The Rising Prominence of LinkedIn Data

The professional world collectively lives on LinkedIn. This platform‘s unparalleled scale and richness of member data offers a goldmine of insights to strengthen business strategy and performance.

In this post, we‘ll explore how organizations can extract value from LinkedIn datasets – understanding the types available, applications, leading providers, best practices and limitations of LinkedIn data usage.

My expertise draws from over a decade of hands-on experience in web scraping and data extraction projects for diverse clients worldwide. Let‘s examine how LinkedIn data is shaping data-driven decision making across functions.

With over 830 million members and 40 million registered companies, LinkedIn has become the world‘s largest professional network. Consider these statistics:

• 63% of B2B lead generation involves LinkedIn [1]
• 90% of recruiters use LinkedIn to source and vet candidates [2]
• 61% of C-level executives have a presence on LinkedIn [3]

This ubiquity and depth of professional insights available on LinkedIn makes it a vital data source for organizations seeking competitive advantage. Let‘s examine how.

A LinkedIn dataset refers to a structured collection of data systematically sourced from the LinkedIn platform.

This data can be extracted through methods such as:

• LinkedIn‘s APIs: Provide programmatic access to profile, company and other data as per usage terms. However, these have limits on call volumes.

• Web scraping: Automated extraction of publicly viewable info from LinkedIn via scripts. Helps overcome API limits but less reliable.

• Partnerships: LinkedIn partners with vendors to supply data leveraging login consent. E.g. Sales Navigator.

• Consent-based collection: Members explicitly permit capturing of their data for specific purposes.

• Aggregation: Compiling data from multiple public sources including LinkedIn profiles.

Based on your use case and budget, you can choose between pre-built LinkedIn datasets from vendors or custom data extraction using APIs and scraping.

Now let‘s examine the types of LinkedIn data available.

LinkedIn data can be categorized into four broad types:

1. Member Profile Datasets

These contain professional information shared by LinkedIn members on their profiles. Over 750 million members have public profiles on LinkedIn [[4]](https:// periphery.link/linkedin-statistics-for-business/ ).

Typical profile data points include:

• Basic details: Name, headline, location, connections etc.

• Employment history: Companies, titles, durations, descriptions etc.

• Education: Degrees, universities, fields of study etc.

• Skills: Expertise listed by members and endorsements received

• Licenses & certifications, volunteer experience, publications

• Recommendations: Written by connections

• Interests: Groups, causes etc. followed by the member

Such data reveals professional backgrounds, capabilities, career trajectories and interests of members.

Fig 1. Sample LinkedIn profile dataset (Image credit:BrightData)

2. Company Datasets

These provide details on organizations with a presence on LinkedIn. There are over 50 million company pages on LinkedIn [5].

Typical company data points are:

• Company name, industry, location, website

• Company size based on LinkedIn’s categorization

• Company description, specialties, products/services

• Follower statistics and employee counts

• List of employees on LinkedIn

• Recent news and updates

Such datasets facilitate understanding target companies, estimating company sizes and competitive profiling.

3. Job Postings Datasets

These provide insights into hiring demand based on job listings data from LinkedIn. Over 20 million jobs are listed on LinkedIn at any point [6].

Typical data points for job listings are:

• Job title, employment type, seniority level

• Skills, qualifications required

• Date posted, location, department, company

• Job responsibilities

• Salary range and applicant count

Analysis of job postings provides a pulse on hiring demand, talent needs, salaries and aids recruitment analytics.

4. Industry Trends Datasets

These provide aggregated insights on particular industries, companies and roles. For instance:

• Top skills demanded within an industry

• Emerging job titles in an industry

• Company growth trends – hiring, followers etc.

• Salary benchmarks for specific roles

Such datasets provide market intelligence to guide strategic decisions.

Now let‘s examine how this LinkedIn data powers key business use cases.

LinkedIn data provides unmatched professional insights that can strengthen your business strategies, processes and performance. Here are some top applications:

Recruitment & Talent Sourcing

LinkedIn profile data aids talent sourcing by revealing professional backgrounds, skills and interests of prospects. Recruiters can identify and attract candidates matching specific hiring criteria.

Analysis of skills demand and supply derived from profiles and job postings can shape effective talent strategy. Companies also use LinkedIn company followers data to benchmark employer brand against competitors.

According to LinkedIn research, employees hired through LinkedIn stay 43% longer at firms [7].

Sales Prospecting & Intelligence

LinkedIn company and profile data enables sales teams to identify, research and engage high-value accounts and contacts. Prospect company details, role responsibilities and interests allow customized outreach.

Sales professionals also utilize events, conferences and groups data from LinkedIn profiles to expand networks and uncover new leads. Integrations like Sales Navigator provide ready prospect data to enhance sales workflows.

As per LinkedIn, sales reps using Sales Navigator achieve 9% more quota attainment [8] than peers.

Competitive Intelligence

LinkedIn data delivers market intelligence to guide strategic decisions and positioning. Company datasets facilitate competitor profiling based on headcount, products, office locations etc.

Industry trend data provides strategic insights into market movements, emerging technologies, consolidation trends etc. Job postings data reflects wider economic conditions and demand forecasts.

For instance, a Forbes analysis of 50 million LinkedIn profiles revealed the top rising technology skills as blockchain, robotics and cloud computing. Such data enables tactical skill investments.

Executive Recruiting & Leadership Analysis

Datasets with senior leadership profile information enable executive recruiting and leadership assessment. Public data on executive career histories, responsibilities, skills and accomplishments facilitates targeted talent mapping.

Executive profile data also aids due diligence during leadership transitions. Red Flag analyses by tools like JitBit uncover concerns like false credentials, integrity issues, conflicts of interest etc. by scouring executive LinkedIn profiles.

Advertising & Audience Targeting

LinkedIn Marketing Solutions leverage member data for highly targeted advertising based on job role, industry, interests, skills etc. Specific audiences matching required attributes and behaviors can be created to maximize ad relevance.

Marketers can also identify influencers and thought leaders within target segments using LinkedIn data to amplify branded content and messaging.

Risk Assessment & Compliance

Profile data assists in background checks on employees, vendors, partners etc. by revealing associations, integrity concerns, and career gaps.

Organizations can monitor and mitigate third-party risks by screening executives and companies using LinkedIn data integrated with other sources. This is vital for regulatory compliance in sectors like financial services.

Now let‘s examine some leading vendors to access LinkedIn data.

Here are some prominent providers offering comprehensive, ready-to-use LinkedIn datasets:

Provider	Key Data Offerings	Collection Methods	Data Volume	Formats
BrightData	Profiles, Companies, Jobs, Interests	Web Scraping	170M+ Profiles	JSON, CSV, PostgreSQL
Datarade	Profiles, Companies, Jobs	Crawling, Partners	10M+ Profiles	JSON, CSV, XLS
Apollo.io	Profiles, Companies	Consent-Based	200M+ Profiles	JSON, CSV
Lusha	Profiles, Companies	Crowdsourcing	12M+ Profiles	API, Chrome Extension

BrightData stands out for its unmatched scale of over 170 million LinkedIn profiles harvested through sophisticated web scraping. Its datasets are refreshed to stay current as profiles change. Custom outputs and deliveries like PostgreSQL database imports are available.

Datarade offers pre-built datasets on a self-service marketplace via subscription plans starting at $99/month. It provides intelligent linking e.g. employees to departments to company hierarchies for richer insights.

Apollo focuses on consent-driven data gathering from members to enable sales prospecting use cases. Lusha also follows a crowdsourced data model tailored to sales, recruiting and marketing users.

As the usage of LinkedIn data gains traction, it is vital for organizations to follow certain best practices:

• Follow LinkedIn‘s Terms: Avoid violating LinkedIn‘s data usage guidelines. Understand restrictions on accessing member data.

• Anonymize Data: Remove personally identifiable information from collected datasets.

• Limit Data Retention: Only store LinkedIn data for as long as required for the business purpose.

• Secure Access & Storage: Allow data access on a need-to-know basis only. Employ adequate cybersecurity protocols.

• Transparency: Clearly communicate collection and usage of LinkedIn data to your users. Seek additional consent where applicable.

• Ethical Usage: Use data only for legitimate business needs and avoid superfluous collection.

• Stay Compliant: Adhere to regional regulations like GDPR, CCPA and industry-specific norms relevant to your use case.

Adopting an ethical, thoughtful approach while tapping into LinkedIn data will maximize its value for your business while avoiding potential pitfalls.

While LinkedIn data provides powerful insights, it is important to be aware of some limitations:

• Data can be incomplete or outdated, with members not updating profiles promptly

• Restricted access to certain data fields like contact info and recommendations

• Biases possible in self-reported profile data

• Privacy concerns around excessive usage of personal data

• Violating LinkedIn‘s terms of service or data laws entails legal risks

• Technical complexity in large-scale data extraction and processing

• Need for expertise to properly analyze and interpret LinkedIn data

Organizations should thus take a measured approach, combining LinkedIn data with other sources and not overly relying on it for business-critical decisions.

• The scale and professional richness of LinkedIn data provides unique competitive intelligence to strengthen strategies and performance.

• Top applications include recruitment, sales prospecting, market research, risk management and targeted advertising.

• Leading data providers like BrightData legally supply comprehensive LinkedIn datasets encompassing profiles, companies, jobs, interests and more.

• Responsible sourcing, usage and governance of LinkedIn data is vital for managing risks amidst growing adoption.

As LinkedIn‘s platform continues to widen, so will the potential to derive value from its data assets. Organizations must leverage LinkedIn datasets strategically under proper data governance to maximize business impact.