What is web scraping? Here’s what you need to know about the process of collecting automated data from websites, and its uses

software developer analyzing data on laptop tablet desktop
Web scraping, the process of extracting data en masse from websites, has a variety of practical uses.

  • Web scraping is the process of using automated software, like bots, to extract structured data from websites. 
  • There are many applications for web scraping, including monitoring product retail prices, lead generation, and analyzing sentiment about products and companies on social media. 
  • Here’s a brief overview of web scraping, its applications, and how it works. 
  • Visit Business Insider’s Tech Reference library for more stories.

Web scraping is the name given to the process of extracting structured data from third-party websites. In other words, it’s a way to capture specific information from one or more websites without also copying unwanted or unrelated information. It’s a common practice that has a lot of potential applications and a murky legal profile. 

What to know about web scraping

Web scraping is usually an automated process, but it doesn’t have to be; data can be scraped from websites manually, by humans, though that’s slow and inefficient. More commonly, scraping is performed by software designed specifically for this application, generally in two main components. A crawler is a program that browses the internet and indexes the content of interest, and it passes this information onto the scraper.

The scraper is designed to locate the relevant structured information using markers called data locators. These locators indicate the presence of the data, which the scraper then extracts and stores offline in a spreadsheet or database for processing or analysis.

One simple example of web scraping: Consider a website that aggregates pricing information for retail products so shoppers can see which retailers have the best prices. A scraper can be programmed to index the product pages at every major retailer, with the scraper then visiting each page and using data locators to zero in just on the price field and ignore all the other data on the page – product description, reviews, and so on. The scraper can be run daily to update the webpage with the latest pricing information from around the web. 

How web scraping is used

Because there is an enormous variety of data online, there is a wide variety of applications for web scraping. Here are some of the most common uses:

  • Price intelligence: Like the example above, many web scrapers are designed to monitor prices from retail sites. Retailers might use this to monitor prices at competitor sites, or the data might be used for competitive analysis, monitoring trends, or as a service to other users.
  • Real estate: Similarly, web scrapers commonly target real estate sites to monitor rental and sale prices, appraise property values in a given region, and conduct market analysis.
  • Lead generation: Marketers commonly use web scraping to generate leads by scraping structured data from websites like LinkedIn.
  • Sentiment analysis: Brands even use web scraping to understand how their products and services are being talked about online. Companies can collect data that mentions their name from social media sites like Facebook and Twitter. 

The legality of web scraping

There’s no easy answer to the question of web scraping’s legality. This technology has had a number of legal challenges dating back to 2000, when online auction site eBay filed an injunction (which was granted by the court) against a site called Bidder’s Edge for scraping its auction data

In the years since, there have been a number of additional challenges to web scraping, but in 2017 LinkedIn lost a suit against a business that was scraping its content. With some precedent in the courts both for and against web scraping, it’s currently a common practice across the internet. 

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CEO of data analytics firm Quantexa shares how digital resilience and data-driven decision-making will determine which businesses thrive in a post-Covid world

Quantexa CEO Vishal Marria
Quantexa CEO Vishal Marria

It’s no secret that even before the COVID-19 pandemic, there was a significant missed opportunity in enterprise data and analytics. In fact, at least three-quarters of companies today have limited their use of analytics and fail to capitalize on the operational decision-making opportunity of modern data intelligence. Organizations often struggle to operationalize analytics into the day-to-day business. However, businesses have begun to realize that state-of-the-art decision intelligence requires a blend of machine intelligence with human intelligence to ensure optimal decision-making. Applying graph representations to high performance data sets is fast becoming an imperative to modern decision-making success.

Digital resilience is the new watchword in a post-COVID-19 world

The ability to respond and adapt quickly to new situations has never been more stark than during the pandemic. The crisis has taught organizations that a new level of agility and digital resilience is needed across ecosystems, partners and the supply chain. The focus for any would-be intelligent enterprise should evolve the capabilities created to manage the impact of Covid-19 into productive analytics hubs, capable of using leading indicators to predict and react to future risk with greater frequency, while simultaneously discovering hidden future opportunities. Those that fail to implement an effective enterprise data model enabling the foundation for resilient decision-making by 2021 are forecasted to underperform on profitability by 10% according to IDC.

Data at the core – but how can you trust it?

The volume of data being created is quickly surpassing the rate at which computing and storage systems are being developed. According to IDC, the amount of data available will be enough to fully occupy a stack of tablets measuring 6.6 times the distance between the moon and the earth by the end of this year. The point is, both external and internal data are growing at such a rate, 26% year over year, that ensuring data is available in a meaningful, operationalized way is becoming more important as a core discipline. By definition, the volume, velocity and variety of big data is creating huge operational pressure – called the data-decision gap.

According to KPMG, 56% of CEOs don’t trust the integrity of their data. That said, when the analytical models and technology they use to guide decision-making work with untrustworthy data, they naturally doubt its recommendations. It’s become important to understand the context of your data so you can reveal the unseen and, in some cases, unexpected connections that either create risk or opportunity.

A new generation of intelligent decision-making

The lack of a single, trusted view of data across an organization is a serious obstacle to data-driven decision intelligence. Without this, decisions can’t be automated in an accurate or efficient way, and individual entities such as customers and transactions, cannot be properly and fully understood and analyzed. However, reliable data integration, especially at scale, is difficult, which is why data becomes stuck in multiple silos – inhibiting the connected single view and holistic, contextual analysis that is desired. Traditional rules-based approaches to decision support are not sufficiently agile or resilient in today’s uncertain and rapidly changing business and geopolitical environment – advanced analytics, machine learning, and AI are needed to empower users or automate key processes.

The good news is that new approaches and innovations to data and analytics show a path forward for maximizing the value enterprises can get from their data.

Entity resolution and network generation, surfaced through graph analytics, are key to understanding relationships and behaviors of customers and third parties in the supply chain, resulting in better, faster operational decisions. By integrating the right data, decision makers can become empowered as their new insights come from finding explainable links between fully understood, trusted data in a single view provided by entity resolution.

Machine learning to deliver big, but not without human input

Less than 15% of analytics adopters have made progress with automated decisions. This is a big problem, especially when dealing with large complex data sets. Deployment of fully automated operational decision-making moves analytics from reactive reporting to active, intelligent, and real-time decision-making. As more tasks are automated, the enterprise can focus more on differentiating work.

The key to this is augmentation – combining the best of human and machine intelligence. This allows repeatable routines of work to be fully automated and exceptional cases requiring fine judgement to be dealt with by humans. A great benefit of augmented analytics is that it accelerates the formulation of new data and analytics capabilities which, in effect, can be adapted to the skills, needs and problems of different classes of business user, which extends the reach of analytics across an organization. By maximizing the value of human and machine intelligence, there is a clear path to creating an effective data-driven enterprise.

Organization implications – creating the ability to adapt

To shift to a data-driven enterprise, business leaders need to reimagine how they operationalize the data they consume and analyze. The key to this is gaining a trusted, contextual, connected single view of the vast amounts of data that now exist for better decision-making.

Analytics now drives today’s enterprise, from formation of business strategy to powering operational excellence. Creating a culture of collaboration and getting the best out of humans alongside machines is crucial. Analytics has clearly moved from being an optional extra to serving as the core of decision-making, so creating a data-centric contextual decision intelligence framework has never been more important.

The C-suite and all business leaders need to spearhead a change across the enterprise to help drive adoption and utilization of advanced analytics. Before the pandemic, data and analytics were already the new competitive differentiators. But now, creating the right level of digital resilience across an organization that can adapt and change quickly in response to external pressure and threats will set the foundation for the enterprises that ultimately survive and thrive. The key questions we should all be asking ourselves are how well do we trust the data that we use to make decisions?  And how can organizations implement decision intelligence to ensure future sustainability and growth?

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