The first web browser was created in 1990 and the first web robot was built in 1993. It was only for measuring the size of the web. By December 1993, the first web-crawler based search engine, JumpStation had been created even though the data was not being scraped. Python’s BeautifulSoup, the easy-to-use web scraping library was created way back in 2004. But these were only the stepping stones to the form and extent that we are seeing in the field of web-scraping today.
Some of the biggest ongoing data science projects, be it on social media data, or image detection, are using the vast amount of data available on the internet to build a database before validating which algorithm runs best. Hence, web-scraping is a new way forward- be it in the field of medical science or marketing. The massive amount of data it has put in the hands of people has helped make decisions more data-backed and intelligent.
The Future of Web Scraping will lead to New Opportunities:
- As newer and faster web scraping techniques come into play, data will get cheaper with time. As a result of this, more companies and people will be able to have better access to market data. Today, while most of the companies that are using data scraping, machine learning and predictive algorithms in different departments are mid to large-sized, as web scraping becomes more common, even startups or companies that are just setting up businesses will be using data in their decision-making processes. Companies have started to use data even before they set up shop. For example, if a person wants to open a new cafe. He will not go about asking a real estate manager to help him decide the location. Instead, he will crawl data from the web to find the most popular cafes in town and the regions with a maximum density of cafes. Then, he will find the ideal location with a demographic. That would most likely visit the cafe and also not have a high concentration of existing cafes. In this way, a business owner would decide on the most suitable location for his upcoming businesses.
- When we speak of web scraping or data scraping today, in most cases we are talking of textual data-comments, tweets, messages, sentiment analysis and more. However, web scraping has gone far beyond these. Analysis of satellite images to predict natural disasters, using videos of interviews for training a computer. And more such projects are underway at this very moment. Most of these use data scraped from the web for building the training set. One of the most popular methods of research. In which such unstructured data used is facial recognition. These projects require a vast amount of unstructured data, and often a steady feed of it- something that can only be gathered through web scraping.
- Web scraping is only the first step to business solutions formulated by companies. Building an entire decision engine or a predictive model is possible today in a matter of minutes using cloud infrastructure like those offered by Amazon AWS. This is beneficial for companies that do not have the resources to build their entire infrastructure in-house by buying dedicated servers. This way, cheaper and more accessible infrastructure would help companies make the most of massive datasets. That they have scraped from the internet. Machine Learning algorithms can run 24×7 on fully managed instances in the cloud and can take care of consuming your steady web-scraped data-feed.
- With the growth of web scraping, the collaborative spirit will increase. Whether you are a lawyer trying to find relevant information on a case or a doctor who is trying to find if any data exists on a new type of virus strain that he has discovered, you can crawl data off the web using automated spiders which can provide you with the relevant information in the desired format. If the published information gained is not enough you can then contact the professionals who have written the texts that you scraped and in this way, data would bring people living thousands of miles apart, much closer.
- Today, most business decisions are still based on outcomes of board meetings and end up being prone to wrong decision making. But data-backed decisions are becoming more and more common, and with time, we can expect that soon enough, decisions and plans will be fed into predictive engines that will be using historical and current market data to predict the viability and chances of success. Even though it would not remove risks and problems completely, your decisions would be based on actual data, and you will have a better understanding of scenarios and can predict issues that can prop up, early on.
- Investors will benefit the most because of the strides in the field of web scraping in the upcoming days. Be it amateur investors or hedge-fund managers, live data feed related to the market that would shed light on scandals, fiasco, and news related to companies. The stocks they want would help in faster decision making and would also enable people to make data-backed investments. Live data from web scraping feed will reduce the fear of missing out among investors.
- Data cleaning will get more challenging with time. As more and more types of media content get added to web-pages. The separation of structured and unstructured data becomes more. They also convert data scraped from a website into data in a database server. This will result in the need for dedicated data cleaning solutions so that massive databases. Even if there is a small percentage of unclean data they are not rendered useless.
- Redundancy management and handling duplicates will be an issue when companies plug in multiple streams or web-scraping sources. Duplicate data can result in inflated numbers or a biased predictive model. Duplicates handled by running a dedupe logic even before data added to the database. On the other hand, when you have multiple sources, you can use data from one source to validate the other.
- The rise of newer front-end technologies can result in more complicated websites, in terms of web scraping.
- Every time a new technology comes to play, web scraping spiders need to configure and train to crawl data. This becomes especially hard and time-consuming in case the entire layout has also changed.
- Many websites are preventing scraping by allowing access to data only through a login page. And when you login, you are accepting certain rules and conditions which usually negate web-scraping. This can make web-scraping more complicated.
- With more types of data scraped today, there’s a need for more types of storage solutions. Also, data will be stored in a way such that it is easily retrieved. The other problem is that as we add more and more data sources, our scraped data storage increases. But we end up using only a small part of the total data for our decision making. Hence, there’s a need for efficient data scraping and storage so that one can save both money and time.
With web scraping becoming so common, almost every industry and sector. They try to make the most of the huge repository of data to revive and transform itself. Whether you are in the workspace renting business, or you’re just selling books online. You will have to use data to your advantage, and for businesses that end up not doing that. This will only leave more data at the table for their competitors.
If you are a tech-based company, you should try to incorporate scraped data into your workflow. If not, you should try to use cloud-based solutions to crawl data and use it to your advantage. Different SaaS solutions by Amazon AWS help in the storage and transformation of data and even let you run machine learning algorithms on them to build predictive models. And when it comes to getting the web scraped data, all you need is a DaaS solution like PromptCloud. We offer fully managed enterprise-grade web scraping solutions that can transform your business.