The Modern Landscape of Website Data Collection in 2024

Gulbahar Karatas

Web Scraping

Updated on Jan 2

5 min read

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The Modern Landscape of Website Data Collection in 2024

Table of contents

What is website data collection?Difference between data collection, data mining, and data analysis Web data collection methods Best Practices for responsible data collection The Future of website data collection Further reading

Today websites are not just information hubs, they have become data-rich environments that enable businesses to reveal patterns about user interactions. However, evolving user expectations of privacy and shifting regulatory sands add layers of complexity to data collection. This means that businesses not only have to invest in data collection tools to gather data but also ensure that their data collection procedures are compliant with global regulations such as the European Union’s General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act (CCPA).

This article delves deep into the modern landscape of website data collection, exploring innovative methodologies, and the ethical dilemma.

What is website data collection?

Website data collection refers to the process of extracting publicly available data from public web sources such as review platforms, social media platforms, and eCommerce sites. This data can be gathered directly and indirectly for a variety of purposes, including personalization, market research, and UX improvement.

However, many people believe that collecting publicly available information from the internet is legally permissible. It is a held belief. You can technically collect any publicly available information on the internet, but there are still some ethical and legal implications to consider.

Difference between data collection, data mining, and data analysis

Data collection, mining, and analysis denote different stages in the data lifecycle. Data collection is the process of gathering information from web sources manually or automatically.

Data mining, also known as knowledge discovery in data (KDD), is a computational process of discovering patterns in large datasets. Data analysis is the process of inspecting, cleaning, transforming, and interpreting data to reach conclusions.

The following table provides a clearer distinction among the three terms by comparing their definitions, techniques, purposes, and examples.

Criteria	Data collection	Data mining	Data analysis
Definition	Process of extraction data from websites	Process of discovering patterns in large datasets	Process of inspecting, and interpreting data to discover meaningful insights
Key techniques	Web scraping Online tracking Surveys Search trends	Data cleaning & preparation Tracking patterns Classification Clustering Neural networks	Data visualization Hypothesis testing Descriptive Statistics
Purpose	Transform unstructured web content into structured data that can be analyzed	Create predictive models	Provide a clearer understanding of the available data
Example	Gathering product review data from review platforms like G2	Assessing the risk of lending to individual customers based on their credit history, and transaction records	Analyzing sales data to understand market trends and customer preferences

Web data collection methods

Data is collected through various methods, each data collection method designed to cater to specific applications. Mainly, data is gathered using the following techniques:

1. Web scraping

Off-the-shelf web scraping tools (including low/no code web scrapers): Pre-built web scrapers allow users to collect data from websites and convert web pages into structured data without the need for extensive programming knowledge. These data collection tools provide features like visual point-and-click, where users can point and click on elements they intend to scrape. The main advantage of no-code web scrapers is that they are suitable for non-technical users. They can be scaled based on the user’s specific needs. Compared to in-house web scrapers, outsourcing might lead to ongoing fees in the long run.

In-house web scraper: In-house web scrapers are developed internally by an organization using web scraping libraries. One of the main advantages is the ability to customize self-built web scrapers based on the organization’s particular scraping needs and business requirements. In-house web crawlers allow you to have tighter control over data security. In the long term building your web scraper can be cheaper than using a pre-built web scraping tool. However, developing a web scraper can be time-consuming, requiring development, testing, and optimization.
Web scraping APIs: Web scraping APIs (Application Programming Interfaces) allow users to access and collect data from web pages. Instead of writing a scraping script, you can use the scraping API’s pre-written algorithms to navigate and extract data. Utilizing APIs for gathering information requires some programming know-how.
Ready-to-use datasets: Pre-collected datasets allow businesses to bypass the time-consuming process of data collection, cleaning, and pre-processing. Collecting data, especially large-scale data, can be expensive compared to using off-the-shelf data collection tools. However, such datasets might not fit specific requirements or niche use cases. Pre-collected datasets might be suitable for those who prioritize data privacy and want to bypass the time-consuming data collection phase.

Bright Data is one of the leading companies in the data collection industry, providing various web scraping and data collection services designed for different purposes. Web Scraper IDE provides ready-made JavaScript functions, enabling businesses and individuals scrape data from any website while avoiding CAPTCHAs and IP blocks.

2. Tracking online behavior

Online tracking technologies monitor and record the actions of website visitors as they interact with applications and digital ads. Online tracking involves different methods such as cookies, web beacons, fingerprinting, localStorage, and SessionStorage.

For instance, when a user fills out a form, leaves a comment, or signs up for a newsletter, they leave a trail of data points such as time spent on pages, exit pages, pages visited, and products viewed. Online tracking technologies allow website owners to deliver tailored content, and detect unauthorized access attempts by analyzing user behavior.

3. Qualitative data collection

Qualitative data collection is the process of gathering non-numerical data through online surveys, interviews, and observations to understand intangible aspects of individuals, such as behaviors and motivations. However, collecting qualitative data can be time-consuming and might not be generalizable to broader groups.

Best Practices for responsible data collection

This section highlights some best practices to consider while gathering web data.

Limit data collection: Instead of bombarding the target website with rapid requests, space out your connection requests. This best practice ensures that the website remains responsive for other visitors.
Read the terms of use of the scraped website: Before conducting any scraping activity, review the target website’s terms of use or terms of service. Many websites explicitly mention their stance on automated data extraction in these documents.
Follow robots.txt: Robots.txt file indicates which parts of the target website should not be accessed by web scrapers.
Do not collect personally identifiable information (PII): Includes information such as names, phone numbers and addresses. Unauthorized collection of such data can pose both ethical and legal risks. Make sure that you anonymize or delete the collected data promptly and have robust measures to protect the data.
Anonymize data: Do not expose the scraped data to the public. Mask the data to minimize exposure at processing.
Use website’s API: Utilize website’s API rather than a web crawler when available. It is a more responsible way to access data compared to scraping the website directly.

The Future of website data collection

Technological advancements and regulatory landscapes shape the future of website data collection. With concerns growing over data privacy, users might need to be more transparent about the data they scrape. Major web browsers like Chrome and Safari have started to restrict phase out support for third-party cookies.¹ This makes it harder for businesses to track users across the internet and collect third party data. Businesses might shift their focus towards gathering first party data, seeing the importance of leveraging first-party data.

This makes it harder for businesses to track users across the internet and collect third party data. Businesses might shift their focus towards gathering first party data, seeing the importance of leveraging first-party data.

Advanced algorithms such as AI and ML can anticipate the most valuable data and adjust collection strategies in real time. For example, AI-based data collection tools such as adaptive web scraping adjust themselves to changes that are implied by a website, even minor design changes rather than just its structure.