DataHub LinkedIn is a platform that allows users to extract data from LinkedIn to analyze and gain insights. It is developed by DataHub, a data analytics company, to help businesses make data-driven decisions using LinkedIn data.
How Does DataHub LinkedIn Work?
DataHub LinkedIn uses advanced web scraping and data extraction techniques to pull data from LinkedIn profiles and company pages. Once the data is extracted, it is structured and organized into easy-to-analyze datasets and dashboards.
Users can extract data on LinkedIn members, including profile information, skills, education history, work experience, accomplishments, interests, groups and more. They can also extract data on LinkedIn companies, such as company overviews, employee headcount, follower counts, job postings and other information.
The extracted LinkedIn data can then be analyzed and visualized using DataHub’s built-in analytics and business intelligence tools. Users can create customized dashboards, graphs and reports to gain insights from the data.
Benefits of Using DataHub LinkedIn
Here are some of the key benefits of using DataHub LinkedIn:
- Access to rich, structured LinkedIn data: DataHub provides access to detailed data from LinkedIn profiles and company pages that would be difficult to gather manually.
- Powerful data analytics: Once the LinkedIn data is extracted, DataHub’s analytics tools allow users to analyze trends, metrics and derive insights for data-driven decisions.
- Customizable dashboards: Users can create customized dashboards to visualize LinkedIn data as per their requirements, making it easy to identify patterns.
- Competitive intelligence: The LinkedIn data can be used to research competitors, understand their employee base, skills footprint and other useful insights.
- Lead and talent sourcing: DataHub LinkedIn makes it easier for recruiters and sales teams to identify and source potential leads or talent.
- Market research: Product marketers can analyze audience groups, skills, interests and other variables to get market insights.
What Kind of Data Can Be Extracted From LinkedIn?
Here are some of the key types of data that can be extracted from LinkedIn using DataHub:
Member Profile Data
- Basic information – name, headline, location
- Employment history – companies, titles, durations
- Educational history – schools, degrees, durations
- Skills and endorsements
- Accomplishments and awards
- Interests and hobbies
- Groups and associations
- Profile views and followers
Company Page Data
- Company name and industry
- Followers and employee counts
- Company size and revenue
- Locations and branches
- Job openings and descriptions
- Competitors and partner firms
Advanced Data
- Member skills by company or job title
- Common career transitions and paths
- School and university alumni networks
- Company leadership and organizational charts
- Industry-wise talent pools and skill sets
What Are the Key LinkedIn Data Points That Can Be Extracted?
Here are some of the key LinkedIn data points and metrics that can be extracted using DataHub:
| Category | Data Points |
|---|---|
| Profile Information | Name, headline, location, connections, profile photo |
| Employment History | Companies worked at, titles, durations, descriptions |
| Educational History | Degrees, schools, majors, durations |
| Skills | Skills listed, endorsements received |
| Interests | Hobbies, interests listed on profile |
| Accomplishments | Awards, honors, publications, certifications |
| Company Information | Followers, employee count, industry, revenue |
| Job Postings | Title, location, description, responsibilities |
In addition to these, DataHub can also extract derived metrics like skill frequencies, common transition paths, average tenure, competitor analysis and more.
What Are Some Real-World Use Cases of DataHub LinkedIn?
Here are some examples of how businesses can use LinkedIn data from DataHub in real-world scenarios:
Recruitment & Talent Sourcing
- Map industry and function-specific talent pools
- Source potential candidates based on skills, titles and experience
- Identify employee skill gaps to define hiring priorities
- Analyze hiring trends and competitor moves
Market & Competitive Intelligence
- Understand skill sets and footprints of competitors
- Analyze employee churn rates in the industry
- Identify companies with the most followers and talent mindshare
- Monitor brand awareness and perception
Sales Prospecting
- Identify and source key decision makers
- Discover connections to target prospects
- Personalize outreach using prospect company or role data
- Prioritize accounts based on company size, industry etc.
Partnerships & Business Development
- Discover companies with complementary capabilities
- Identify influencers to amplify partnerships
- Monitor partnership opportunities and news
- Research potential partners’ capabilities and footprints
Market Research & Brand Positioning
- Analyze audience groups and buyer personas
- Benchmark brand presence versus competitors
- Identify key influencers and brand advocates
- Monitor awareness and perception by segment
What Are Some Limitations of Extracting LinkedIn Data?
While DataHub provides extensive LinkedIn data extraction capabilities, there are some limitations to be aware of:
- Access restrictions – LinkedIn may block or restrict access to data for scraping. DataHub uses sophisticated techniques to minimize this risk.
- Missing or incomplete data – Not all LinkedIn users complete their profiles fully, so some data fields can be empty.
- Fake or spam profiles – A small percentage of LinkedIn profiles may be fake or spammy, with misleading data.
- Data staleness – If LinkedIn data is not extracted frequently, it may become outdated. DataHub allows scheduling recurring extractions.
- Limited historical data – Only current snapshot of LinkedIn data can be extracted, not historical records.
- Privacy restrictions – Data extraction is limited to fields users make public. Private data cannot be scraped.
What Are Best Practices For Extracting and Using LinkedIn Data?
Here are some best practices to keep in mind when extracting and using LinkedIn data:
- Use an extraction tool like DataHub to avoid manual scraping and potential restrictions.
- Extract data frequently, at least weekly, to prevent staleness.
- Filter out any spam or fake profiles by reviewing anomalous data.
- Respect user privacy – only use public profile data users have agreed to share.
- Combine LinkedIn data with internal or external data sources for more robust analysis.
- Have a clear business purpose for extracting and using LinkedIn data.
- Follow LinkedIn’s terms of service and data use policies.
- Store and secure LinkedIn data to prevent unauthorized access or leaks.
- Analyze data in aggregate – avoid targeting specific individuals without consent.
Conclusion
DataHub LinkedIn provides a powerful way to extract insights and intelligence from LinkedIn profiles and company pages. With its advanced data extraction capabilities, businesses can analyze LinkedIn data for a variety of use cases – from recruitment to sales prospecting and competitive intelligence. While care needs to be taken to address potential limitations, following best practices allows organizations to tap into LinkedIn data safely and effectively.
