What is data collection: methods and how to do it better

What is web data collection?

Data collection is the process of gathering information from various online and offline resources for business decision-making, strategic planning, research, etc. Let's say, you want to collect competitor prices and customer reviews on social media to modify marketing approaches. These steps are part of data collection and are crucial for a successful business since gathering reliable and accurate data can lead to actionable insights that improve business strategies. By ensuring correct and appropriate data collection, business professionals can make better-informed decisions.

Alt: Data collection

Data collection methods

From a broader perspective, data collection methods include surveys and forms, interviews, observation, and online tracking such as tracking cookies that store personal information and behavioral data (e.g. browsing habits).

Surveys: Surveys are designed with a fixed set of questionnaires to collect data/responses from a targeted group of individuals. They are often with pre-defined response options (e.g., multiple-choice Likert scales), which makes it easier to quantify responses and analyze the gathered data statistically. This data collection method is one of the most popular approaches to gathering data and involves a series of steps, such as designing survey questions, selecting appropriate methodologies (whether online or in-person), distributing them, collecting responses, and analyzing data to derive insights. If they target a large sample, time and cost can be significant factors that impact the efficiency and feasibility of data collection.
Interviews: Interviews can be useful when collecting in-depth, nuanced, and contextual data. They can be structured (with predefined questions), semi-structured (with a mix of predefined and open-ended questions), or unstructured (more conversational), which adds more flexibility to data collection. In addition, interview data can be obtained through in-depth communications and interactions with respondents so they can provide a deep dive into the respondents' thoughts and opinions on specific topics.
Observation: Observation is also considered significant in data collection because this method is often used to gather data about complex phenomena that are difficult to quantify or capture through other methods. In observation, the researcher (a.k.a. the observer or data collector) becomes part of the group or setting being studied, engaging in the activities and interactions while observing. It provides real-time data on how people behave, interact, or perform specific tasks in natural settings, offering a direct insight into phenomena as they occur. It presents some challenges, such as potential bias from the researcher or observer, but it still can be effective in both qualitative and quantitative data collection.
Online tracking: Online data collection, primarily represented as online tracking, is widely used to collect data that can be captured online, such as browsing data, clicks, time spent on pages, and other online user interactions. You may have heard of cookies or cookies consent while navigating websites. Cookies refer to small files of information that a web server generates and sends to a web browser, and they can track user activities (e.g., login details and browsing patterns) across websites. This type of data collection method is very powerful in collecting data and uncovering valuable insights with ease. However, it also presents challenges related to privacy and data accuracy, which must be managed carefully to comply with legal requirements.

How to do better: Automated data extraction

There are various ways of collecting data, but what makes automated data extraction special is using software programs and algorithms to automatically retrieve data from a variety of web sources, such as websites and databases, without human intervention. It is designed to streamline the process of gathering large volumes of data efficiently and accurately, so it can dramatically reduce the time spent on data collection using traditional methods. Data extraction can be automated in the following ways:

Pre-built web scrapers: Ready-to-use web scraping platforms (e.g., Listly, Import.io, Apify, Octoparse, etc.) feature pre-built templates and accessibility for individuals who may not have extensive programming knowledge with their user-friendly point-and-click interfaces. These scraping services often offer features like automated scheduling and execution of scraping tasks.
APIs: Application Programming Interfaces are provided by web automation services that pull data from websites through API calls, facilitating seamless integration into other software. Since they allow users to access data through the pre-defined API's endpoints, they are less flexible; however, web scraping APIs have strengths in that they require less maintenance as they are managed by the service provider.
Web scraping tools: Software programs navigate and extract data directly from websites by parsing various HTML formats and structures (e.g. BeautifulSoup and Selenium). Although they may require more manual steps and programming skills (e.g. dealing with JavaScript-rendered content, solving CAPTCHAs, etc.), they are highly customizable so users can deal with various website structures.

Pros of automated data extraction

Speed and efficiency: Automated tools can extract massive volumes of data more quickly than manual methods, saving time and effort.
Accuracy: Automated data extraction can reduce human errors and ensure consistent data collection.
Scalability: Automation allows for handling large amounts of data from multiple sources and managing data extraction processes at scale.
Maintenance: Automated data extraction can be set up on a schedule, providing users with up-to-date data and allowing for continuous monitoring of changes.

Cons of automated data extraction

Technical challenges: Websites often use measures such as CAPTCHAs to prevent data scraping conducted improperly, and there may be technical complexities in implementing automated tools for data extraction.
Legal and ethical issues: Automated data extraction can sometimes violate website terms of service or copyright laws, leading to legal or ethical concerns.