Traditional methods of hiring, which relied heavily on resumes, interviews, human analytics, and gut feelings, are now being revolutionized by the power of big data.

Yet, the using of big data for recruiting within IT companies involves increasing budget sizes if you implement advanced AI tools. Since 2019, it has become 4 times more security- and compliance-related expenses necessary to launch AI features due to the Data and AI Leadership Executive Survey 2022.

We'll explore the plus factors of big data for recruiting and answer whether its impact on the hiring process is worth urgent investments.

What is Big Data in Recruitment?

Simply put, big data in recruitment is the practice of streamlining vast amounts of data to make informed decisions about hiring. 80-90% of data in the world is unstructured and has no business value in its original form.

Sophisticated algorithms and artificial intelligence (AI) collect data to identify patterns and trends “invisible” to human recruiters and help companies find ideal employees.

Sources of Big Data for Recruiters

Where should we look for the numbers and facts necessary to foster big data analytics in recruitment for tech company? Let’s observe the available data landscape.

External Sources

• Job boards & Social media

Data from social media profiles and job boards showcases employment histories of potential candidates, their professional network, interests, and industry engagement.

• Market & Industry Trends

Understanding market and industry trends (like hourly rates for the role you want to fill) can help you make informed decisions about hiring strategies and talent acquisition approaches. That’s why it’s crucial to monitor industry reports, economic indicators, and government labor statistics to anticipate changes in the tech talent market.

• Competitor Analysis

Analyzing data about your competitors' hiring practices and talent pools can help you identify opportunities and areas for improvement in their recruitment strategies.

CRM & ERP

Big data for recruiters includes some types of data from your CRM and ERP systems form business objectives for new hires.

• Sales Data

By tracking the sales growth, you can be more precise in estimating your team-scaling needs.

• Delivery data

If your plans (to finish the app update till the agreed date, for instance) tend to differ from reality, you may need to fill some skill gaps in your team to align with deadlines.

ATS

An ATS (Applicant Tracking System) is a software application which is able to organize a wealth of data on candidate interactions, interview feedback, and recruitment stages. ATS often includes reporting and analytics features.

• Resumes and Applications Data

This structured data from resumes, cover letters, and job applications can help identify relevant skills, qualifications, and experience from a large pool of candidates.

• Assessment and Testing Data

Data from pre-employment assessments and skills tests, like live coding sessions, can be valuable for predicting a candidate's potential success within a tech company.

• Candidate Feedback Data

Collecting feedback from candidates who have gone through the hiring process can provide valuable insights into the candidate experience, allowing you to make improvements.

HRIS

HRIS, or Human Recourses Information System, works similarly to ATS, but it contains the data about existing team members. By studying HRIS data, you can attract better candidates.

• Performance Tracking

Existing team performance data is a source of information necessary to identify patterns and traits associated with successful hires.

• Compensation & Benefit

You can decide on rates and describe possible benefits and compensation growth for new developers or other tech specialists, taking into account your current compensation programs for similar roles.

• Learning Progress

You can determine the most promising candidates for new team roles inside your company. Check if some of your hires have demonstrated they can easily acquire new skills!

• Exit Interviews

By studying the scores of exit interviews, you learn why specialists leave and can prevent those scenarios in the future.

A good idea is to integrate your HRIS with ATS, CRM, external sources, and data visualization tools like Tableau, Power BI, and QlikView. Such tools allow users to create interactive dashboards and various reports that highlight, for example, results of tracking key performance indicators (KPIs).

How Big Data Recruiting Changes Hiring?

Using big data for recruiting makes hiring less time-consuming and more bias-free for recruiters and hiring managers. We can name 5 reasons why it happens.

1. Enhanced Candidate Matching

   Advanced machine learning and AI algorithms can help you automate candidate screening and verify the information from resumes and job applications. Those algorithms make a deep background employment check and match candidates with job openings.

2. Predictive Analytics

   Big data algorithms can analyze historical hiring data and predict which candidates will perform better in the long run. That enables recruiters and hiring managers to reduce tech team turnover and increase developer’s life cycle accordingly.

3. Diversity and Inclusion

   By studying demographic data from HRIS, it’s possible to identify uncovered population groups. Thus, you can build more diverse and inclusive teams.

4. Improved Candidate Experience

   With big data recruiting on board, you leave candidates satisfied with the company, even if they don't get the job.

   When you are hunting for tech talent with the help of AI-powered chatbots and automated scheduling systems, candidates can have a smoother and more personalized experience throughout the application and interview process.

   Want to identify potential red flags or positive indicators? NLP (Natural Language Processing) techniques cover that, analyzing the sentiment and content of candidate communication, such as emails, chat interactions, and interview transcripts.

5. Cost Savings

   Big data can help you optimize recruitment budgets by identifying the most effective sourcing channels and strategies.

The Future of Big Data and Recruitment

The upcoming decades of the 21st century will bring more tasks for big data in the recruitment sector due to the general tech progress. Here are some of the relevant tech shifts.

1. Augmented Intelligence

   Augmented intelligence, a fusion of human and artificial intelligence capabilities, will become increasingly prevalent. Recruiting teams will work alongside AI systems that provide valuable insights and recommendations, enhancing their decision-making processes.

2. Skill-Based Hiring

   Traditional qualifications and degrees will take a backseat to skill-based hiring. Big data will contribute to hiring pre-vetted tech talent, by enabling tech teams to engage candidates with actual abilities and competencies, rather than just with excellent educational background.

3. Continuous Candidate Engagement

   Big data and recruitment will build a continuous engagement process. Instead of relying solely on job postings, companies will use data-driven insights to nurture relationships with potential candidates over time, ensuring a steady talent pipeline.

4. Ethical AI and Bias Mitigation

   There will be a heightened focus on ethical AI and bias mitigation in people analytics. Now, governments actively develop guidelines to ensure responsible and fair use of technologies you leverage when dealing with big data in recruitment.

5. Talent Analytics Platforms

   Talent analytics platforms will become central to HR operations. These platforms integrate with various data sources, providing comprehensive insights into workforce planning, performance, and development.

Should Recruiters Stay in The Past?

Big data analytics and IT recruitment agencies work shoulder to shoulder. It is hard to replace the human element in recruitment with mechanisms. While AI developers improve AI systems responsible for big data recruiting, we need somebody to evaluate their progress and give constructive feedback.

Learning some stats and trends is not enough to launch a successful hiring campaign. Recruiters have years of experience. You may struggle with structuring your hiring process with or without AI prompts. At the same time, professional recruiters armed with the big data research results can fill more positions and invent the boldest strategies of candidate attraction.

From that perspective, implementing big data processing for recruiting in small and mid-size tech companies may turn into buying the cat in a sack. Should you feed that “glutton cat”? We have some doubts and some stats that might make you more careful with your first steps in that direction.

• According to the study by Capgemini, 50% of US companies (and 43% of the companies worldwide) face budget constraints as the main challenge of using big data algorithms.

• 43% of IT decision-makers consider big data processing a too high load on their IT infrastructures.

Using Big Data for Recruiting Without Risks

Wonder what is an alternative solution to ensure that your hiring is data-driven? Outstaffing teams constantly sharpen their data analysis skills and choose the best modern tools, including ATS and HRIS as must-haves, to conduct talent market research and compare candidates. In case you partner with Outstaff Your Team, data-based recruiting could be the part of talent management services where payrolls and well-being check-ins included.

Join the family of our clients to see the new recruiting standards. Good recruiters don’t stay in the past. They adapt to changes and satisfy more complex requests.

FAQ

by Kateryna Balakyreva
on September 20, 2023. Share on LinkedIn Post on Twitter

Kateryna joined the IT industry 3 years ago. Reviewing B2B software and related frameworks, she concluded that the best-in-class programs need well-built teams and started to write about tech teams scaling. Her natural habit to improve texts and search for alternative visions comes from working at the publishing house in early youth.