Introduction
As a boundaryless workforce becomes the new normal, the need for job data is on the rise. Scraping job data sites help individuals and businesses to navigate the labor market better.
Job posting data helps businesses in many ways, such as hiring new talent, creating a job aggregator, analyzing the job market, building skill development courses, determining compensation packages, and more. Let’s explore the techniques of job data scraping in detail.
Web Scraping Overview
Web scraping is the process of crawling websites to retrieve and store data locally or on the cloud. Manual web scraping is time-consuming and error-prone. Automated web scraping tools simplify this task by pulling data from relevant websites effectively. Job data scraping is highly sought after by organizations and individuals to study the job market and match skills with roles.
Individuals use job posting data to find competitive employers with the best benefits. Organizations can use this data to find potential candidates beyond boundaries. This data is also valuable for determining compensation and offering benefits based on current job market trends. Trend analysis also helps enterprises to become future-ready by identifying and overcoming talent gaps.
Importance of Job Data Scraping and Job Market Data
The number of people who use online channels and social media to look for new jobs is increasing exponentially. More than 30% of the global workforce actively seeks jobs using online job boards. Potential candidates compare organizations based on compensation packages, career growth opportunities, and work-life balance. So, organizations can raise their value in the talent market by analyzing job data and offering exactly what the talent is looking for.
Job data scraping pulls data from two critical sources – job aggregator websites and career pages of companies. The scraping bots fetch essential information, such as:
- Job description
- Job title
- Posting date
- Company name
- Salary range
- Job type
There are many free tools available for web crawling. These tools reduce the cost of fetching data for organizations. However, it is essential to remember that these job scraping tools only fetch data. Data science experts must analyze the available big data to derive meaningful insights.
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Pros |
Cons |
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Efficient crawling Fast data scraping Cost-effective for fetching large amounts of data Reliable and robust automation support Low maintenance overhead |
Involves a learning curve Needs continuous maintenance to obtain fresh data Data extraction is not data analysis Websites block automated scrapers |
Techniques for Scraping Job Postings
Collecting data from websites comes with its own legal and ethical issues. Not all website data can be crawled using bots. Also, organizations employing job posting data crawlers must understand the importance of privacy and protecting sensitive data. Multiple techniques are available to obtain job postings data, each with pros and cons.
Using Dynamic Microsoft Excel Queries
A simple and cost-effective way to fetch data is to write Excel queries to extract data feeds from external websites and store them in spreadsheets. One can find online tutorials to write such scripts. These scripts provide some automation benefits but don’t wholly automate the entire data scraping process. Individuals looking for job market trends can use it to get job data.
In-House Data Scraping Team
Organizations need big data for effective market trend research and analysis. Based on the needs, enterprises can research only their competitors, specific groups of job seekers, individual job roles, etc. Simple Excel scripts are no longer helpful in gathering data from thousands of websites. An in-house team of data scraping experts can design automation tools to extract relevant data. This is cost-intensive, and the extraction process has to be repeated periodically to get fresh data.
Hiring Data Scraping Services
Many service providers use data scraping tools to gather data required for organizations. With this technique, organizations can get the exact data they want without the hassle of fetching data. However, this type of service doesn’t deliver value because organizations can miss crucial data analytics by missing hidden patterns and trends. Also, there is no guarantee that these provide real-time supply data.
Cloud-Based Data Scraping Tools
Using cloud-based scraping tools enables the organization to scale data analytics without worrying about infrastructure challenges. These tools can be run as frequently as needed to fetch and store data on the cloud. Such tools work on real-time data, providing many scopes for analysts to derive meaningful and visual insights.
Job Market Data Applications
With a vast volume of data available for consumption and a host of AI tools that can leverage these data, businesses are looking for ways to use big data for business development. Job data has multiple use cases for companies, such as:
- Recruitment and hiring – Analyzing job data and competitor job postings will help businesses to hire top talent with attractive compensation packages.
- Market research and analysis – Job data is also useful in analyzing the latest job market trends, skill demands, competitor business performance, etc., all of which enable businesses to stay competitive.
- Data-driven decision making – Crucial decisions such as recruiting, training programs, and skill development courses can be made with better ROI if leaders have visual insights about job market data.
Job intelligence data helps build brand reputation and improve perception among the talent. Such data plays a crucial role in market research to identify competitors and improve existing company strategies. AI helps HR to improve candidate screening, training, talent development, and succession planning.
Common Challenges and Solutions
Despite the numerous benefits of job posting data and vast availability, it is difficult for businesses to access such data. Some of the common challenges that companies face while scraping job data are:
- Bot access restriction – The robots.txt file on a website specifies information access to crawling bots. If the websites restrict access to bots, market researchers can ask the website owner for permission or look for other data sources.
- Complex web page structure – Webmasters use complicated web page structures to enhance user experience. This makes it difficult for standard web scrapers to fetch data. Customizing scrapers for websites can help solve this challenge.
- IP blocking – Website publishers block scraper IP addresses. However, this can be addressed using proxy servers.
- Captcha and login requirements – Using Captcha and login to avoid robot website access is common. Modern-day scrapers have Captcha and advanced capabilities to overcome this issue.
Conclusion
Scraping job posting data is a niche process that needs dedicated servers, data storage, high-speed infrastructure, and skilled IT experts. Using a cloud-based scraping setup, organizations can get the best ROI from job data scraping. These tools extract complex job data from multiple sources and store them on the cloud for use in big data analytics. JobsPikr is a job posting data scraper that can integrate with your existing enterprise system to fetch website data seamlessly. You can access the most wanted job data on the cloud anytime and always get real-time data for analytics needs.
