What is Web Scraping?
Web Scraping also termed as, screen scraping, web data extraction or web harvesting is a simple technique used to extract large amounts of data from websites to save it to a local file on your computer or a database spreadsheet. A web scraping software will automatically load and extract data from multiple web pages based on the client’s requirements. Then, the data built is customised either for a specific website or is configured to work with any website. You can easily save the data available on your computer with a single click.
If you’re setting up a new business or expanding a business, web scraping is important. It will give you valuable information about your competitors and other insights. With the top-notch services of a web scraping company, you will be able to obtain the right information legally, that is required related to your business. For example, an e-commerce website like Flipkart wants to know about the sales and logistics of another e-commerce company or a blogger wants to know different keywords used by other online publishing companies. Web scraping analyses the data, for example, what clothes are trending online, which particular beauty product is used by different age groups, etc. Nobody can manually do the digging and scrap the data from different web pages, categorise them and present them. This will take time and be full of errors without any valuable insights. Web scraping software makes this task easier and the data that is delivered to you is the actual product of the service. With web scraping services, you can count on the accuracy levels and the data will prove to be an advantage to your business.
Some examples of the services provided by a web scraping company in different industries are- real estate listings of different properties, addresses, flooring, price fluctuations, analyses of huge data on bank accounts with its comparisons on the basis of the information you provide, interpretation of numbers into statistical data of B2B or B2C companies, marketing trends that made millions to a competitor company, real-time stock market tracking and so on.
Some of the known web scraping software in the market are- OutWit Hub, Spinn3r, ParseHub, Octoparse, ScraPy, Selenium, etc.
This entire process of extraction and analysis of the data from different websites is also called data extraction. Data is extracted using different tools and techniques. Every web scraping software has a different language and functionalities. To understand this, let us see an example- FMiner is a web scraping software that performs web data extraction, screen scraping, web harvesting, web crawling and web macro support for Windows and MacOS X. some of the features of FMiner include no coding required, multi-threaded crawl, visual design tool (visual editing) and CAPTCHA Tests, etc. ScraPy is another software that is an open source and collaborative framework to extract data in a fast and simple way. In order to scrape data in Python, we use the ScraPy spiders (basic units for scraping) and coding. It identifies the pattern and makes a way to get into the HTML coding of a website. One can save images and try new commands under the ‘interactive cell’. This way a web scraping company uses different software to extract data as per the requirements of the client’s website.
Once the data is extracted, it is saved in excel sheets as per the categories provided by the client. The data is maintained with all the records of the targetted websites and is checked for errors. A web crawler is another program that indexes all the web pages on the website and creates entries for a search engine index. A web or a data crawler browses through each web page collecting all the necessary information and links related to them. It also validates the hyperlinks and the HTML code. This procedure is called web crawling. Web crawlers are also used in data mining in which the pages are analysed for different properties like statistics, data analytics, etc.
As web crawling focuses on collecting sets of information or data directly from the websites, extracting the content required and ultimately this raw data is processed into large data sets. This process of allotment of data is called Data mining. Through data mining, information is calculated to derive meaningful patterns like metrics, statistics, complexities, etc. It provides a predictive and descriptive value to the data collected and involves hands-on knowledge of different tools such as Regression, Association rule and Classification. Data mining is responsible for predicting the unknown values of features and find interesting patterns that describe the data in detail.
Data processing is important in Data mining as it rectifies the missing values, identifies and removes noisy data, resolve redundancies, etc. There are some major challenges that one faces in case of managing the quality of data like handling the missing data, tackling data duplication, achieving data quality, performing data assessments and so on.
How to Achieve Data Quality?
Data quality is a major challenge after the data is processed. There are situations when companies have extensive data but fail to understand where to start. It is imperative to understand why the data was lost or recurring. Data cleansing is the first step to find these answers. It is when a company has multiple colossal databases, it becomes difficult to manage and thus, the quality of the data degrades or becomes outdated. Data quality can either give a business a competitive edge or backslide a company. Without high-quality consistent data feed, scrapping will never be able to achieve its true figures. Today, artificial intelligence and decisions on the basis of the substantial data is a reliable source. With the onset of the web scrapping process, you must always keep in mind the ways through which you will achieve high-end data and what would you do with it.
The following are some important measures to achieve data quality:-
1. Requirements– Make sure that you meet all the requirements of your client. The website data is highly questionable and category- based. The number of desired fields per item must be high. Items and fields must be covered, checked and updated from time to time. Since there are many different page layouts and, alternatives of different entities are being scraped, it is hard to check the quality of the data.
2. Identify– It is the responsibility of the web scrapping company to identify the root causes of the different challenges, acknowledge and work on them to deliver unambiguous data.
3. Data governance– The chief data officer is responsible for data governance, implying a series of policies and procedures that dictate how will the data be collected to meet the growing demands legally. It is critical to allocate the budgets correctly and in case there is no chief data officer in the organisation, this function can be carried out by the IT or marketing department.
4. Semantics– Semantics needs to be verified of the textual data or information that is being scraped. This is a major challenge for an automated QA. since no computer is absolutely perfect, a manual QA is also required for good quality of data.
5. Risks and Returns– You must always study the risks and expenditure your brand is shelling out on the data-driven marketing efforts. Your business will face setbacks if your databases are incomplete and inaccurate.
6. Spider monitoring– This is another key component of data assurance which is a reliable system for monitoring the status and result of your spiders in real time. With spider monitoring, one can detect any potential sources of issues simultaneously or post- execution of the data.