LinkedIn Datasets: Types, Applications and Providers in 2024

Updated on Jan 2

6 min read

Table of contents

What is a LinkedIn dataset?What data is included in the LinkedIn dataset?How to access LinkedIn datasets?Applications of LinkedIn Datasets Best practices for using LinkedIn data ethically and responsibly Scraping LinkedIn data vs. utilizing LinkedIn datasets: which is better?Top 4 LinkedIn dataset providers: key features and offerings Further reading External links

LinkedIn datasets have become a valuable resource for businesses and professionals across different industries. Leveraging these datasets can lead to improved talent acquisition strategies, comprehensive market research, and efficient competitor analysis.

In this article, we will explore the different types of LinkedIn datasets, their applications, and the top providers in the market. We aim to provide you with a comprehensive understanding of the current landscape so that you can make informed decisions when selecting the LinkedIn dataset provider that meets your unique requirements.

What is a LinkedIn dataset?

LinkedIn data refers to the user-generated information available on the LinkedIn platform, such as user profiles, company pages, industry trends, and events.

A LinkedIn dataset is a collection of structured data obtained from the LinkedIn platform.

What data is included in the LinkedIn dataset?

A LinkedIn dataset includes different data points related to users, companies, and job postings. Here are some of the data points included in a LinkedIn dataset:

LinkedIn profile dataset: Work experience title, position, current company, education, connections, avatar, skills, and endorsements.
LinkedIn company dataset: Company page, industry size, # of followers, website, location, and employee counts.
LinkedIn job posting dataset: Job title, date posted, number of applicants, requirements, company, and location.

Types of LinkedIn Datasets

There are different types of LinkedIn datasets that can be categorized based on their sources and the methods used to obtain them. The choice of LinkedIn dataset depends on the specific needs, use case, or budget of individuals and businesses. The main types of LinkedIn datasets include:

Public LinkedIn datasets: They can be accessed through LinkedIn’s public APIs and by web scrapers. LinkedIn APIs provide a more reliable way to access LinkedIn data. However, APIs have rate limits and restrictions that regulate the volume and frequency of data requests.
Web scraping allows users to collect specific data points tailored to their specific requirements. However, web scraping may violate LinkedIn’s terms of service and data privacy regulations (e.g., GDPR).
Proprietary LinkedIn datasets: Data is available through its premium products and services, such as Sales Navigator and LinkedIn Talent Insights (Figure 1). Proprietary datasets provide businesses and recruiters with exclusive access to certain data points that may not be available through public datasets.

Figure 1: LinkedIn premium products

Source: Kinsta¹

3. Third-party LinkedIn datasets: Collections of data obtained from third-party sources and supplemented with LinkedIn information.

How to access LinkedIn datasets?

There are several methods to access LinkedIn datasets. Regardless of the method you select, it is essential to adhere to data privacy regulations and LinkedIn’s terms of service.

LinkedIn API: LinkedIn provides several APIs that allow developers to access and obtain data from the platform. Some of LinkedIn’s APIs include:
- LinkedIn Company API: Provides access to publicly accessible LinkedIn members’ profile data, such as name, headline, profile picture, and location.
- LinkedIn Profile API: Allows users to obtain company data, including company description, industry, and employee count.
Web Scraping: Web scraping techniques can be used to access and extract publicly accessible LinkedIn data. This method of obtaining LinkedIn data can be suitable for large-scale LinkedIn data collection projects.
Third-Party Data Providers: LinkedIn data providers are companies or platforms that provide access to LinkedIn datasets through third-party datasets, APIs, or web scraping tools (Figure 2). Some companies specialize in providing tailored LinkedIn datasets for a specific use case or industry.

Figure 2: A sample of Bright Data’s LinkedIn dataset

Source: Bright Data

Data Enrichment Services: Integrate LinkedIn data with other data sources to provide a more comprehensive individual customer or prospect profile. These services can assist sales and marketing teams in better targeting potential customers.
Since data enrichment services typically focus on enriching individual data points, they may be limited to providing industry-level information. If you require broader insights that combine LinkedIn data with other sources, third-party data providers offer more extensive and targeted insights.

Applications of LinkedIn Datasets

1. Recruitment and talent sourcing

LinkedIn datasets can help recruiters identify talents and streamline their talent acquisition process. LinkedIn datasets provide a significant amount of data on professionals, including their skills, work experiences, and education. This information can be used to conduct a targeted candidate search, tailor talent acquisition strategies, and identify areas for improvement in employer branding efforts.

2. Market research and competitor analysis

LinkedIn datasets might be useful for market research and competitive analysis. Organizations can use the professional data accessible on LinkedIn to:

Reveal industry trends like emerging technologies, popular job titles, and in-demand skills
Benchmark a company’s performance against its competitors
Identify potential partners or acquisition targets based on factors such as company size and industry.

3. Lead generation

LinkedIn has more than 930M members with over 63 million registered companies (Figure 3). LinkedIn datasets enable sales professionals to target the right prospects and create tailored outreach strategies. They can analyze a prospect’s professional background, including their connections and interests, enabling them to create personalized outreach messages. Sales professionals can also reveal upcoming events and conferences by utilizing LinkedIn data, allowing them to expand their network and identify new leads.

Figure 3: Figure 3: Showing the numbers of LinkedIn members from 200 countries and regions worldwide

Source: LinkedIn²

Best practices for using LinkedIn data ethically and responsibly

Ensuring ethical practices when collecting and utilizing data is important to avoid legal and ethical concerns. For example, HiQ Labs, a data analytics company, scraped publicly available LinkedIn profile data for a professional skill analysis. However, LinkedIn sued HiQ Labs in 2019, alleging that HiQ Labs violated the Computer Fraud and Abuse Act (CFAA) by accessing LinkedIn’s data without authorization. The Ninth Circuit determined that HiQ Labs’ actions did not violate the CFAA because the data was publicly accessible.

Follow LinkedIn’s terms of service: LinkedIn outlines data usage limitations, unapproved use cases, and access limitations in LinkedIn profiles in their terms of service. Adhere to LinkedIn’s terms of service to ensure that you are complying with their guidelines and policies.
Compliance with data protection laws: It is essential to adhere to regional and industry-specific regulations, such as the General Data Protection Regulation (GDPR) in the European, the California Consumer Privacy Act (CCPA) in the United States.
Secure storage: You can employ access restrictions to limit who can access your stored data or use secure storage infrastructure, such as firewalls and intrusion detection systems, to protect data from unauthorized access.
Anonymize data: There are numerous techniques for anonymizing data, including data masking, pseudonymization, and generalization(Figure 4). Data anonymization removes or alters personally identifiable information (PII) to assist organizations in protecting the privacy and adhering to data protection regulations.

Figure 4: Data masking substitutes sensitive data with other symbols and characters

Source: Informatica

Scraping LinkedIn data vs. utilizing LinkedIn datasets: which is better?

You can obtain public LinkedIn data either by scraping using a LinkedIn scraper and web scraping API or by utilizing pre-built datasets. The choice between scraping LinkedIn data and using LinkedIn datasets depends on your specific requirements and budget, as both have their advantages and disadvantages.

For instance, scraping data directly from LinkedIn may provide more up-to-date information. However, web scraping may require technical expertise to set up and maintain web scrapers, and it can be time-consuming and resource-intensive compared to pre-built datasets.

On the other hand, pre-built datasets save time and resources, and using a dataset from a reputable data provider may eliminate legal and ethical concerns. However, pre-built datasets may not be tailored to specific requirements. It is crucial to select a data provider that allows users to modify the data to fit their unique needs.

External links

1. Osman, M “Mind-Blowing LinkedIn Statistics and Facts (2023)“. Kinsta. Retrieved 26 April, 2023.
2. LinkedIn Statistics

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