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is web scraping legal or not is web scraping legal or not
The Greyness of Web Scraping – Legal or Not?

“Web scraping,” in quite literal terms, involves the scraping of data from the web. In the hands of a search

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“Web scraping,” in quite literal terms, involves the scraping of data from the web. In the hands of a search engine, web scraping is the activity that generates search results by assessing millions of websites for information relevant to search queries. On the other side, in the hands of businesses (using scraping tools), the legality of it becomes questionable.

Why, though?

The Computer Fraud and Abuse Act (CFAA) prohibits unauthorized use of computers and information therein – which includes web scraping. However, the scope of this activity remains unclear yet. Recently, the US Supreme Court ruled in favor of Van Buren v. the United States by announcing that accessing permissible data, even though for unauthorized/prohibited use, cannot be said to be a violation of the CFAA.

The “greyness” of the question of the legality of scraped data cannot be clarified without taking a deep look into the ecosystem of web scraping, what it entails, and what makes it legal or illegal.

Is Scraping a Website Legal?

A lot many factors command how legal it is to scrape web data. The ubiquitous nature of web scraping may fall under the ambit of the Trespass to Chattel laws, where unauthorized use of a person’s information could become a legal issue.

Additionally, a multitude of other laws, acts and regulations have been mobilized today to protect consumer privacy and information theft. You may have heard of the General Data Protection Act (GDPA), the Children’s Online Privacy Protection Act (COPPA), and the Health Insurance Portability and Accountability Act (HIPAA) – all of these protection measures have been put in place to prevent unchecked abuse of private consumer data.

However, with the ruling of Van Buren v. the United States, it would seem that web scraping, under certain circumstances, may be alright.

In a Ninth Circuit Court of Appeals ruling for the case of LinkedIn v. hiQ Labs, it was announced that scraping information from public profiles was alright since this activity wasn’t covered under the ambit of CFAA (because the scraped data was available publicly). It did, however, cause LinkedIn to restrict user profiles from being accessed publicly – a login by the viewer is now required.

The requirement of logging into your user account on a website to view the information contained therein brings all your activities thence under the terms and conditions of the website. These terms and conditions may have clauses that deter or prohibit web scraping – if you still engage in extracting data, you may get into a legal mess.

Speaking of which, this is precisely why LinkedIn mandated logins to view user profiles – to restrict web scraping information of its users.

With that said, the grey area still remains wide. So…is web scraping illegal? It largely depends on the kind of data you are trying to scrape and the nature of that data:

Public Data

The data that you encounter on the internet is mostly public data. Unless you are required to log in to your account or agree to the terms of data use or authenticate your identity or credentials to access certain data, it is perfectly legal to scrape.

The only deterrent to web harvesting here would be the measures that these websites put in place to deflect your web scrapers (to protect their information, of course).

Personal Data/Private Data

It is illegal to scrape an individual’s personal information. Personal information could be anything – name, address, financial details, health details, date of birth, any other contact information, etc. Anything that gives away an individual’s personal identity (Personally Identifiable Information, or PII) is a red flag for web scraping. It is a strict no-no.

If you must, though, it is mandatory to seek that individual’s consent first. Additionally, if a legal motivation is a cause behind scraping PII, it must be made known.

Copyright Data

Any data on the internet that is an intellectual property of the publisher is illegal to scrape. If you must use this data, its copyrights notwithstanding, you must credit the source of that information wherever you use it.

Terms of Service

This is a conditional instance of the illegality of web scraping. If a website explicitly restricts data scraping, consider it illegal to do so. Before you go ahead with your scraper bots, it is best to check the terms of use and service thoroughly.

Account Login

Much like LinkedIn has mandated account logins to access its user profiles, a login instance almost always gets your consent on the website’s terms and conditions. These terms and conditions may contain clauses on data scraping. When you still release your scraper bots after logging in, you are risking a ban or even legal action.

How to Legally Scrape Data

To ensure that there are no legal actions taken against you, thoroughly understand the following aspects before you proceed with web scraping:

  • Is the data publicly available?
  • Does it reveal the PII of any individual?
  • Does the website mention any prohibitions regarding scraping?
  • Are there any laws, acts, policies, or regulations that control what information you can scrape and use?

Carefully weighing the answers to all of these questions would help determine the degree of grey your web scraping activity is in.

Wrapping Up

In quintessence, “Is it legal to scrape a website” is not the question. The real question is, “How legal is website scraping?“.

It is best to ensure that web scraping fetches only the data that is publicly available and not protected by any legally actionable clauses. You can also  outsource web scraping to professional agencies like PromptCloud that know what they are doing.

walmart data walmart data
Scrape e-commerce data from Walmart- the world’s largest retail store

Based on this article published by Forbes, Walmart had 20,000 stores in 28 countries as of July 2021. It is

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Based on this article published by Forbes, Walmart had 20,000 stores in 28 countries as of July 2021. It is still the largest retailer in the world, with Amazon following far second at almost half its sales. Established in 1962, while it is not a new company, it has improved its tech efforts, leaving behind a lot of new players in the industry. It is also one of the top companies dabbling with data and enabling data backed decision making in its board rooms.

In 2021, it started building the world’s largest private cloud that could process anywhere in the ballpark of 2.5 petabytes (2500Tb) of data per hour. To further work on this massive data, it has also set up an analytics hub called Data Café at its Bentonville, Arkansas headquarters. At this hub, close to 200 streams of internal and external data, as well as 40 petabytes of transactional data, can be transformed, visualized, or used to create models. Decreasing the time required for crunching data from weeks to minutes has helped the company is spotting trends and enabling quicker decisions making, thus decreasing the turnaround time for applying data effectively.

Walmart and big data

E-Commerce sites and retailers often use internal and external data sources (competitor data) for dynamic pricing management. While this is the default (and often the only) use case for most companies, Walmart uses its data sources to perform multiple activities–

Personalize your online shopping experience

Just like Netflix uses your previous usage data to provide you with a personalized experience and recommendations, Walmart uses your historical data to show products and deals that might be more relevant to you. This helps in customer retention and often larger order sizes.

Improve in-store checkout processes

Those who still prefer to go grocery shopping at physical stores dread unmanned checkout counters and long lines. Walmart is trying to remove such last point bottlenecks by studying previous data and computing how many associates can facilitate efficient billing ‌at any hour of the day.

Supply chain management

Every item reaches customers across a series of steps each involving a different transportation system. Walmart tries to optimize the supply chain by reducing the steps as much as possible and changing truck timings to ensure that they can fill up their entire cargo space. It even studies routes and timings to figure out which route would enable customers to receive their orders at the earliest.

Restocking pharmacies efficiently

It uses internal as well as historical data to create simulations and predict to a high degree of accuracy certain data points. These include–

  • at what time of the day do stores see maximum footfall
  • the busiest days of a month or year
  • which medicines are most in demand

All this information helps in managing staff and medicines efficiently to ensure less time is required for filling every prescription.

Optimize product selections

It uses data from both online and offline sales to have the most optimum selection of brands and products on shelves at its stores and warehouses. It also tries to gauge which of its internal brands are a hit with the customer to increase their availability.

Data points and sources

Discussing use cases are a great way to increase public interest on topics. However, what we need to focus on most are the data points that are being collected and what are the sources for these data streams.

Walmart has a wide presence across international boundaries as well as in the online sphere. This is why it can gather data from multiple sources–

  1. 245 million customers at 10,900 physical stores as well as 10 live websites worldwide every day.
  2. 300,000 mentions and tags across social media websites every day.
  3. 2,00,000 associates with close to 50,000 more are hired every year– all of whom generate internal data and enable Walmart to improve its hiring process and provide better working environments.
  4. Customer data on 145 million US citizens, 60% of which are adults.

This massive data hoard allows Walmart to analyze millions of keywords daily and accordingly bet on keywords to place its advertisements. It is also able to analyze thousands of products– those that it sells and those that it doesn’t. All this has enabled it to increase its sales by having products that the customers want most.

Walmart has gone so far as to analyze local events, weather and social media phenomena and how they impact customer behaviour. For example, suppose a movie is a hit and the lead actor wears a watch which immediately becomes a rage among young adults. Walmart would be able to predict a higher sale as a result of data from social media and would try to stock up on the product.

Converting data challenges for competitions

Every company faces data challenges when working with new datasets or trying to answer new questions using data. In 2014, Walmart needed to find an efficient way to predict sales with a small range of historical data. It held this competition on Kaggle where it shared the sales data for 45 physical stores across multiple departments. Sales on special days and the holiday season were also tagged in the data.

Individuals were provided with more data points corresponding to the location where every store was located. This contained information like weather patterns, unemployment percentages, median wage, cost of fuel, and more. This was a recruitment challenge– thus allowing Walmart to nail two birds with one stone.

Implementation of Data practices from Walmart

In case you plan to scrape product or pricing data from Walmart, you should first decide on the department that you want to target. Getting all the data from all the departments might turn out to be a ginormous task. In case you are operating in a specific geographical location, it would also be wise to scrape data only related to that place. Getting all the data and filtering through it later would be a two-fold waste of time and computational resources.

Scraping data from Walmart can get you places, given the variety of markets and departments that it serves and the number of products on its catalogue. However, you’d go much further if you adopted the “data practices” at Walmart, be it from a data handling perspective or its cloud infra.

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