How to become a data integrity success story

how to become a data integrity success story

GIGO: these four simple letters can lead to one almighty mess of an ATS.

Mathematicians have known it for millennia; a small mistake at the beginning will inevitably lead to large errors. It was only at the advent of computer science that this concept got its own snappy acronym: garbage in, garbage out.

Data integrity has become critical to success in today’s binary world, where the sum of your organizational knowledge is represented in zeroes and ones. It allows for smooth operation and allows a staffing firm to capitalize on automations, freeing team members up to do what they do best.

What are the reasons for bad data, the dangers of it, and the perks of doing it right? Read on to find out.

Why data integrity matters

When it comes to your database, bigger doesn’t always mean better. Instead of wanting to know how many candidates you have in your database, clients are now asking questions like “How many candidates have you talked to in the last 90 days?” or “How many could you reach out to with a job?” And with a disorganized database, you may not have the answers to these questions.

“Most staffing firms are only engaged with about 10% of their database,” explains Robert Mann, Director of Sales & Marketing at 3DIQ. “And that number tends to go down as the pool of candidates goes up. If I was a business owner, and a staffing firm came to me and said, ‘We have a candidate pool of 10 million,’ I probably wouldn’t be interested. I’d ask, ‘How many of those can you get a hold of quickly? How fast can you engage with a good number of those candidates?’ Those are the questions I’d want answered.”

A staffing firm unaware of its data position is in a bad data position. The tell-tale signs of bad data may be all too recognizable: recruiters refusing to use the ATS to source candidates, candidates refusing to engage with your firm, and high unsubscribe rates on email, amongst others.

But these are nothing more than symptoms. The challenge of data integrity is to treat the underlying cause.

Identifying data integrity issues

The first step to a clean database is to get a sense of where your organization is.

A basic SWOT analysis (strengths, weaknesses, opportunities, and threats) is a great place to begin. This can help you to understand the data you’re collecting, how you’re collecting it, and what you’re doing with it. It’ll help you uncover the issues and how you might fix them. It’ll also allow you to identify missing and incorrect data – the garbage in that leads to the garbage out.

The idea is one of quality over quantity. If you want to realize the full power of your database, you need to remove any candidates that aren’t in your pipeline. By focusing exclusively on those who are, you make your ATS/CRM much more useful, to the point where recruiters are excited to search your database for talent.

Put the spotlight on data intake processes. Think of every avenue by which information enters your database. Do accurate and robust processes apply to each intake? Does your entire team understand the importance of data integrity, and their role in ensuring it? This is a task that takes a village, after all – the effort can’t be left to one person or team.

Data is more than just the information that allows you to do your job. Because it facilitates automation, it can also be an extension of your brand, for better or worse. Think of a candidate name entered in all caps – data that is then used in an automated email to the recipient. “Hi SANDRA,” it begins. The curtain covering your automated email process falls, and your reputation falls with it.

Tips for solving data integrity issues

How do you solve the data integrity issues you uncover? Three main solutions go a long way to solving most.

1. Training and operationalization

Data integrity begins with the frontline workers tasked with collecting and managing it. For your recruiters, maintaining data integrity has obvious, tangible, and incredibly alluring benefits. Clearly outlining these benefits will incentivize your recruiters to do the right thing.

“You need to create clear and concise processes and communicate them to your team,” explains Michelle Bousquet, VP of Organizational Effectiveness at Floyd Lee Locums. “We run a show, do, review system. We show our workers how to input, maintain, and utilize data, we get them to do it themselves, and then we review their work. We also capture that process to refer to it whenever needed.”

“Plan your work, work your plan,” adds Paul Sabatino, Regional Sales Manager, Solutions Consulting at Kyloe Partners. “Old habits die hard, so championing change requires a constant voice. Information breeds confidence; silence breeds fear. Communicating your data practices early and often is critical.”

Starting with the “why” is an effective strategy. By communicating to your recruiters that good data entry results in a wealth of manual and mundane tasks being completed automatically, it becomes an easy sell. They’ll eventually realize this allows them to make more placements and work more effectively.

“We had a recruiter who was a 30-year industry veteran. When she started, she was literally thumbing through Rolodexes and stacks of paper resumes,” says Billy Davis, Enterprise Customer Success – Automation and AI at Bullhorn. “I showed her the importance of data integrity and what would happen if she committed to it. Within just two weeks, despite being a senior member of the organization, she became my number one data quality recruiter.”

Recruiting is so much easier when quality data backs you up. When your team sees what’s in it for them, you’ll have no trouble encouraging adoption of good data hygiene habits.

2. Structured data

Data collection demands a game plan. Take a couple of days to map out what you hope to achieve with your data. Form an eagle-eye view of how you want to move a candidate through the entire lifecycle and the steps you’ll need. Set a goal to shoot for, then design all your processes around that goal. All of this will help you determine the information essential to collect.

It’s important to start small with your data collection process. At the beginning, ask for no more than six or seven pieces of information – any more than that, and you risk losing a candidate before you even begin. From that initial touchpoint, continue to build out the profile over time. Nurture the relationship to get those “nice to have” pieces of data that add color to the ‘must haves’ collected at the beginning.

Taking a structured approach to data collection helps to build trust. If your first contact in two years is an email asking for an updated resume, a candidate is unlikely to send it. But by keeping in contact over that period, sending out surveys and asking for updates, a candidate will be more open to your advances.

A good way to enhance your data collection efforts is to go through your own candidate process. Work to understand the pain points and remove friction wherever possible.

3. Data hygiene

Collecting structured data is one thing. Keeping it updated is quite another. The better the integrity of the data, the more automation possibilities, so this effort offers incredible rewards.

Cleaning your database can seem monumental, so starting with some easy wins is important. Contact information is vital – there’s no point in having a candidate in your system if you can’t reach them – so begin by removing records with no phone number or email. Checking and correcting statuses is another simple yet incredibly effective way to improve the health and hygiene of your ATS/CRM.

There are several automations that can help you in this effort. Resume parsing tools are a great example, as they allow a candidate to essentially update their own information. You can use automations to remove instances of duplicate entries by copying information automatically from one field to another and to remind people that they forgot to do something through emails, texts, or notes.

Becoming a data integrity success story

What can a firm expect if they were to follow through on all the advice laid out above? Davis offers some real-world examples.

“We had an agency that got rid of half a million bad, parsed-in entries that didn’t have any information. They transformed their database from huge and largely useless to a lean one that their recruiters were excited to use.

“I’ve also seen five- or six-recruiter firms functioning at higher levels than 100-recruiter agencies, purely based on the automations that quality data facilitates. You can get some really cool and complex stuff happening, but it all comes from a foundation of good data and process.”

By avoiding GIGO, you’ll enjoy QIQO – quality in, quality out – and the endless possibilities such a situation brings.

As Sabatino noted, “Data and automation success comes in many shapes and sizes. The worst thing you can do is nothing at all.”

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