Often times data acquisition is time consuming and financial costly during research. With the help of automation, data scraping can help researches eliminate these constraints.

Challenges in Research for Data Collection

During the research process, data collection is unavoidable and can be very tedious. Researchers would need a high volume and randomized data in order to conduct good research projects.

Here are the types of data collection that may occur during research processes:

Manual Data Collection

Manual collection is often one of the method that researchers would use for data collection. Although this allows researcher to be fully in control with the process, it is very prone to human error and requires the most time to complete. Another issue with manual data collection is the higher amount of effort in order to randomise data. When the data base volume gets bigger, it will be very taxing to organise and maintain these data.

Company Data Collection

There are times where collecting data from business is required. Especially with business working closely with research institutions such as universities, business sometimes would provide tools, data or grants to help them conduct their researches. However, researchers might find that these datasets could be biased and have little significance for their research.

Public Research Data Collection

Datasets can be purchased by universities to give access to their researchers. In these cases, some data from government could be accessed, these data and information could be released yearly for the public to view. Although these data can be quite useful in research, it is difficult for an individual researcher to gain data access from government institutions and departments. 

These are some of the viable options when it comes to different researches, such as medical, academic or market research. However, they usually will cost researchers a lot of time, financial resources and labour resources to gain access. All of these above methods might have levels of limitations when it comes to errors, information biases and accessibility. 

Nowadays, some researchers are turning to Web Scraping as an additional option to gather data, and they are able to optimise data collection with Web Scraping automation.

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What is Web Scraping?

Web scraping is the automation of structured web data extraction. This tool can be used by business to collect high volume of data for decision making, as well as researches collecting high volume of data for research projects.

By using web scraping, you are able to automatically collect publicly available web data at a large scale. Through Cognitive Automation, web scraping is able to collect millions of data from the inter web.

However Web crawling and Web scraping are not the same, it is important to know the different roles that these tools serve.

Differences Between Web Crawler & Web Scraper

Web crawler is also know as “spider”. This is a type of AI that will browse and follow links. Often time we use this tool to target specific URLs for content before moving to the data scraping process.

Web scraper is a tool that can accurately extract data from web pages quickly. Data selectors are a vital component for data scraper, these can be used to extract data from HTML files.

How Web Scraping Automation Helps to Transform Research

Whether it is for market research or academic research, web scraping can be useful for online research. Web scraping automation allows researchers gather a large amount of datasets in shorter amount of time.

We can customize the process of collecting data according to the needs of the research subject and field. This eliminates the limitation of data errors, biases and accessibility that is possible to happen usually in the collection methods above. Researchers are free to access data that can be updated with any desired information at any time. This not only saves time and cost for data collection, researchers are able to conduct interesting and unique researches without the previous limitations.

Above video is an example of how web scraping can be useful for market research. Not only that robot can collect data automatically from the targeted e-commerce platform, but it can also analyze customers’ reviews with Natural Language Processing (NLP). This automation can help businesses understand the feedback of their products in a timely manner. 

For more information on how AI can help business with Intelligent Automation, please visit Gleematic AI for your automation needs.

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Written By: Reiko Anjani