Data parsing is a parsing technique that computer programs and systems employ to automatically extract structured data from sources of semi-structured or unstructured data. Data parsing is used to extract specific information from a large dataset, which can then be analyzed and used to gain insights into trends and patterns. The process of data parsing can involve various techniques, such as text parsing, image parsing, and speech parsing.
In this article, we will explore the process of data parsing in more detail, including the techniques used, the tools available, and the applications of data parsing in various industries.
Definition of data parsing
There are many definitions for data parsing as it is a vast concept but, we’ll try to keep it simple in this article.
In simple terms, data parsing is the conversion of data from one format to another; usually from unstructured data (for example in HTML or PDF) to structured data (for example JSON or CSV). It converts the data into another, more readable, format for a machine.
A data parser helps to parse data and transforms it into any structured format that you need. However, not every data parser works in the same way; some have specific parsing rules to follow.
By the way, what is parsing?
Wikipedia defines parsing as “the process of analyzing a string of symbols, either in natural language, or data structures.
In a programming language, data parsing refers to analyzing data and structuring it according to specific rules. At Parseur, for example we specialize at parsing emails and parsing PDFs so that you can reuse the unstructured document data in other applications.
Is data parsing the same as data extraction?
Data parsing and data extraction are distinct from one another. Data extraction refers to retrieving the data from documents and data parsing is the transformation of data into a usable format.
Data extraction is the first step in the ETL (Extract Transform Load) process while data parsing is the second step.
There are 2 types of data parsing
Data parsing can be categorized into 2 different types of approaches such as:
In grammar-driven data parsing, you define a set of rules to identify the structure of a piece of input text. These rules can be defined in a file or as part of your code. A great example of grammar-driven parsing is regular expressions (regex).
In contrast, data-driven parsing uses Machine Learning and AI methods and languages such as natural language processing (NLP). An example of data-driven parsing would be to extract and identify names or addresses from a document.
Benefits of data parsing
When dealing with large chunks of data, it becomes important to ensure that the data is reliable, accurate, and free from errors. And, data parsing has many advantages over manual data entry as indicated below.
Data parsing can be much faster than manual entry because a machine can process large amounts of raw data quickly, way faster than a human would. A single data parser can parse thousands of files at once and process their contents within seconds or minutes. Manual entry takes much longer because each record must be entered individually by an employee who will probably make mistakes along the way.
In 2013, U.S. businesses were hit with nearly $7 billion in IRS civil penalties, due mainly to incorrectly reporting business income and employment values.
When companies rely solely on human employees for their database needs instead of using data parsing solutions like Parseur, errors may occur. Data parsing guarantees accuracy because it's done using software that is reliable when entering numbers into fields or looking up names in a database.
Organizations that have been collecting data for a while may have it in a completely different format. Data parsing makes it simple to digitize those data and put them to good use.
Use cases of data parsing
Undoubtedly, data parsing is widely used by various organizations in different industries. We’ve gathered the most popular use cases below:
Real estate agents receive hundreds of leads daily from different platforms (Zillow, Trulia, Realtor). With data parsing, they can easily extract buyers’ information and property details and send those data to real estate CRM tool such as Realvolve or Wise agent.
Learn more on how to automate real estate leads.
Financial organizations like banks or insurance companies deal with millions of transactions every day. These transactions are stored in databases and need to be parsed for analysis and reporting purposes. Data parsing helps them make sense of this huge amount of information so that they can provide better services to their customers
Healthcare organizations are required to store an enormous amount of patient records that need to be parsed for analysis purposes. For example, doctors want to access the medical records of patients instantly at any point in time during the surgery or treatment process.
Food ordering & delivery
If you are in the food industry, then you must be aware of how important it is to extract the correct order details and customer information to deliver the right order. Through the data parsing process, information can be easily extracted, transformed, and sent to a shared Google spreadsheet.
Just like Barberitos sales increased to 30% with Parseur, you can also automate your food ordering process.
Should you build your own data parser?
The most important question which crops up now is whether you should build a data parser or buy a data parsing tool. Building a data parser has both pros and cons.
Advantages of building a data parser
- More control over the parsing process
- Customize the tool as per your requirements
Disadvantages of building a data parser
- Training staff to understand requirements and draft specifications
- Resources and funds needed to invest in the development of the tool
- Need for inevitable maintenance to adjust the tool which will cost substantial time and money in the long run
The alternative: Use a data parsing tool like Parseur
Parseur is a powerful email and PDF parsing tool that automates data extraction from emails, PDFs, spreadsheets and other documents. Parseur has an innovative OCR engine that uses zonal OCR and dynamic OCR to capture all data quickly and reliably and requires zero coding knowledge.
With its built-in features, Parseur can:
- Extract data from both text-based and image-based documents
- Extract repetitive blocks from tables
- Automate data parsing from specific use cases such as food ordering, real estate, or Google alerts
- Send data to any other application such as Google spreadsheets, Zapier, Make or Power automate
We hope that now you have a good idea of what is data parsing and how a data parser works. When deciding to build your parser or buy one, keep in mind whether you have large volumes of data to parse or not.
Here’s a practical tutorial on how to parse data with Parseur without coding. Parseur can parse millions of data within minutes - yes, you’ve heard it right! If you want to see how Parseur can help you be more cost-efficient, do not hesitate to sign up for our free plan below.
What is an example of data parsing?
Data parsing can be used to extract specific information from a large text document, such as a resume, using techniques such as keyword matching and regular expressions.
How to use a data parser?
Different data parsing tools have different features. If you use a data parser like Parseur, there will be zero parsing rules or coding knowledge involved.
What tools are required for data parsing?
Parseur, Scraper API or Import.io are all examples of data parsing tools.
What is data parsing in Python?
You can write your own code in Python for advanced data parsing