Biographical data is a goldmine of valuable insights about people and their professional backgrounds. It includes key information like:
- Name and contact details
- Job titles and employers
- Education and qualifications
- Skills and areas of expertise
- Career history and achievements
- Social and web profiles
This data powers a wide range of business and research applications, from talent sourcing and lead generation to due diligence and people analytics.
But biographical data is often scattered across many different web pages and sources, making it time-consuming to gather manually. That‘s where web scraping comes in – automatically collecting and structuring data from websites at scale.
In this in-depth guide, we‘ll explore how to scrape biographical data from the web, best practices to follow, and tools that can help. Whether you‘re working with a single site or thousands, these techniques will enable you to build large, rich datasets of people and their bios.
The Value of Biographical Data
First, let‘s look at some eye-opening statistics that show the importance of biographical data in various domains:
Recruitment: 98% of Fortune 500 companies use applicant tracking systems to screen candidates based on their biographical details. (source)
Sales: Marketers with data-driven personas incorporating biographical attributes achieve up to 56% higher sales than those without. (source)
Risk Management: Including biographical data in predictive models can reduce fraud detection false positives by 20-30%.
People Analytics: By 2025, 60% of organizations will use biographical data to analyze skills gaps, career paths and performance.
Clearly, biographical data offers significant value and a competitive advantage to organizations that can harness it effectively. Web scraping makes that possible by allowing you to:
- Gather biographical data at massive scale across many websites
- Structure messy web data into a consistent, normalized format
- Enrich and combine data points to get a 360-degree view of people
- Keep biographical datasets accurate and up-to-date over time
How to Scrape Biographical Data
Now let‘s get into the nitty-gritty of actually scraping biographical data from websites. We‘ll use the example of scraping lawyer profiles from law firm websites, but the same process applies to any type of biographical pages.
Step 1: Identify Target Pages
The first step is to determine which pages you want to scrape biographical data from. These could be:
- Individual profile pages for each person
- Directory or search result pages listing many people
- Resume, CV or portfolio websites
- Social media and professional networking profiles (e.g. LinkedIn)
For our law firm example, let‘s say we want to scrape all the lawyer bio pages from the website https://www.morganlewis.com. We‘d start by looking at the "Our People" directory to get a list of URLs for each individual lawyer page.
Step 2: Analyze Page Structure
Next, we need to analyze the HTML structure of the biographical pages to determine how to extract the desired data points. Using the browser developer tools, we can inspect a sample lawyer page and find the elements containing fields like name, job title, contact details, education and so on.
For example, here‘s the HTML for the page header with key biographical details:
<div class="bio-page-header">
<p class="current-title">Partner</p>
<p class="phone">+1.212.309.6000</p>
<p class="mail"><a href="mailto:john.smith@morganlewis.com">john.smith@morganlewis.com</a></p>
<p class="office">New York</p>
</div>And further down the page, we can see sections for Education, Bar Admissions, and Practice Areas:
<section id="bio-education">
<h2>Education</h2>
<ul>
<li>University of Michigan, J.D., 2010</li>
<li>Dartmouth College, B.A., 2007</li>
</ul>
</section>
<section id="bio-bar-admissions">
<h2>Bar Admissions</h2>
<ul>
<li>New York</li>
</ul>
</section>
<section id="bio-practices">
<h2>Practices</h2>
<ul>
<li><a href="/practices/litigation-disputes-and-investigations">Litigation, Disputes and Investigations</a></li>
<li><a href="/practices/intellectual-property">Intellectual Property</a></li>
</ul>
</section>By identifying the relevant HTML elements for each data point, we can tell our scraper what to look for and extract.
Step 3: Write Scraper Code
With the target pages and data structures in mind, we‘re ready to code our biographical data scraper. While you can build scrapers from scratch using libraries like Python‘s BeautifulSoup or Scrapy, let‘s use the no-code Octoparse tool to keep things simple.
Here‘s a high-level overview of the steps:
- Create a new task and set the start URLs to the lawyer directory page(s)
- Add a loop to click through to each individual lawyer page
- On the lawyer pages, add data fields to be extracted:
- Name (H1 text)
- Job Title (P text under image)
- Phone (P text containing phone number)
- Email (A @href attribute)
- Location (P text after email)
- Education (LI text in #bio-education section)
- Bar Admissions (LI text in #bio-bar-admissions section)
- Practice Areas (A text in #bio-practices section)
- Run the task and check the extracted data for completeness and accuracy
- Export the scraped biographical data in CSV or JSON format
Here‘s what the Octoparse task configuration looks like:
[octoparse screenshot]After running the scraper on all the lawyer bio pages, we‘ll have a structured dataset that looks something like this:
| Name | Title | Phone | Location | Education | Bar Admissions | Practice Areas | |
|---|---|---|---|---|---|---|---|
| John Smith | Partner | +1.212.309.6000 | john.smith@morganlewis.com | New York | ["University of Michigan, J.D., 2010", "Dartmouth College, B.A., 2007"] | ["New York"] | ["Litigation, Disputes and Investigations", "Intellectual Property"] |
| Jane Doe | Associate | +1.617.341.7700 | jane.doe@morganlewis.com | Boston | ["Harvard Law School, J.D., 2015", "Yale University, B.A., 2012"] | ["Massachusetts"] | ["Labor and Employment", "Employee Benefits"] |
Now we have clean, structured biographical data ready for analysis! We could further enhance this data by connecting it with other sources, running entity extraction on the unstructured text, or building aggregations and filters in a BI tool.
Of course, there are some factors to keep in mind to scrape biographical data responsibly and effectively. Let‘s look at a few key considerations.
Scraping Best Practices & Considerations
Legal Compliance
When scraping any personal data from websites, it‘s critical to ensure you comply with relevant laws and regulations. In particular, be aware of:
- The website‘s terms of service and robots.txt file, which may prohibit scraping
- Copyright restrictions on reusing biographical text and images
- Privacy laws like GDPR and CCPA that govern the collection and use of personal data
- Sector-specific regulations on handling sensitive data (e.g. HIPAA for medical info)
As a best practice, only scrape personal data from public sources, get consent where required, and provide clear notices about your data practices.
Performance & Reliability
Scraping can be hard on web servers if done too aggressively. To avoid overloading sites and getting blocked, follow these tips:
- Add delays (3-5 seconds) between page requests to limit the rate
- Use a rotating proxy service to distribute requests across many IPs
- Handle errors gracefully and retry failed requests with exponential backoff
- Monitor scraper logs and set alerts for any issues or anomalies
It‘s also important to regularly test and update your scrapers to handle any changes to the biographical page structures or layouts.
Data Quality
Raw web data is often messy and inconsistent. To ensure your biographical data is accurate and usable, you‘ll need to:
- Clean and normalize field values (e.g. strip HTML, parse dates, remove extra whitespace)
- Break out complex values into separate fields (e.g. full name into first/last)
- Validate data types and formats (e.g. email pattern, numeric ranges)
- Deduplicate records and resolve conflicts between data sources
Data quality is an ongoing process, so be sure to spot check samples of your biographical data and continuously monitor and improve your cleaning scripts.
Biographical Data Use Cases
So what can you actually do with biographical data once you‘ve scraped it? The possibilities are endless, but here are a few common and emerging applications:
Recruiting & Talent Acquisition
Biographical data helps recruiters and HR teams to:
- Source candidates with specific skills, experience and qualifications
- Build talent pools and identify top prospects to reach out to
- Analyze talent market trends and competitive landscape
- Predict hiring needs and skills gaps based on industry and role
Sales Intelligence & Account-Based Marketing
For B2B sales and marketing, biographical data enables:
- Targeted lead generation and account prioritization
- Buyer persona development and tailored messaging
- Stakeholder mapping and org chart building
- Trigger-based campaigns for job changes and other signals
Fraud Detection & Investigation
Investigators and analysts use biographical data to:
- Verify identities and spot fake profiles
- Surface connections and conflicts of interest between people
- Build risk profiles based on past history and associations
- Provide context for financial transactions and other activities
Research & Due Diligence
Biographical data also supports deep research by:
- Creating searchable databases of experts and influencers
- Tracking career moves and board appointments over time
- Analyzing diversity and representation within organizations
- Surfacing potential reputational and legal risks
As these examples show, biographical data has the power to drive smarter decisions and automate processes across every part of an organization.
Conclusion
Web scraping is a game-changer for collecting biographical data at scale. By automatically gathering key data points from across websites, you can build rich, up-to-date views of people to power your business.
To get started, focus on identifying high-value pages to scrape, analyze the data structures, and then use tools like Octoparse to extract the biographical details you need. Be sure to follow web scraping best practices around legal compliance, data quality, and performance.
Looking ahead, biographical data will only become more important in an increasingly digital world. Advances in AI and entity resolution will unlock powerful new applications, but also raise fresh challenges around privacy and responsible use.
By taking a thoughtful approach to scraping and managing biographical data, you‘ll be well positioned to harness its full potential while mitigating risks. Here are some next steps to dive deeper:
- Talk to your team and stakeholders to prioritize the highest-value biographical data sources and use cases
- Try out Octoparse and other web scraping tools to prototype an MVP biographical data pipeline
- Research relevant compliance requirements and develop a scraping policy for your organization
- Connect with the web scraping community to exchange tips, tools and best practices
I hope this guide has given you a solid foundation to begin your own journey with web scraping and biographical data. Feel free to reach out with any questions!