Data Extraction Solutions

Modern digital technologies are constantly developing, and more and more buzzwords are appearing, confusing ordinary users and non-technical businesspeople who contract IT services for a variety of purposes.

Let’s talk a bit about the terms related to data science, data scraping, mining parsing, and extraction that puzzle people. They are really much more complicated than it may seem at first glance.

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:

1. Data source selection.
2. Collection of data.
3. Data storage.

Data parsing is the process of extracted data analysis for many purposes. The goals of data parsing could be: structuring unstructured text from a picture or PDF to operate in Word or Excel, or analyzing text to understand its meaning (data mining).

Data mining is one kind of data parsing. The goal of data mining is to analyze text and pictures and extract meaning and hidden meanings or detect trends and derive value from them. It is an area of machine learning called deep learning and often referred to as the KDD (Knowledge Discovery in Database).

Data mining techniques are widely used in various industries, including retail, e-commerce, healthcare, transportation, finance, telecommunications, and others, for the generation of data-driven insights.

It helps predict sales, analyze e-commerce carts, detect fraud, segment customers, and much more.

Data mining includes the following steps:

1. Data cleaning through automatic and manual inspection.
2. Data integration – combining and integrating information gathered from unstructured data sources, like text files, documents, the internet and databases, spreadsheets, data cubes, etc.
3. Selecting and fetching information from the large database.
4. Data transformation through normalization, aggregation, generalization, and so on into suitable forms for mining.
5. Data mining utilizing intelligent methods (classification, clustering, prediction, regression, association learning, and more) for data pattern detection.
6. Pattern evaluation in the already structured data.
7. Knowledge representation and visualization.

Data mining is more complicated compared to data extraction and requires a well-qualified staff and large investments due to the complexity and time required to prepare the information for processing.

HTML Parsing is performed with JavaScript that handles HTML pages. Primarily it’s used for text, resource and link extraction, screen scraping, incremental extraction of data, and so on.

DOM (Document Object Model) Parsing is used to define the style, structure, and content in XML files and get an in-depth view of web page structure.

Vertical Aggregation platforms are built to monitor specific verticals mostly by companies able to access large-scale computing power. Such harvesting bots are created automatically and operate without human interference, their efficiency is measured by the quality of the extracted data.

The other parsing techniques to mention are XPath, or XML Path Language, Text Pattern Matching, and even Google Sheets, which are all widely used by scrapers.

Data extraction technologies are used in many ways, and here are a few examples:

• to pull information from any website or web page
• to extract data from databases
• to retrieve text from paper documents
• to extract data from receipts
• to digitize pictures and turn them into code
• to digitize music and voice and turn them into code

The real power of the web scraping technique is its ability to facilitate building and powering groundbreaking business applications that help companies optimize their business processes, enhance their operation and bring customer service experiences to a totally new level.

Data scraping (also called web scraping) is the automatic process of copying and pasting content or metadata from web pages. Web scraping requires three elements: web crawling, data extraction, and data parsing technologies. Data extraction is only a piece of web scraping. You can read more about web scraping in our blog.

Our company develops custom data extraction software and delivers extracted, parsed, and cleansed data to our clients on demand, which is usually only one element of our more complex solution. We are eager to make our clients’ lives easier by delivering them software solutions or data of the highest quality. We relieve our clients of the necessity to code and to know the technical aspects of data extraction, scraping, mining, and parsing. We scrape information from all over the web, and the sources include complex, dynamic, and multilayered websites and web pages.

A good example is our recent project related to document text extraction. Our client had a business goal to extract information from electricity bills and structure it in Excel format for further analysis. We built a data scraping solution that is able to find a web source, copy bills from it, then extract row data securely and parse it. Our client came to us because he didn’t find any ready-to-use tools to implement this task. The most difficult part of the extraction process in this project was the text withdrawal and text parsing stage, as bills have different fields in their structure.

In addition, we know how to get past various anti-scraping measures widely used by target sites to prevent data extraction, how to handle challenges that may arise in the process of data harvesting, and how to stay within the bounds of the law.

Contact our professional expert Dmitrii to talk about your project. Schedule a free consultation.