The importance of data quality for effective candidate engagement

This piece was coauthored by our partners, Daxtra.

Imagine a world where you could easily predict the success of your candidate engagement strategy. You could tailor personalised communication, boost your brand reputation and significantly improve your overall performance.

If this sounds too good to be true - it isn't. With high-quality data to power your candidate engagement, you can improve your strategy, collaboration and performance. In this article, we'll share why data quality is important, how it impacts candidate engagement and present best data practices for recruitment firms and the tech companies that support them.

The role of data in candidate engagement

"In the age of talent, data is the key that unlocks the door to meaningful candidate engagement, where every interaction can lead to a game-changing opportunity."

Martin Bramall| Chief Growth Officer at idibu

Data plays a fundamental role in driving just about every business activity - successful candidate engagement included. 

Consulting firm, Gartner, shared that bad data quality costs organisations an average of $12.9 million per year. When it comes to data, the concept of quality over quantity has never been more relevant - especially when it comes to the direct impact that data has on the effectiveness of candidate engagement strategies. 

Successful candidate engagement has emerged as a crucial differentiator. It's no longer just about finding candidates, it's about standing out from the crowd and engaging them effectively. At the heart of this shift lies a critical component – data.

As a recruitment firm, you’ll typically be using various types of data in your engagement strategy, including:

Personal information: This includes basic details like name, contact information, and location. 
Professional details: Information about the candidate's education, work experience, skills, and certifications.
Behavioural data: Insights into the candidate's online behaviour, such as the type of job posts they interact with, their response time, and their preferred communication channels.
Feedback data: Information gathered from the candidate's feedback on the recruitment process, interviews, or job offers. 

Together, these data types provide a comprehensive view of candidates, enabling more effective engagement. However, recruitment companies, and the tech firms partnering with them, will often encounter challenges surrounding data, such as inconsistency or irrelevance. Issues like these can hinder the accurate applications of data, leading to ineffective candidate engagement strategies. 

Tech companies providing data solutions to recruitment firms often grapple with issues such as:   

Data accuracy: Ensuring the data is correct and up to date can be challenging, especially with large volumes of data.
Data consistency: With data coming from various sources in different formats, maintaining consistency is a significant challenge. 
Data security: Protecting sensitive candidate data from breaches or misuse is a critical concern.
Data integration: Integrating data from different systems or platforms can be complex but is crucial for a holistic view of the candidate. 

Addressing these challenges requires robust data management strategies and tools, as well as a commitment to continuous improvement of data management practices (more on these later). 

Why data quality matters in candidate engagement 

"Data quality is key to successful candidate engagement, as it can drive every response and decision you make. Keeping candidates engaged isn’t as simple as it once was, and ensuring your data is to a high standard provides the ideal baseline”

Martin Bramall | Chief Growth Officer at idibu

Poor data quality can have severe consequences for your engagement strategy. Inaccurate or outdated data can lead to miscommunication, misplaced targeting, and a significant waste of resources.  

Let’s imagine you reach out to a candidate with an outdated email address or incorrect job preferences. Not only will you likely have zero success in engaging a suitable candidate, but you’re also probably going to harm your organisation’s reputation in the process. That can be a big deal – especially since a massive 82% of candidates consider employer brand and reputation before applying for a job.

Similarly, say you’re sending an email to multiple candidates for a job opening only to find that half of the emails bounced back due to incorrect addresses. Or, consider the embarrassment of contacting a candidate for a job they were previously rejected for due to outdated data.

We could go on…there’s a long list of negative consequences of not keeping your data updated.  

Benefits of ensuring data quality 

"Data quality is a pillar when it comes to informed decision-making and is often the key to unlocking valuable insights in a data-driven world."

David Mercer| Head of Sales EMEA at Daxtra

On the other hand, if you can ensure a database full of current, relevant information, you’ll enjoy countless benefits. For starters, great quality data can enable more personalized and relevant communication with candidates. This can lead to improved candidate experience and a more positive brand reputation.   

You’ll likely improve response rates when candidates are more engaged. Better data can also increase efficiency in your overall recruitment operations, reducing both the time and costs associated with inaccurate data    

Strategies for ensuring data quality 

So, let’s look at how to mitigate data-related challenges. Here are just a few strategies to help you maintain and improve the quality of your data.

1. Implement data governance: Data governance involves establishing clear policies, procedures, and responsibilities concerning data management. It ensures that data is handled consistently and accurately across your organisation. A hardy data governance framework helps maintain data quality by defining standards for data collection, storage, processing, and usage.

2. Use data validation techniques: By data validation, we mean the process of checking your data for accuracy and consistency. This can involve a range of techniques, such as:
- Range checks - Confirming that a data point falls within a specified range.
- Consistency checks - Ensuring data is logically consistent.
- Completeness checks - Verifying that all required data fields are filled in. Implementing these checks during data collection can prevent the introduction of errors, boosting the quality of your data.

3. Invest in quality tools: There are various software tools available for automating many aspects of data quality management, from validation and cleaning to monitoring and reporting. Standardising your data involves transforming it into a common format. This makes it easier to compare and analyse data, improving its usability and quality. Data cleaning, also known as data cleansing, involves identifying, correcting, or removing errors in datasets. This could include dealing with duplicate entries, incorrect or outdated information, or missing data. It's important to regularly clean data and use quality tools that can clean and standardise data easily, quickly and with reduced risk of human error.

4. Train your team: Human error is a common cause of data issues, and your team can play a crucial role in maintaining data quality. Regular training sessions can ensure they understand the importance of data quality and how to achieve it. This can involve training on data collection methods, the use of data quality tools, and adherence to data governance policies.

5. Monitor data quality: Monitoring involves regularly checking your data against defined quality metrics. This helps identify issues early, allowing for timely correction. Metrics could include accuracy, completeness, consistency, uniqueness, and timeliness

And finally, remember that quality data is never “done.” It’s an ongoing process that will require a combination of robust policies, effective techniques, the right tools, and a well-trained team.

"Ensuring data quality is an ongoing process, and good quality data is a pillar of strategic decision-making. With reliable processes, organisations can ensure they maintain precision, and ensure overall good quality within their data."

David Mercer | Head of Sales EMEA at Daxtra

Best practices for tech companies supporting recruitment firms  

Recruitment firms heavily rely on technology to enhance their operations. Tech companies can play a transformative role by providing the necessary tools and solutions -- not only for collecting and storing data but ensuring its quality too.

To truly add value and support to recruitment firms effectively, tech companies need to go beyond simply selling products. So, here are three ways that tech companies can better support recruitment firms, particularly focusing on data management: 

Collaboration
Collaboration is key to understanding an organisation’s data needs. In the same way a recruiter will spend time with a candidate or client to gain deep insight into their background, preferences, and ambitions, a tech company needs to fully understand the needs of the recruitment business it’s working with.

Data informs everything from candidate sourcing to engagement strategies. Understanding the data needs of recruitment firms is the first step towards offering effective support. Whether you choose meetings, conference calls or workshops, tech providers should know their clients’ business inside and out. This will help tech companies to tailor solutions that add real value. 

Training
Even the most cutting-edge tools are useless if people don't know how to use them effectively. It’s up to tech companies to provide comprehensive training on their data management tools.

And don’t forget ongoing support. Beyond initial training, tech providers can offer access to resources like user guides, FAQs, and tutorials. A dedicated support team should also be available to resolve any issues promptly. 

Auditing
Auditing is the best way to ensure the ongoing effectiveness of tech. If you’re a tech firm offering solutions to a recruitment business, look to offer services for regularly auditing and evaluating their data.

Are they getting the most from your solutions? During an audit, you'll be able to identify any issues in your workflows, so you can better assess how to use tech solutions effectively to address your pain points. From there, solutions providers and recruiting companies can collaborate to address any issues and ensure the data remains of the highest quality. 

Making your data work for you 

To summarise, data is the lifeblood of recruitment. It is the basis of successful candidate engagement plans, which can help you fill roles faster and more successfully, as well as enhance your reputation and position you as an employer of choice.

Tech companies can offer vital support to recruitment firms, but to reach their full potential, the organisations and providers in their tech stack need to forge a close partnership and be fully aligned on practices, needs, and ultimate goals.

What are two tech stack solutions that are working to provide the best experience for their customers? 

Daxtra specialises in intelligent recruitment automation, semantic search and match, and resume and job parsing in 40+ languages. Its technology, built on AI and machine learning, seamlessly integrates with a recruitment ATS or CRM. Daxtra has been developing innovative recruitment software since 2002 and supports customers globally via offices across Europe, North America and Asia-Pacific.  

idibu is more than a multi-posting platform. Not only does it allow recruiters to effortlessly distribute their job ad to as many different sites as they wish, it's bursting with features to help your ads stand out. Seamless CRM integrations mean you can capture and manage data more efficiently than ever before. Learn more about how idibu could help you, here.