Although difficult to manage, the value of data cannot be understated. Businesses that base their decisions on data-driven analytics are 23x more likely to acquire new customers, as well as 19x more likely to turn a profit. These incredible statistics demonstrate just how essential it is for businesses to find data, understand it, and put it to good use. One of the main data engineering methods that have become vital over the past few years is web scraping – where data is extracted on-masse from different websites around the internet. This form of data extraction is highly targeted, allowing businesses to pull specific information from their competitors or their whole industry at large. The particular strength of web scraping is due to how powerful it can be when pulling huge quantities of data. While effective on a small scale, web scraping is most potent when scaled, with the scalability of this system allowing businesses to pull millions of data points in mere minutes. In this article, we’ll be exploring large-scale web scraping, demonstrating how businesses can use this data extraction tool to increase their understanding of customers, improve processes, and boost the success of their business.
Why Use Web Scraping Over Manual Data Collection?
Web scraping, as an automatic process that can continually run in the background while a data engineer gets on with other work, saves a huge amount of time when compared to manually collecting data. Instead of moving from page to page and searching for the information that your company may need by hand, a web scraping tool will race through potentially millions of pages in only a few minutes.
Why Use Web Scraping Over Manual Data Collection?How Can a Business Put The Findings of a Scaled Web Scraper To Use?Data-Driven InsightsCompetitor ResearchUnderstanding SEO – Backlinks and external SEO factorsWhat Issues Prevent Data Collection at Scale?How To Prevent Human Error When Using Scaled Web Scraping with Web HooksFinal Thoughts on Web Scraping Scalability
The scalability of this system, being able to increase the information load that a web scraper is moving through, ensures that huge amounts of data can be collected in seconds. As this data is then directly used for business analytics and intelligence, it provides a huge advantage for a range of companies. Going beyond the benefits of data collection, there are several specific advantages of web scraping when compared to manual collection:
Speed – When scaled fully, web scrapers can work continuously, picking up new data points and entering them into your designated spreadsheet. While a data engineer could do a few sites every minute, platforms such as Bright Data can run millions of web scrapers at a time, saving your company both time and money. If you’re looking for speed and efficiency, then the automatic features of web scrapers push them far ahead of manual work in terms of usefulness. Less work – Alongside being faster than manually collecting data, using a web scraper means that your data engineers have significantly less work facing them. Instead of wasting hours of their data farming data, they can focus on conducting data analysis, and providing insights to your business from their findings. No missed data points – When conducting a huge data collection operation at an industry-wide scale, there is a fairly high chance of human error resulting in missed data points. In comparison, when a web scraping tool is conducting this same investigation, the fact it’s simply following a set script will ensure that not a single data point is missed. This, combined with the speed, makes web scraping significantly more efficient than manually collecting data. Collected into one neat database – Web scrapers are a data engineer’s dream, with their continual feeding into singular databases ensuring that all the necessary information ends up in the same place. The delivery of data to a specific database when using a web scraper ensures that data engineers can conduct analysis without having to search around for data. If the parameters of where the scraper will be delivering data to are correctly configured, this neat database makes the secondary part of any investigation much smoother.
Data engineers notoriously spend a lot of time working on collecting and preparing data, with around 80% of their entire job being directly linked to this function. By using web scrapers within your business, you’re able to cut back on the total amount of time that they spend collecting data, ensuring that they’re working on more useful tasks to the company. The movement away from manual data collection and toward web scrapers has been catalyzed by the ability to scale data extraction, with the automatic web scraping system winning out every single time.
How Can a Business Put The Findings of a Scaled Web Scraper To Use?
Scaled web scrapers allow businesses to collect data on virtually anything that they want information on. From checking how much competitors are charging for a certain product to tracing the public sentiment around a company based on social media posts, web scrapers can obtain almost anything. Their flexibility is one of their biggest advantages, which has meant that web scrapers have been adopted into a range of different industries. Flight companies, eCommerce brands, real estate agents, financial institutions – everyone needs data, so web scrapers are the perfect solution. Some prominent use-cases for web scrapers that are used across several different industries are:
Data-Driven Insights Advertising Competitor Research Understanding SEO
Let’s break these down further.
Data-Driven Insights
The main purpose of gathering data within a business setting is to help inform a company about future decisions. Depending on what industry data or competitor data reveals, a business may decide to opt for one decision over another. Even when looking inward, a business’ own data can reveal trends that were once hidden, allowing the company to capitalize on this new information. Reactions to data are known as insights, with actionable insights being the plan that businesses put in place after understanding which direction data is pointing them in. Data-driven insights are extremely common within the world of business, but even more so in eCommerce businesses. As eCommerce businesses inherently exist online, they have access to a pool of web data information. When a customer enters their online store, every single movement is tracked, giving the business information about what they clicked on, how long they stayed on certain pages, and even where they clicked off the website in the sales process. Moving externally, a business could use a web scraper to farm information about trends within their industry. By analyzing the movement of products or the surge of interest in certain search terms within the industry, your business can always stay one step ahead and react quickly when new information is uncovered. For example, in the world of eCommerce, web scraping at scale through social media may reveal that users are beginning to talk more about a certain type of product. By knowing this information ahead of time, your business can then focus on bringing out an eCommerce product that aligns with current trends, This is seen all over the world, with brands adapting their products or the marketing around those products to ensure they remain in line with what their customers want to buy.
Competitor Research
Scaled web scrapers are able to move through millions of different pages at once, farming specific data and then feeding it directly to your data analysts. When trying to establish an industry average through competitor research, web scrapers are by far the most effective way of doing so. With a web scraper, your business can move through your competitors’ web pages, farming pricing information and then informing you of how much you should be charging for certain services or products. This is most commonly used in the aviation industry, where flight companies will gather information about how much their competitors are charging for a flight and then adjust their prices accordingly. Additionally, by using scaled web scraping, you’re able to pull information from millions of other businesses at once. With this, you can establish industry averages, then using these to determine where you fall on the wider scale of your industry. This information can illuminate your business strategy and help you to refine what you’re offering, and the price you’re offering it for.
Understanding SEO – Backlinks and external SEO factors
Search Engine Optimization (SEO) is one of the most important elements of modern business strategy, this feature of your company’s online presence deciding where you rank within search engine results. Considering that the top 3 Google results for a search term get 75.1% of all clicks, being near the top of the rankings can significantly increase the success of your business. When attempting to create a strong SEO strategy, building backlinks and focusing on the correct keywords for your business are two of the largest goals. At scale, web scraping is one of the most useful tools there are for both tracking, defining, and organizing these two goals. SEO research and web scraping are intertwined in a range of ways:
Backlinks – Web scrapers can move through millions of websites and search for backlinks to your site, creating a total number of links and helping you contextualize your growth in this area. Guest Posts – In order to build a backlink strategy, businesses need to find guest post opportunities. While many of these backlinks will come from press releases and other content opportunities, the vast majority of early links will come from guest posting. Especially if your business is only a few years into operation, then finding and utilizing guest posting opportunities is vital. A web scraper can comb through the internet in the business industry you work in, finding leads and opportunities that would have taken weeks manually. Keywords – A core part of SEO strategy is keyword research. To effectively understand the competition and search volume behind each keyword, web scraping tools are used to rapidly farm the necessary information. The vast majority of SEO tools, everything from Ahrefs and SemRush to SurferSEO and Google Keyword Planner, use web scrapers to gather information on keywords. This information is then relayed to businesses to help them inform their ongoing SEO strategy.
Without scaled web scraping, this whole industry would struggle to establish itself, as the information that SEO research relies on has to be taken at scale to have any reflection of reality.
What Issues Prevent Data Collection at Scale?
While web scraping is a perfectly legal process, many companies try to protect their web pages from being accessed by these tools. Due to this, there are a range of different issues that may cause your web scraping tool to either be unable to access a page or be unable to find the correct information. Typically, there are four main reasons that your web scraping tool could run into some issues:
CAPTCHA – This acronym comes up a lot when browsing the internet, with users having to complete a short task to demonstrate they’re human before entering onto a site. If a site has a CAPTCHA on it, then your web scraper will most likely be unable to access the web page, failing to pass the test and recover the page’s data. Honeypots – A honeypot is where someone creates a hidden element on their site that is undetectable to humans. However, once a web scraper begins to move through a page and encounters this element, it will be trapped, with the website then getting information about the IP address it’s being launched from. These websites will then block your web scraper to ensure it cannot harvest any data. Robots.txt – Some websites will edit their website configuration to ensure that web scrapers aren’t allowed on their site. If this is the case, it’s best to stay off their website. Human Error – When managing a web scraper, it’s always a good idea to ensure that at least one data engineer is monitoring the process. Often, if they aren’t monitoring, the web scraper could crash or stop.
While these aren’t all the reasons that a web scraper could run into some issues, they are the most common errors you’ll encounter. Over time, you’re able to refine your company’s web scraping strategy to ensure you can benefit from the scaled data extraction without falling into too many pitfalls.
How To Prevent Human Error When Using Scaled Web Scraping with Web Hooks
While human error can significantly reduce how effective scaled web scraping is, there are a range of ways that you can get around this problem. All of these issues come back to minimizing human contact with the web scraper by using other software tools to replace manual tasks. Typically, developers will connect a webhook to their web scraper. Web hooks allow direct communication between web-based applications and a central platform. This connection allows them to send developers signals that can alert them whenever the search crashes or stalls. Equally, web hooks allow you to set up asynchronous scraping, with a list of potential websites or website types that you want to be scraped being added to the queue with this system. By using web hooks in line with web scraping, developers can effectively take a step back from the process, automating it to a further degree. This automation facilitates scaling, with the removal of a chance of human error allowing web scrapers to work for longer, creating a large pool of data that companies can then put to use within business intelligence.
Final Thoughts on Web Scraping Scalability
Web scrapers are incredibly useful tools that, due to their scalability, provide businesses with the opportunity to collect millions of data points rapidly and with ease. From gathering data on business competitors to refining internal SEO strategy, web scrapers have a range of uses that help businesses plan for success. Their true power lies in their scalability, with the sheer quantity of data out there to move through, making them the perfect tool for mass extraction. Web scrapers allow data engineers to save time, provide a continual flow of information for data analytics, and form the basis of the insights generated for future business intelligence.
