You may hear people say how important quality data is (and that’s true), but how does that genuinely impact your business? Well, let me start with a quick story.
I once had a company moving to a new ATS platform. In the process of conducting training, I mentioned to them one of the main reasons my team and I were doing such intricate work: creating a better structure to allow their business to use automations. They looked about as perplexed as a dog hearing barking on the TV. What did “automations” even mean to them and their business?
I explained the whole goal of creating a better structure is to provide a framework for enhancing data quality. That way, when his team begins building automated email campaigns, text messages, or consistent follow-ups, the message goes to the right people at the right time, almost like magic. This in turn makes everyone’s job easier, allowing people to get back to foundations of building relationships with professionals and connecting them with the right positions. And just like that, the enchanting reality of good data made sense.
But let’s back up and start from the beginning. How do you know if you are cursed by bad data or are showing the signs of it?
The Signs of Bad Data
Let’s start with a working definition. When we talk about data quality (DQ), we’re referring to the dependability of your data from a decision-making standpoint. You need accurate information that can support your choices and provide insight in ways that make meeting and measuring your KPIs simple.
What constitutes quality data in your ATS?
- It may sound obvious, but it can often be overlooked. Your data must first and foremost be accurate and valid. Names need to be spelled right, along with current contact information – this includes details like up-to-date email addresses and mobile phone numbers.
- Next, it must be timely. When an individual updates contact information or availability through your website or other means, how quickly is that updated in your ATS? Knowing whether you last spoke with the candidate three weeks, three months, or three years ago has a significant impact in your communication.
- Your data must also be consistent and complete – do you have clear instructions on how to mark someone’s status in your ATS? Are your status fields available or relevant? If certain fields are left empty or tags left unmarked, summary reports will be inaccurate.
- And last, your data must be garbage in, garbage out (GIGO) proof. If you input incorrect data into your ATS, your output will be inconsequential. For instance, is your framework set up so that one phone number or email address can only be used once? Or are your recruiters creating several records for the same candidate each time a new employee stumbles across him or her? If not, duplicate profiles will be made in troves, and you will have a real mess on your hands.
As you can see, bad DQ is a multi-tiered issue. And according to Gartner, poor DQ costs organizations $12.9 million annually, accompanied by a host of problems that lead to severed customer relationships and misinformed decisions.
If your searches aren’t yielding the results you expect (i.e., you see the same batch of usual suspects with every candidate search), the odds are high your data is bad or lacks the proper automation framework. If your recruiters are pulling in candidates from job boards without considering their quality, your database might grow quickly, but more like weeds than flowers because they are prioritizing quantity over quality.
You need trustworthy data. You can have a plethora of data fields, but if recruiters don’t fill those out properly, then you are creating data that has no relevance or data that is not usable. When that happens, your team will lack the critical information needed to communicate well with your target audience.
If you have a large number of candidates in your system and cannot locate those diamonds in the rough easily, your DQ maybe to blame. You must find a way to whittle down the extraneous from the exquisite. But how?
Valuing Quality and Quantity Accurately
Let’s say you have three million candidates – can you really search through them all? You certainly don’t have the staff for that. Fun fact: no one does. Everybody works with approximately 600 to 1200 people and that’s all that’s manageable.
This is where automations swoop in and save the day to weed out unqualified or ill-fitting candidates. You can refresh your information by asking candidates to fill out/update their info for the database, completing any super important fields so your recruiters or the staff always have what they need. You can remove those who don’t respond, those who don’t have the right qualifications, and in short, those who don’t do anything for you.
It’s worth saying that you can have both quality and quantity, but you must undertake that goal from the ground up with the necessary framework. With today’s technology quantity isn’t the hard part. That’s why any of our solutions are always focused on quality; without it, you won’t be able to deliver the right candidates to clients when their needs are urgent.
Construct a Solution for Your Business
The crux of the challenge across all recruiting firms is creating a well-structured data ecosystem. To produce such an environment, you must get the structure of the database fixed first before you even begin looking at new or updated tools to help you with your data. And when you properly create fields and input accurate information, you create trustworthy data, allowing you to do what you do best: build genuine relationships with your candidates and contacts.
Cubex can help in evaluating your data structure, remove or archive the bad data and get your ATS up to relevant speed. Our magical methodology is all about functionality. We have an end goal to make your lives easier at the end of the day. And that’s exactly what good data does – makes your job easier.
If you’re ready to create better data, from structure to quality, Cubex is here to make that wish a reality. Our services can help automate your process and figure out quality issues.
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