Data comes in various types and ensures access to information which can be in raw or unorganized form. The global spending on big data analytics exceeded US$ 180 billion in 2019, indicating a huge investment by companies worldwide in using data for insights and decision-making. 

One of the ways organizations get data is through data scraping. Data scraping assists in gathering large volumes of information. It is like an ultra-fast robot flipping through different areas in search of data or information regarding a subject. 

Serving legitimate purposes, data scraping can aid market research. However, in the wrong hands, it can become a dangerous tool, causing data breaches and other malicious activities. 

What is Data Scraping?

Data scraping is the extraction of data by a computer program from various sources such as the internet or output generated from another program. There exists web scraping and screen scraping but data scraping is a broad term that encompasses the two.

A study published by the International Journal of Innovative Research in Technology, reveals how Amazon gained its position as industry leader by “using web scraping and continued price monitoring” to have competitive pricing.

In whatever industry or sector data scraping is important, here is why;

1. Market Research: Organizations analyze data scraped from sources like websites and social media platforms to make decisions about product development, pricing strategies, and marketing campaigns.

1. Lead Generation: Scraping data from online directories, social media profiles, and other sources can help organizations build targeted lists of potential customers and reach out to them.

1. Competitive Analysis: Data scraping allows organizations to monitor competitors' activities, such as pricing changes and product launches, informing companies about industry trends and benchmark performance against competitors.

Steps Involved in Data Scraping

1. Identification: the data source is identified, accessed, and parsed. 

1. Extraction: specific data is selected and extracted using selectors like CSS selectors or XPath. 

1. Cleaning: the extracted data may contain unwanted elements or formatting issues which need to be cleaned and transformed. 

1. Storage: the cleaned data can be stored in a database, spreadsheet, or any other suitable storage medium.

Risks and Impact of Data Scraping

1. Legal and Ethical Risks: Data scraping may infringe copyright laws or violate terms of service agreements of websites. When done without proper authorization, data scraping can have legal repercussions. In 2023, Elon Musk, CEO of X (formerly known as twitter) restricted the number of posts both verified and non verified accounts can read. He also filed a data scraping lawsuit against some defendants (names unknown)

1. Data Accuracy and Quality: Scraped data may not always be accurate or up-to-date. Inaccurate data can result in flawed business strategies and financial losses for organizations relying on such information.

1. Security Vulnerabilities: Data scraping can expose organizations to security risks, especially if the scraping process involves interacting with web applications or APIs. Malicious actors may exploit vulnerabilities in scraping tools or scripts to gain unauthorized access to sensitive information, compromise systems, or launch cyber attacks. A developer employed by an affiliate marketer scraped customer data from the Chinese shopping site Taobao using custom crawler software. The duo was slammed with a 3 year sentence.

1. Reputation Damage: If an organization is found to be engaging in unethical or illegal scraping practices, it can damage its reputation and erode customer trust. Consumers are concerned about data privacy and may boycott companies that engage in unauthorized data scraping or fail to protect their personal information adequately.

1. Competitive Disadvantage: Organizations that rely heavily on scraped data may face a competitive disadvantage if their competitors have access to more accurate data sources.

Best Practices for Data Scraping

While data scraping offers opportunities for organizations to gather data it is necessary to do it the right way to avoid any hitch, here are ways to go about it;

1. Legal and Ethical Issues: Ensure you go through policies before scraping data. Check the robots.txt file of the website and follow any directives to avoid scraping restricted areas.

1. Data Privacy: Personal data must be handled according to data privacy regulations. These requirements can be subject to industry or region, be sure to ascertain what applies to you.  

1. Limit Requests: Implement rate limiting to control the frequency of requests made to a website, preventing server overload and potential IP blocking.

1. Be Transparent: Provide clear attribution and acknowledgments when scraping data for research or analysis purposes, being transparent about your intentions and usage of the scraped data.

Looking to learn more on data scraping? Contact us at Cyberkach. We provide expert guidance and interactive learning.