4 Web Scraping Methods to Extract Data to Excel

How to Do a Background Check: Scraping Data From Websites Into Excel

In today‘s digital age, a wealth of personal information is available online. From social media profiles to public records databases, the web offers a trove of data that can be invaluable when conducting a background check. Whether you‘re an employer screening job candidates, a landlord vetting potential tenants, or an individual looking into someone‘s history, being able to efficiently gather and analyze online data is a powerful skill.

One of the most effective ways to collect data for a background check is by scraping it from websites and exporting it into a spreadsheet program like Microsoft Excel. Web scraping refers to the process of using automated tools to extract information from web pages. When combined with Excel‘s data processing and analysis capabilities, web scraping provides a robust method for conducting comprehensive background research.

In this guide, we‘ll take an in-depth look at how to scrape data from websites into Excel to help you perform more effective and efficient background checks. We‘ll cover the ethics and legality of web scraping, compare different data extraction methods, and show you how to parse and clean web data using Excel‘s features. Finally, we‘ll discuss some strategies for using scraped data to identify red flags and risk factors when screening an individual‘s background.

Ethical and Legal Considerations
Before diving into the technical aspects of web scraping for background checks, it‘s critical to understand the ethical and legal implications. Just because information is publicly available online doesn‘t necessarily mean it‘s fair game to collect and use for any purpose.

There are important privacy concerns to consider, especially when it comes to sensitive personal information. Even if data is technically public, scraping and utilizing it may be seen as invasive or a violation of someone‘s privacy. Some key best practices to keep in mind:

  • Only collect data from websites where the information is clearly intended to be public (e.g. LinkedIn profiles or public records databases), not private sites like social media pages with restricted settings
  • Use collected data only for legitimate business purposes that have been clearly disclosed, not for unethical or discriminatory purposes
  • Ensure data is stored securely and disposed of properly when no longer needed
  • Give data subjects a way to access information collected about them and to request corrections or removal

In addition to the ethics, there are also important legal regulations around background checks and the use of personal data that may apply:

  • The Fair Credit Reporting Act (FCRA) requires background screeners to follow certain guidelines around consent, disclosure, and disputing of information in the U.S.
  • The EU‘s General Data Protection Regulation (GDPR) has strict rules surrounding the collection and use of personal data originating from the EU
  • Various U.S. state and local laws may impose additional requirements on background checks

It‘s essential to have a clear understanding of any relevant laws and to ensure your web scraping practices for background checks are fully compliant. When in doubt, consult with a legal professional.

Manual Data Extraction Methods
The simplest way to get website data into Excel is manually. This involves visiting web pages, highlighting and copying the desired information, and pasting it into a spreadsheet. While tedious, manual methods can be an easy starting point for gathering small amounts of data.

One manual method is to simply use your web browser‘s built-in tools to select and copy information from a page. You can highlight text, right-click and select "Copy", then switch over to Excel and paste the data. For more control, you can right-click or Ctrl-click on a web page and choose "Inspect" or "Inspect Element" to open your browser‘s developer tools. This allows you to drill into a page‘s underlying HTML elements to copy more specific data.

Some websites may offer export options that let you download data directly into a CSV or Excel file. For example, a public records database might provide an "Export Results" button to download your search queries. This can save significant time over copying and pasting.

The main advantage of manual data extraction is that it requires no special technical skills. Pretty much anyone can figure out how to highlight, copy and paste information. Manual methods are fine for one-off extraction of small data sets.

However, manual scrapers suffer from major limitations in terms of scale and efficiency. Copying and pasting gets unruly fast when you need to pull information from multiple pages and websites. It‘s also prone to human error. For background checks of any volume or complexity, manual methods simply aren‘t sustainable.

Scraping With Excel‘s Built-in Features
Did you know that Excel actually has web scraping functionality built right in? While Excel is mainly known as a spreadsheet and data analysis tool, it also offers some surprisingly powerful features for automatically extracting data from web pages. Let‘s take a look at a couple of these:

Excel‘s Get Data from Web
Excel makes it easy to extract data from web pages with its "Get Data from Web" feature, found under the "Data" tab in newer versions of Excel. Simply click on this option and type or paste in the URL of the web page you want to scrape. Excel will download the HTML data from the page and bring up a navigator window showing all the page elements it recognizes as data tables.

You can select the specific tables you want, then choose "Load" to import the data into a new Excel worksheet. Excel does its best to normalize the data into clean rows and columns. In many cases, you can go from a data-rich web page to a neatly structured spreadsheet in just a few clicks, no manual copying and pasting needed!

This method tends to work best for fairly simple, static web pages that use proper HTML table formatting. More complex or dynamically generated websites can cause Excel to improperly parse the data. Still, Get Data from Web is a quick and easy scraping option to try before resorting to more advanced methods, especially for one-off extractions.

Web Queries with Power Query
For scraping jobs that require retrieving data from multiple web pages, or pages requiring user interactions like clicking buttons or filling out forms, Excel‘s Power Query feature is a great choice. Power Query provides a code-free, visual interface for building queries that can navigate websites, select data elements, and transform the results into nicely structured tables.

With Power Query, you can set up a query by example, recording actions like clicking on links and extracting specific page elements. You can then refine and expand the query using Power Query‘s graphical editor, adding steps to further filter, reshape and clean the resulting data. Once you have a query returning the desired data, you can load it into an Excel workbook, and even refresh the query on a schedule to continuously update the data from the latest web pages.

The main advantage of Power Query over Get Data from Web is the ability to scrape and combine data from multiple web pages in an automated fashion. You can set up queries to navigate through search results or crawl a series of URLs. Power Query is also great for cleaning and transforming scraped web data using its wide array of built-in reshaping and parsing functions.

The downside of Power Query is a steeper learning curve than the point-and-click simplicity of Get Data from Web. Building web queries does require some understanding of how websites are structured and how to target elements with CSS selectors or XPaths. Still, for moderately complex scraping jobs, investing some time to learn Power Query can really pay off.

Dedicated Web Scraping Tools
For serious web scraping needs, you‘ll likely want to reach for tools and services built specifically for the job. While Excel can handle some light extraction tasks, dedicated web scraping software offers much more power and flexibility for large-scale jobs involving multiple complex websites.

There are numerous web scraping tools on the market, but they all share the same basic functions. To use them, you typically specify the target webpage URLs along with rules for navigating links and parsing data from the page. The scraper then automates the process of visiting pages, extracting information, and compiling it into a structured format like CSV or JSON.

Some popular web scraping tools include:

  • Octoparse: Point-and-click desktop app for building scrapers with a visual workflow designer
  • ParseHub: Another desktop scraping app with a visual selector for targeting page elements
  • Scrapy: Open-source Python framework for building web spiders that crawl and scrape
  • Mozenda: Cloud-based scraping service handling large volumes of pages

The exact choice of tool depends on your specific scraping requirements, technical skills, and budget. For non-programmers, graphical tools like Octoparse and ParseHub are a good place to start. Developers comfortable with Python should check out Scrapy for its extensibility. For high-volume scraping without managing any infrastructure, cloud platforms like Mozenda are worth a look.

Compared with Excel‘s built-in web query features, dedicated scraping tools offer several key advantages:

  • Ability to scrape data from even the most complex, dynamic web pages
  • Higher degree of automation via scheduling, change detection, and APIs
  • Better performance and scalability for scraping large numbers of pages
  • Wider range of export formats beyond Excel (JSON, databases, etc.)

That said, dedicated web scraping tools also have some tradeoffs to consider. They tend to have higher learning curves and may require a level of coding skill. Some are expensive or charge by the page. And critically, they don‘t offer the kind of integrated data processing and analysis capabilities that Excel specializes in.

For conducting background checks, a common workflow is to use a web scraping tool to automate the retrieval of raw web data, then load the results into Excel for searching, filtering, and analysis. The scraping tool handles the heavy lifting of spidering target sites and extracting data, while Excel provides the interface for combing through the data and uncovering insights.

Preparing Scraped Data in Excel
However you scrape the data from the Web, some additional work in Excel is usually required to get it into a clean, analysis-ready state. Raw HTML data can often come in messy, with inconsistent formatting, missing values, and extraneous content.

Excel offers several useful tools for whipping scraped data into shape:

Formulas: Excel‘s spreadsheet formulas are indispensable for parsing and manipulating text data. Common functions like LEFT, RIGHT, MID, and FIND make it easy to extract values based on delimiters or character positions. For more advanced parsing, you can combine formulas with regular expressions using the FILTERXML function.

Flash Fill: One of Excel‘s niftiest features, Flash Fill detects patterns in your data and automatically fills in matching values. For example, if you have a column of full names and manually split the first couple into separate first and last names, Flash Fill can detect the pattern and populate the remaining rows for you.

Text to Columns: For splitting delimited text data into separate columns, the Text to Columns feature lets you specify a delimiter character (e.g. comma) and automatically parses the data into multiple columns.

Remove Duplicates: Background check data scraped from the Web often contains redundant records that need to be deduplicated. The Remove Duplicates feature under the Data tab identifies and removes duplicate rows based on key columns of your choosing.

Pivot Tables: Pivot tables are a powerful tool for slicing and filtering data along different dimensions. For example, you could summarize a list of criminal records by offense type or location. Pivot tables are especially handy for spotting patterns and outliers in large background data sets.

By leveraging these and other Excel features, you can take the raw data produced by a web scraper and refine it into an accurate, standardized, and de-duplicated data set ready for analysis.

Conducting Background Analysis
With your background data scraped, cleaned and loaded into Excel, it‘s time to sleuth out any potential red flags or risk factors. The exact analysis process depends on the purpose of the background check and the specific data points collected. But here are a few common plays:

Identity Verification: For background checks that involve identity verification (e.g. for fraud prevention), you‘ll want to cross-reference data points like name, address, phone number, and email across multiple sources to check for consistency. Excel‘s VLOOKUP and MATCH functions are useful for finding matches across different data tables.

Criminal Records: If you‘ve scraped criminal history data, you can use Excel filters and pivot tables to quickly zero in on felony convictions, violent crimes, or other serious offenses that might disqualify a candidate. Be sure to comply with FCRA requirements around reporting criminal records.

Employment and Education: For employment and tenant screening, you‘ll want to verify key resume claims like job titles, employment dates, and degrees earned against data scraped from sites like LinkedIn, company websites, and school registries. Again, Excel lookups make it easy to cross-reference records.

Social Media: An individual‘s social media presence can provide valuable context for a background check. Look for any questionable content, affiliations or behavior that might raise concerns. Excel‘s text filter capabilities can help you quickly scan posts for keywords across platforms.

Online Reputation: Has the individual been involved in any lawsuits, controversies or scandals reported in the news or discussed in online forums? Excel can help you compile references and analyze sentiment to gauge someone‘s overall online reputation.

Of course, all of these plays require a careful adherence to the aforementioned best practices around data ethics and consent. Background checks shouldn‘t be fishing expeditions that indiscriminately vacuum up any personal data findable online.

But when focused on specific, legitimate screening requirements and backed by a solid data set compiled through web scraping, Excel can be a powerful tool for uncovering valuable background insights.

Conclusion
Web scraping and Excel are a dynamic duo for background checks in the digital age. By using automated tools to extract online data at scale and then leveraging Excel to process and analyze that data, it‘s possible to build richly detailed profiles that paint a comprehensive picture of an individual‘s background.

We‘ve covered several methods for scraping data into Excel, from manual cut-and-paste to dedicated scraping tools. Which approach makes sense depends on the complexity of the target websites and the volume of data required. But as a general rule, the more you can automate with scraping software, the more time you‘ll have for high-value analysis work in Excel.

We‘ve also explored some of the key plays for analyzing background data in Excel, like identity verification, criminal history checks, and social media scans. But it‘s important to remember that these are starting points, not an exhaustive playbook. Effective background analysis requires critical thinking and judgment to piece together clues and know when to dig deeper.

Ultimately, any background check is only as good as the data that goes into it. And that‘s where web scraping really shines, by providing access to a wealth of public data that can take screening to a whole new level.

Just be sure to handle that data ethically and legally by collecting only what‘s needed, ensuring accuracy, and complying with regulations. Excel can crunch the numbers, but it‘s on the human analyst not to misuse its power.

When done right, web scraping and Excel turn the Internet into a treasure trove of background intel while respecting privacy and due process. And that adds up to smarter screening decisions and a safer world for all.