Data extraction is the process of retrieving or copying information from a given source (a database, web page, or another document). This can be done by using online documents, as well as when digitizing paper documents.
For instance, say you have an electricity bill mailed to you from an electric company. If you want to edit it for some reason via your computer, you must scan it and digitize it into a useful format—PDF or .doc. In this example, scanning is data extraction.
If you want to work with information from any website, you must copy this information into a useful format—Excel, Word, or JSON. In that case, the process of copying is data extraction.
Web scraping is withdrawing large amounts of data: hundreds, thousands, millions, and even billions of data points from the internet into centralized locations for storage or further data processing with the help of intelligent automation.
The unstructured data sources we can scrape information from are web pages, documents, PDFs, text files, emails, scanned texts, spool files, reports, classifieds, etc. The centralized locations for data storage may be on-site, cloud-based, or a hybrid of the two. Scraping technology speeds up data collection many times over, and largely enhances its accuracy.
Data extraction can be used in various industries for multifaceted purposes: price monitoring in e-commerce, research for an individual paper, real estate industry monitoring, business lead generation, sentiment analysis (either brand or product or service), content, and news aggregation, marketing, consulting, finance, and more.
The 2 major goals of data extraction are converting information from physical into digital formats and information transfer. It is important to keep in mind that the data processing or analysis that may take place later are not parts of the data extraction process.
In fact, data extraction is the initial step of a more complicated data integration strategy, which includes the extraction, transformation, and loading of the information. It can also be one of the stages in the data mining process.
Automated data extraction is realized in 3 successive steps:
- Data source selection.
- Collection of data.
- Data storage.