What is Data Parsing and Why Does Your Business Need It?

Typical applications of data parsing are limited only by our own imagination. Parsing extracts large volumes of data from virtually all websites for numerous purposes such as price monitoring, financial data retrieval, news analysis, and so on. Data and price parsing and collection allow businesses to create new products and innovate faster and better.

For instance, a price comparison site like Kayak, an SEO product like Botify, or a job aggregator built from multiple sources – all these sites are built entirely on parsing. By ensuring easy access to data, parsers enhance your value proposition. Before we reveal why parsing is such an important tool and which industries need it most, let us tell you what data parsing actually is.

What is Data Parsing?

Data parsing from websites is the automated identification and extraction of data from websites. The importance and need for data aggregation have grown immensely. Moreover, quality data for the analytics industry is scarce. Web parsers are essentially spiders that collect every bit of information from a site. Regardless of the industry you work in, data parsing will be a solution to at least one of your problems.

Areas of Application for Web Scraping Services

A). Sentiment Analysis

Every social media post published over a certain period of time invariably reveals the big picture and helps analysts understand consumer sentiment and behavior. Built-in APIs of all platforms of social media may be insufficient. To understand where the conversation is happening and which micro-trends attract the most attention, for example by analyzing hashtag usage, it is necessary to "crawl" social networks.

B). E-commerce Pricing and Price Monitoring

Price wars have reached a new level thanks to e-commerce data parsing. In an oligopolistic and price-sensitive market, it is crucial to monitor how the price of a product is set overall. As a seller, you can also see which platform offers the best margin on your products.

C). Job Aggregators

Job aggregators use scraping services to scan all career-related web pages and consolidate them in one place. They essentially work as search engines for job listings thanks to their advanced search functionality. Job scraping occurs regularly to ensure that only current and relevant vacancies are shown to the talent pool.

D). Machine Learning

Artificial intelligence and machine learning require a constant influx of quality data to mimic and replicate humans. They need to continuously receive the latest information so they can constantly adapt. Cloud services help them by collecting large amounts of data, text, and images.

ML drives technological marvels such as driverless cars, smart glasses, image and speech recognition. However, for exponential scaling, these models require regular data updates to improve accuracy and reliability.

E). Brand Monitoring

Most e-commerce players (such as marketplaces Ozon, Wildberries) operate solely based on reviews and ratings. Consumers inherently trust other consumers more. How can you, as a brand, leverage this to promote your image and digital advertising?

You can collect product reviews and ratings from every site where your products are listed and then aggregate them. You can take this to the next level by monitoring social media platforms and combining it with sentiment analysis to quickly respond to detractors or reward and incentivize users who love you.

The industries that need this are endless: travel, hospitality, e-commerce, all online aggregators, app developers.

F). SEO

If a website is not on the first page of Yandex, it doesn't exist. Hence, SEO. And if you're working on SEO, you're likely using tools like SEMrush or Ubersuggest. Fun fact: these tools would literally not exist if not for data and link parsing.

These are the tools you can use to find SEO competitors for a specific search query. You can identify the title tags and keywords they are targeting to understand what drives traffic to their sites and boosts sales.

How to Organize a Data Parsing Project?

A). Define the Goal

It's straightforward. Determine exactly what you need. How to do it? Answer the following set of questions.

• What kind of information do you need?
• What do you expect as a result?
• Where is the sought data usually published?
• Who is this data intended for?
• In what format should this data be presented to end users?
• Typical data retention period? How often do you need to perform this task?

B). Analysis of Parsing Services

Since data scraping is a highly automated process, the type of data parsing service you use is paramount. Here is what you should keep in mind before choosing a scraping service:

• Project sizes
• Supported OS
• Does it support your enterprise requirements?
• Support for scripting languages
• Support for built-in data storage

C). Designing the Parsing Schema

Perhaps our task is to collect data from employment sites about vacancies posted by recruiters. The data source will determine the schema attributes. It will look like this:

• Title
• Identification number
• Description
• URL used by the candidate to apply for the job
• Location
• Compensation
• Job type
• Required experience

D). Feasibility Check and Trial Run

Before embarking on a full-scale parsing project, it is always useful to conduct a trial run. How to do it?

• Check the feasibility of parsing the source websites
• Collect HTML code
• Extract the needed element from the tag
• Identify URLs leading to subsequent pages

If you are satisfied with your results, you can move on to a larger-scale check. You may need to catch corrected Xpaths and replace them with hardcoded values. An external library may also be needed to serve as input data for the source.

Now that we have told you about parsing, you might think it is a giant task requiring technical oversight. And yes, and no. While you can do it in-house by upskilling your employees.

Or by using the many available "do-it-yourself" tools. But websites are becoming more and more complex every day. The need to outsource data parsing to a premium service provider is likely the best way for large-scale data collection. Choose providers who also have experience in custom web development, so they can offer you analytical capabilities to work with the collected data.