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The introduction of big data in business models has completely revolutionized how we perceive business and technology. Yet, it has brought the need for heavy tools for the extraction, analysis, and processing of such a massive amount of data. Also, just like natural languages, computer and programming languages require accurate translation to enable effective communication.
This is precisely where data parsing comes into play and solves the problem of comprehending complex data. Data parsing converts unstructured or unreadable data into well-structured and easily readable data.
The best part about data parsing is that no matter if you work within a company's development team or have to deal with customers with roles in marketing, you need to understand the data for maintaining your productivity, developing new ventures, or communicating with clients. In short, it is essential for long-term business success that you know the data.
In this article, we will explain the structure of data parsers, how they can help your organization, and how data parsing makes it convenient for you to understand data. Additionally, we will tell you whether you should develop your parser or buy one for your company's needs.
Let’s get started.
What is Data Parsing?
Data parsing is a technique where a string of data is converted into a different data type. For example, if data arrives in raw HTML, a parser transforms it into a more readable format, facilitating easy comprehension and interpretation.
It is a popular data transforming process, commonly used in compilers where we have to parse the computer code into simpler machine code. Likewise, when web developers write code that runs on hardware, they have to use data parsers. The exact process is also employed in SQL engines, where SQL engines first parse an SQL query and then execute it and show the results.
Data parsing is also helpful in the case of web scraping, as the data extracted from a web page after scraping is usually difficult to comprehend. So, the data parser makes it more readable and prepares it for better analysis by researchers.
Powerful data parsers can transform any data into another format. For example, they can transform raw HTML into a JSON entity or change a JavaScript page into a readable CSV file.
However, a critical aspect of data parsing is that not every piece of information gets converted during the data parsing procedure. This is because programs have their own rules in terms of parsing.
How does the data parser work?
In terms of computer engineering, data parsing is the process of analyzing a string of symbols, special characters, or data structures using Natural Language Processing (NLP) and then structuring the given information from data sets and organizing it according to some user-defined rules. Or in other words, it is a method of extracting information from files and filtering through them.
Nevertheless, just like the linguistic definition of parsing, the whole data parsing process revolves around scrutinizing sentences and mapping some semantic relationships between them.
Structure of a Data Parser
A data parser comprises two components, i.e., lexical analysis and syntactic analysis. Some parsers also feature a semantic analysis component. It takes the already parsed and structured data and applies meaning to it. For example, a semantic component will filter the data further into positive or negative, complete or incomplete, etc., and enhance the quality of the final data.
How does it work?
Here is how a data parser works -
● First of all, a parser differentiates the information of the HTML string and recognizes which data is actually valuable and needed for further operations.
● Now, following the parsers' pre-written rules and codes, it selects the required information and converts it to JSON, CSV, or another format.
One important thing to notice is that a data parser is not tied to any particular data format. Rather, it is a tool that converts data from one format to another, and the whole process of altering the format depends on the build of the parser.
Uses of Data Parsing
Data Parsers are used for many technologies and languages, such as:
● Java and other programming languages
● HTML and XML
● Interactive data language and object definition language
● SQL and other database languages
● Modeling languages
● Scripting languages
● HTTP and other internet protocols
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What are the different types of data parsing?
Data parsing usually takes two types of approaches for the semantic analysis of text, i.e., grammar-driven data parsing and data-driven data parsing. Let’s discuss both the approaches in detail -
Grammar-driven data parsing
In this technique, the data parser uses a set of formal grammar rules and accomplishes the parsing task. In simple words, sentences from unstructured data are first fragmented and then transformed into a more structured and easily understood format.
However, this approach has one problem, i.e., it lacks robustness. So, in order to overcome this problem, grammatical constraints are often relaxed so that the sentences outside the scope of usual grammar can be ruled out for data parsing analysis.
A popular subset of grammar-driven data parsing is text parsing, which analyzes a given string. This type of parsing process is highly successful in resolving the disambiguation problem faced by conventional parsing methods.
Data-driven data parsing
Data-driven data parsing is based on a probabilistic model of conversion. Unlike the deductive approach of text analysis used by grammar-driven parsing models, it applies rule-based methods, semantic equations, and Natural Language Processing (NLP) for structuring the resultant sentences and their analysis.
This parsing approach uses smart statistical parsers and modern treebanks for getting broad coverage from languages. That’s why conversational languages and sentences that demand precision but are domain-specific and unlabelled are usually managed by data-driven data parsing.
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Benefits of Data Parsing software
Work optimization
The most significant advantage of data parsing is that it helps you navigate through tremendous quantities of data by simplifying it and making it more readable. Therefore, it allows an organization to be more productive and more efficient.
Saving time
Data parsers help businesses by providing them with the right algorithm or the right tool to extract the data from its present form. Next, it converts the data into another form and automates the process that would otherwise need to be done manually. As a result, it helps the company run its operations faster than ever. Also, organizations can use their human resource somewhere else for more value-based tasks.
Improved User Interface
When businesses have a mass of data in their hand, they have to struggle with its usage, extraction, management, analysis, etc.
Data parsing makes the data more accessible and increases its searchability. It creates files that would otherwise be difficult for the company’s computers to read or compile more accessible than before. Also, when these data files become easy to read, the final product offered to business professionals may come out to be more readable than before.
Modernizing Your Data
Data accumulated by businesses can be years old and may not be available in the current format. In other words, it might be challenging to make any use of such stored data. However, this data can be extremely valuable for understanding the company's development or for any other use.
Data parsing can quickly change the format of this data and make it decipherable and usable for today’s demands. And who knows which data will become a lifesaver for your company in the future!
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Use Cases of Data Parsing
In terms of usage, data parsers are being used in different industries for multiple purposes. Here are some of their uses in various domains of work-
Business workflow optimization
Data parsers help companies structure unstructured datasets and convert them into usable information. That’s why businesses use data parsers to optimize their data extraction workflows.
Similarly, data parsers are used for investment analysis, marketing, social media management, and other business applications. Data analysts, programmers, marketers, and investors can observe a substantial increase in their productivity with data parsers.
Finance and Accounting
Banks and NBFCs use data parsing to scrap through billions of customer data and extract relevant information from applications. It is also used for analyzing credit reports, investment portfolios, income verification, and deriving accurate insights about customers.
Finance firms also use data parsers for determining interest rates and loan repayment periods.
Shipping and Logistics
Businesses that sell online products or services use data parsing to extract billing and shipping information. Parsing is also used to manage shipping labels and ensure the data format is correct.
Real Estate
Real estate firms use data parsing technologies to extract data from real estate emails by property owners and builders or CRM platforms and then process the information to forward to real estate agents. These details, such as contact details, property addresses, cash flow data, and lead sources, are highly profitable for real estate companies and help them make purchases, rentals, and sales.
Investment analysis
Smart investments demand acquiring lots of relevant data such as equity research, evaluation of start-ups, earnings forecasts, and competitive analysis. Collecting and analyzing all such data is time-consuming.
Using web scraping tools along with a data parser can simplify these tasks, optimize the workflow and allow you to direct the resources elsewhere while providing you with more in-depth analysis.
Moreover, data parsing tools can provide investors and data analysts with better insights for making the right business decisions.
This is why big investors, hedge funds, and other investment professionals use web scraping and data parsing tools to evaluate start-ups, predict earnings, and even monitor social sentiment and maintain accurate market insights.
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Should you build your own Data Parser?
The most important question is whether your organization should build its own data parser or not. Let’s find out by comparing the pros and cons of building your data parser software.
Pros of Building your Own Parser
- Customized according to your needs - The most significant benefit of creating text parsing software is that it is specially customized for your organization. So it helps in-house teams meet your company’s specific parsing requirements.
- Cheaper - It’s usually economical to build your parser.
- Gives you More Control - When you develop your own data parser, you’re in better control of whatever decisions you have to take for updating and maintaining your data parser.
Cons of building your own data parser
- Staff Training - The main issue with data parsing technology is that you have to train your whole staff to use it.
- Expensive - Secondly, the costs of building a custom parser software is relatively high as it needs lots of time and resources. If you have to build a parser, you’ll need to hire and train a whole in-house team.
- Require lots of planning and endeavor- These kinds of intelligent solutions require lots of planning and need their own dedicated servers for instant parsing. So, you might need to buy or build a powerful server fast enough to parse the information.
- Migration - If you have to migrate your systems, your parsers might not be compatible with the new systems and will demand upgrades.
- Maintenance - A parser needs regular maintenance, meaning you would have to spend more money and time.
So, you may need a data parser to meet your requirements or boost your organization's efficiency. In that case, you should choose a data parser compatible with your legacy systems and designed for various use cases.
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Buying a Data Parser: Alternative for Building your Own Data Parser
Now, if you are not willing to build your own parser, another option is buying a tool that parses data for you. Many companies sell parsing tools that you can customize for your requirements and incorporate into your operations. You don't have to worry about spending time and effort on building a parser to enjoy its benefits.
Here are the pros of buying a data parser
● No expenditure on human resources - When you purchase a parser, you don’t have to spend a penny on human resources. Everything will get done, including the maintenance of the parser and the servers.
● Faster solution for issues - If any problems come across your parser’s way, you don't have to stress as the company you buy your tools from will have extensive knowledge about its maintenance and be familiarized with their technology.
● Fewer chances of failure or crashes - Parsers made for sale are already tested and perfected to fit the markets’ requirements. So, there will be fewer chances of crashing or experiencing issues in general.
However, there are a couple of downsides to buying a parser. To begin with, it will be slightly more expensive. Secondly, you won’t have too much control over it compared to a parser made by your in-house team. Therefore, you must calculate all the pros and cons and then choose the right approach for your parsing requirements.
The right parser for you also depends on your parsing requirements and the amount of parsing work done by your organization. For example, if you need an easy parser, an expert developer can probably make it within a week. However, if it is complex, the whole development process can take months.
Nanonets for Data Parsing
Nanonets is a document automation platform. Nanonets can be used to parse data from any kind of document: PDF, word, scanned, or digital document. Our data parsing tool can extract data with 95% accuracy using our in-built OCR software.
Here are some features that make Nanonets an excellent choice for data parsing:
- OCR Software with accuracy of 95% or more
- Automate document sourcing from emails, drives or more
- Easy integration with 5000+ applications
- Modern UI
- 24x7 Support
- No Hidden Charges
- Rule based workflows
- Forever free plan
Conclusion
To sum up, data parsing is one extremely helpful technology for organizations such as management firms, insurance companies, etc., that can make information more accessible and usable than before. This smart technology automates the manual work of data extraction and makes business operations more agile and scalable by nature. The converted data can be used for sharing information with clients, partners, and teams.
Therefore, if you haven't considered incorporating a good parser, you should do it today.
FAQs
What are the tools required for data parsing?
These are the leading data extraction tools used for extracting data seamlessly.
● Nanonets : Intelligent Document Automation Platform
What is a parsed file?
When a string of commands, usually a computer program, is fragmented into easily processed components and then analyzed for correct syntax and made easily readable, it is called a parsed document file.
Why is parsing necessary?
Data pressing makes the complicated or even impossible to understand files into simple, readable ones. For example, reading data in raw HTML can be challenging for you. A data parser will convert it into a more readable format, such as plain text.
How is data parsing done?
Like conventional parsing, where we take a sentence, break it down into different parts of speech, and then identify the grammatical relationships between the words to interpret the sentence, data parsing analyses symbols and data structures.
It uses Natural Language Processing (NLP) and structures the information from data sets by organizing it and determining its meaning.
Why do you parse Data?
Data parsing is when you take some unstructured data and transform it into other file formats such as JSON and CSV and add more structure to the given information. This way, we turn unreadable data into more comprehensive and easy-to-understand.
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Update: The post originally published in July 2022 is updated in August 2022.
