LinkedIn Datasets: Types, Applications and Providers in 2024

ByPaul Christiano Last Update on

Hey there!

As a professional in this data-driven age, you’re likely always on the lookout for robust datasets to power analytics and uncover strategic insights about your customers, competitors or industry.

Well, look no further than LinkedIn – the mother lode of professional data on over 930 million members and counting.

In this detailed guide, I‘ll walk you through everything you need to know about LinkedIn datasets – from the data goldmine available, how to access it, top use cases, ethical usage and the best providers out there today.

Let‘s get started, shall we?

What is LinkedIn Data Exactly?

In simple terms, LinkedIn data refers to all the user-generated information flowing through the LinkedIn platform, including:

  • Member profile data: work experiences, education, skills, recommendations, connections etc. This makes up the bulk of LinkedIn data.
  • Company Pages data: industry, locations, employee counts, descriptions, follower counts etc.
  • Group data: discussions within LinkedIn groups around industries, interests etc.
  • Job posting data: salaries, applicant counts, requirements, skills needed etc.
  • Content data: published posts, articles and links shared on LinkedIn.

With over 310 million monthly active members and 20 million company pages, you can imagine the vast breadth of professional data getting created daily on LinkedIn.

This raw LinkedIn data can be scraped, collected via APIs and packaged into structured LinkedIn datasets for analysis.

Now let‘s dive deeper into the types of LinkedIn data and datasets available out there.

Key Categories of LinkedIn Datasets

LinkedIn datasets can be grouped into categories based on various parameters:

By Use Case

1. Recruitment & Talent Datasets

The most popular use of LinkedIn data. Contain member skills, job titles, experiences to identify and vet top talent. Some examples:

  • Tech talent dataset with developers, programmers etc.
  • Healthcare dataset with physicians, nurses and other medical professionals
  • Finance talent dataset with bankers, analysts, accountants etc.

2. Company Intelligence Datasets

Provide a 360-degree view of target companies or competitors. Examples of data points:

  • Company headcount
  • Locations
  • Technologies used
  • Competitors
  • New hires and departures

3. Sales & Marketing Datasets

Fuel hyper-targeted outreach and messaging to prospects with data like:

  • Contact names, titles and emails
  • Company information
  • Groups and interests
  • Content engagement data

4. Business Development Datasets

Identify partnership, investment and acquisition targets based on attributes like:

  • Company funding and valuations
  • Technologies possessed
  • Patent assets
  • Leadership team skills

By Collection Method

1. LinkedIn API Datasets

Pull data directly using LinkedIn’s APIs like the Company API and Jobs API. But these have major usage limits.

2. Web Scraped Datasets

Involve scraping data from LinkedIn profiles and pages. Risks violating LinkedIn‘s ToS if done incorrectly.

3. Third-Party Curated Datasets

Specialist providers acquire LinkedIn data legally through partnerships and package it into business-ready datasets.

Notable Data Points Included in LinkedIn Datasets

Now let‘s look at some of the wealth of data points that can be extracted from LinkedIn and compiled into datasets:

Member Profile Data Points

  • Name
  • Headline
  • Location
  • Industry
  • Profile photo
  • Summary
  • Experience
  • Education
  • Skills
  • Recommendations
  • Connections

Company Page Data Points

  • Company name
  • Followers
  • Location
  • Industry
  • Company size
  • Description
  • Website URL
  • Founding year
  • Technologies (on some pages)
  • Leadership team

Job Post Data Points

  • Job title
  • Company
  • Location
  • Date posted
  • Responsibilities
  • Minimum qualifications
  • Required skills

There are over 50 million job postings listed on LinkedIn at any given point!

Group Data Points

  • Group name and category
  • Discussions and comments
  • Member lists and follower counts

Content Marketing Data Points

  • Content text, images and videos
  • Engagement metrics
  • Shares and links
  • Comments and reactions

As you can see, LinkedIn data paints a rich, multi-dimensional picture of professionals, companies, jobs, content and industry dialogues.

4 Major Ways to Access LinkedIn Datasets

Now that we‘ve seen the breadth of LinkedIn data up for grabs, how do you get your hands on it? Here are the main options:

1. LinkedIn APIs

LinkedIn offers several APIs to retrieve company, job and profile data from their platform. However, these have strict daily usage limits and restricted access.

2. Web Scraping

Tools can scrape data from LinkedIn pages, but this risks violating LinkedIn‘s Terms of Service if done incorrectly or unethically.

3. Third-Party LinkedIn Data Providers

These specialist data platforms acquire LinkedIn data legally through partnerships or APIs. They package and deliver it in flexible, business-ready formats.

4. LinkedIn Premium Products

LinkedIn sales products like Recruiter and Sales Navigator offer access to some exclusive company and contact data.

I‘ll expand more on choosing providers later in this guide.

Critical Business Applications of LinkedIn Data

Now let‘s explore some of the most popular business use cases where curated LinkedIn data can provide a strategic edge:

1. Recruitment and Talent Sourcing

LinkedIn has become the go-to platform for recruiting and talent intelligence today.

In 2022 alone, over 92% of job recruiters used LinkedIn to source and vet candidates.

LinkedIn data empowers smarter talent sourcing by revealing professional skills, titles, experiences and other attributes.

This data enables recruiters to:

  • Identify "passive" talent not actively job searching. There are over 740 million professionals in this category!
  • Discover candidates with niche, in-demand skills – like cloud computing, AI, cybersecurity etc.
  • Filter and rank candidates that best match job requirements
  • Craft targeted outreach messages based on a candidate‘s profile
  • Benchmark compensation and benefits using salary data
  • Gain insights into talent availability across different markets and regions

In short, LinkedIn datasets provide the intelligence to find and engage qualified talent faster.

2. Competitive Intelligence

In 2022, over 80% of companies used LinkedIn to research competitors.

LinkedIn data offers unparalleled competitive insights like:

  • Company team size and locations
  • New hires – especially at leadership levels
  • Departing employees
  • Technologies and skills leveraged
  • Patent assets
  • Partnerships
  • Leadership and org structure changes
  • Funding and valuations

These insights empower strategic benchmarking and analysis vs. rivals and peers.

They help assess competitor strengths, strategies and threats accurately. Leaders can then respond and position themselves intelligently.

3. Lead Generation

LinkedIn is the top channel today for B2B lead generation, ahead of options like tradeshows and email.

In fact, over 80% of B2B leads come from LinkedIn today!

LinkedIn data fuels sales prospecting and outreach by revealing:

  • Ideal target companies based on attributes like industry, headcount etc.
  • Contact names, titles, and email addresses at these companies
  • Technologies used to identify product-fit
  • Groups and interests for hyper-targeted messaging
  • Recent content interactions indicating warm prospects

Check out the image below showing sample LinkedIn lead generation data:

image

With these insights, sales teams can nurture prospects intelligently and boost qualified pipelines.

4. Market Research and Trend Analysis

LinkedIn data also provides macro market insights, including:

  • Hiring demand across locations, industries and roles
  • Most in-demand and emerging skills by segment
  • Salary trends by roles, experience levels and regions
  • Demographics of industry professionals
  • Content consumption patterns
  • Industry dialogues and buzz

These insights help leaders spot trends, size opportunities, benchmark performance and derive strategic recommendations.

Just look at the sample data visualizations below based on LinkedIn data:
image image
Now that you‘ve seen the immense potential of LinkedIn data, let‘s switch gears to discussing how to use it legally and ethically.

Best Practices for Ethical Usage of LinkedIn Data

While LinkedIn data offers golden opportunities, you absolutely want to avoid misusing data or violating people‘s privacy.

Here are some best practices I highly recommend adopting:

  • Anonymize data where possible – this includes masking identities, aggregating data points, blurring images etc.
  • Acquire data only from trusted providers – ethically via APIs or partnerships to avoid legal issues. Never scrape without permission.
  • Limit internal access to LinkedIn data and store it securely on encrypted drives, behind firewalls etc.
  • Aggregate insights and derive macro trends rather than directly using raw, individual data.
  • Let members opt out of data collection.
  • Have clear data retention and deletion policies.
  • Follow regional regulations like GDPR in Europe and CCPA in California for data protection.

Stay on the right side of data ethics, and you‘ll be amazed at the powerful above-board insights LinkedIn data offers!

Next let‘s explore your options for accessing compliant, business-ready LinkedIn datasets.

Should You Scrape LinkedIn or Use Curated Datasets?

Let‘s compare the pros and cons of web scraping LinkedIn yourself vs. purchasing readymade LinkedIn datasets:

Web Scraping

Pros

  • Fully customized latest data possible
  • Flexible large-scale extraction

Cons

  • Technically very challenging
  • High legal compliance burden
  • Tricky to maintain as site changes
  • Heavy time and resource intensity
  • Data inconsistencies and quality issues

Curated Datasets

Pros

  • Data sourced ethically and legally
  • Clean, structured format
  • Fast and convenient access
  • Technical complexity handled by provider

Cons

  • Some lack of customization
  • Data lags slightly behind live site
  • Additional costs involved

As you can see, leveraging curated datasets from specialist providers offers significant advantages for most use cases.

The key is choosing a provider that offers maximum customization and data freshness while handling the heavy lifting.

Next let‘s compare some leading options.

4 Top LinkedIn Data Providers Compared

Many providers exist today offering LinkedIn datasets. I‘ve researched and handpicked a few top, reputable options for you to consider:

Bright Data

BrightData

Key Features

  • Custom output fields and filters
  • Continuous dataset updates
  • Range of file formats – JSON, CSV, XLSX etc.
  • Millions of accurate residential proxies to prevent blocking

Ideal For

Data scientists, analysts, developers needing flexible, configurable LinkedIn data extraction.

Pricing

  • Free samples
  • Starting at $0.001 per record
  • Custom quotes available

Visit BrightData

Datarade

Datarade

Key Features

  • Preview and sample datasets before buying
  • Certification of provider data sources
  • Custom dataset building
  • Data needs assessment

Ideal For

One-stop data marketplace to discover tailor-made LinkedIn datasets from 100+ providers.

Pricing

  • Free registration and platform access
  • Pay per dataset purchased

Visit Datarade

Kaggle

Kaggle

Key Features

  • 1000s of open datasets to browse
  • Discussion forums for each dataset
  • Online data analysis tools
  • Dataset competitions and challenges

Ideal For

Data scientists and analysts interested in open datasets and community.

Pricing

  • Free access to public datasets
  • Paid plans for premium features

Visit Kaggle

Data.World

DataWorld

Key Features

  • Searchable data catalog
  • Online data previews and visualizations
  • Enterprise-grade data hosting
  • Access control for security

Ideal For

Sharing and hosting open datasets securely.

Pricing

  • Free for individuals
  • Paid plans for storage, support etc.

Visit Data.World

Key Takeaways and Next Steps

I hope this guide has shed light on the vast potential of LinkedIn data and how to tap it properly. Here are some key tips to take away:

  • Clearly identify your business goals and LinkedIn data needs upfront.
  • Maintain high ethics and compliance when collecting and using any data.
  • Leverage curated datasets from reputable providers for convenience and legality.
  • Look for customization options and fresh data from any provider.
  • Continuously analyze LinkedIn data for strategic insights as business needs evolve.

Interested to see LinkedIn data in action for yourself? Get in touch here and I‘ll happily showcase sample datasets, use cases and our platform.

I‘m always glad to help data-driven professionals like you extract more value from LinkedIn. Feel free to reach out with any questions!

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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