiles, and even extracting key information from calls and interviews.
  4. Shift focus from quantity to quality: Finally, move away from a culture obsessed with meeting quotas for new candidates or client contacts. Instead, prioritize the quality of your pipeline and placements. By focusing on the right candidates for the right roles, you'll ultimately achieve higher success rates and boost your agency's reputation.

Remember, a clean ATS isn't just about having tidy data – it's about unlocking a new level of efficiency and effectiveness for your entire staffing agency.

With a well-defined data quality policy and the power of AI, you can transform your ATS from a liability into a powerful tool for growth.

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