Data has always held a huge place in recruitment. Because data-driven recruitment means you’re making objective hiring decisions. They’re fact-based.
That “data” goes beyond your usual sources: resume screening, interviewing, and then extending job offers.
And without good data, you can’t have a good talent pool.
Because one thing’s for sure: the talent climate isn’t what it was a decade ago.
It’s evolving and changing more rapidly than ever.
To keep up with the change, you need to have a replicable recruitment process. Something that doesn’t just work on “luck”, something that succeeds with data-backed insights. That’s how you’ll be unique in 2025 and moving forth.
In this article, we’ll cover:
And more.
Every day, tons of data make space in this world. With increased availability, the problem of “data integrity” becomes inevitable.
But what is data integrity and why is it so important?
Data integration is the completeness of data. It also refers to the accuracy or the quality of data and its maintenance over a long time. That indicates that protecting data integrity is a continuous process.
One thing to understand data integrity is not the same as data security. The two concepts are related, but data integrity is different. Data integrity can be compromised when there’s a chance for:
You wouldn’t want all these problems. Neither would you want to spend the time, resources, and effort to fix these problems after they arrive. Plus, data integrity helps you make decisions based on strong data.
If, for some reason, your data gets compromised, it would have dire consequences.
Lastly, even a small mistake in data can lead to a ripple effect.
For example, if you store your candidate’s home address, or any other personal information (even their name), and there’s a small error in the spelling - it can lead to misinterpreted information.
Thus, for the sake of everyone (you and your users), you must invest in good data.
We know that data-driven recruitment means making objective decisions. Plus, it uses past data to help improve your recruitment process and make it more effective.
That’s the main goal behind this recruitment workflow. But there are several other benefits it entails. Here’s an overview:
@https://www.youtube.com/watch?v=Z8XGLAbsa5s
We know AI is trained on old data.
If that training data isn’t any good, it won’t give good outputs.
(that’s one major point that our integration partner, Firefish, talks about in the podcast)
This leads to a vicious cycle. You input bad data, feed the algorithms on useless information, and expect spectacular results.
It doesn’t work that way.
It can even lead to inaccurate AI-driven insights. All this leads to users blaming the AI for not being “useful” enough, which is why most people oppose the idea of using AI.
That’s one reason (and a benefit) of having integral data.
When you have accurate, updated, and useful data, you won’t have to worry about the bad outputs from AI.
An example here is Quil’s AI notetaker.
It uses real-time updates - which improves your overall recruitment processes (and fills your ATS with data that has integrity).
Another major problem in the recruitment industry is the lack of personalized candidate experiences.
According to research from SHL, 42% of candidates say no to offers because of bad experiences.
That means a huge percentage of candidates flunk out if they don’t feel they had a good experience. Especially the Gen-Z folks.
They’d report a negative experience if:
But…
If you have accurate data on the candidate, you can make your outreach process better.
You’ll know the person you’re contacting is the right fit for the job. Plus, the candidate will appreciate you paying attention to their profile.
Data integrity means nothing if it's not compliant.
For instance, if you see a company that stores but isn’t GDPR or SOC-2 verified, you know it doesn’t store data the right way.
These laws, verifications, or certifications may not seem like a big deal.
But data is important. Protecting its integrity is important. Thus, these frameworks are there for a reason.
Plus, it’s a legal requirement. Even if you have good intentions at heart, data can be mishandled and lead to compliance violations.
Recruiters waste valuable time sifting through unstructured recruitment data.
However, if you fit a strong data-driven recruitment process into your existing workflow, it leads to many, many benefits.
It means better sourcing, faster candidate matching, and more placements. After all, that’s what recruiters are after 😉.
(and an awesome testimonial from our client is a testament to this.)
Data-driven recruitment includes sourcing (using information from different channels - social media, job boards, agencies), the selection process, and the candidate experience.
We’ve talked about all of these in great detail.
With that, let’s look into some best practices for applying all this theoretical information.
Dashboards are an incredible way to visualize data, narrate its story, and make it useful for people who find it hard to interpret the data.
For instance, in Quil, you can track the minutes you’ve spent on the app, the calls you’ve taken, the number of outbound calls, inbound, and so on.
Recruitment data is not limited to your ATS. Expand your view by gathering insights from candidate surveys, job boards, social media analytics, and direct feedback from hiring managers.
Collecting data from various sources offers a greater perspective on your recruitment process. This diversity in data helps identify hidden trends and pinpoint areas for improvement. That might be overlooked.
A lot of AI-based recruiting tools aren’t foolproof. That’s why completely relying on these tools is a huge mistake.
A lot of this recruitment tech rejects even qualified candidates only because they didn’t fit the required format, had a different keyword in their resume, or had a couple of skills missing from its list.
Thus, as a recruiter, you need to understand the situation’s context, use your experience, and knowledge to make a decision.
It’s no secret that AI has made huge strides in recruitment. The impact has been so great that no one can stop talking about it.
And this is only the beginning.
The AI that we use today is ANI - narrow AI. It’s the weakest form of AI - and is still so powerful.
When we interact with Siri, Alexa, or Google Assistant - we think they’re incredibly knowledgeable. They know everything. But they lack that human essence.
With AI advancements, AI is predicted to grow at an unprecedented rate and level up to ASI - Super Artificial Intelligence. That’s where AI is expected to think like a human, or even better.
Thus, for recruiters who are against the idea of these “modern AI practices” … we have nothing to say to them - except you folks are missing out. And yes, we have data to back up why you’re missing out:
*stats are from Insight Global.
Since ASI isn’t going to be around for a decade or so, at the moment we only have ANI - and that’s only as good as the data it’s trained on.
And, for the moment, the stats show that without good data, even the best AI will falter (underscoring the need for continuous data integrity).
82% of companies have a firm belief in data and its capabilities for talent acquisition decisions.
That’s how much importance good data holds in recruitment.
As an AI-powered note-taking and ATS integration tool, Quil helps recruitment teams maintain accurate, structured, and actionable recruitment data.
With zero manual effort (but of course, you’ll need your human brain to connect certain dots).
Here’s what it helps with:
If you have a “people-first” approach at your recruitment firm, you also need to be data-driven.
Data speaks volumes about your recruitment strategy, hiring ethics, and a lot of other things.
In this AI age, if there’s one thing employees look for in companies, it’s their DE&I initiatives. It’s important what the data narrates about how we recruit, who we recruit, why we recruit them specifically, and where.
All of this information is backed with data. Thus, you need to be data-driven if you expect to have a people-first approach in this “rapidly evolving landscape” - as ChatGPT would put it.
Data integrity means your data is accurate and complete. It is important for making smart hiring decisions. Plus, good data builds a strong talent pool.
Our favorite, most-asked question.
An AI notetaker records and organizes notes automatically. It captures key details during interviews and updates. Plus it creates a list of actionable items. However, Quil is built specifically for recruiters, so it differs from the normal AI notetakers.
It updates your ATS, helps follow up with hiring managers and candidates efficiently, plus creates awesome write-ups.
Data-driven recruitment uses facts to guide hiring. It speeds up decisions and improves candidate matching. All of this leads to a more efficient process overall.
AI relies on the data it is fed. High-quality data means better insights and fewer mistakes. Poor data can lead to missed information.
Quil automates note-taking and ATS updates. It ensures candidate information is accurate. Quil also offers dashboards that help you spot trends quickly.
Quil is an AI notetaker that assists you with automation. It counters redundant tasks like note-taking and manual updates to your Applicant Tracking System (ATS). Plus, it integrates with your favorite platforms like Zoho Recruit, HubSpot, and Salesforce, making the entire hiring process feel like a breeze.
