Data parsing is used for crawling information from large datasets and structuring it in a way humans can understand. Traditional data parsing is done on HTML files where the parser converts HTML text into readable data. However, not all parsers work the same and there are distinct differences in parsing technologies. There are numerous benefits of data parsing for businesses ranging from automated data extraction, improved visibility, cutting costs, and boosting employee productivity. But parsing doesn’t stop there, and today we’ll dive into what it is all about.
Data parsing is a process in which a string of data is converted from one format to another. If you are reading data in raw HTML, a data parser will help you convert it into a more readable format such as plain text. Not all the information is converted during the parsing process and programs have their own sets of rules when it comes to parsing information.
In short, a data parse program is used for converting unstructured data into JSON, CSV, and other file formats and adds structure to said information.
In the field of computer programming, the definition of parsing is to analyze a string of symbols, special characters, and data structures using Natural Language Processing (NLP). When you define extracting in parsing, it refers to structuring information from data sets and giving it meaning by organizing it, based on user-defined rules.
Parsing has different definitions for linguists and computer programmers but the general consensus is that it is used for analyzing sentences and mapping semantic relationships between them. In other words, you define extracting information from files and filtering through them as parsing.
Data parsing takes two approaches when it comes to the semantic analysis of text- grammar-driven data parsing and data-driven data parsing. An important aspect of parsing is to capture information from data in a way that it fits contextual structures.
Here is how these two approaches work:-
Grammar driven data parsing means the parser uses a set of formal grammar rules for the parsing process. The way this works is sentences from unstructured data get fragmented and transformed into a structured format. The problem with grammar-driven data parsing is that models lack robustness. This is overcome by relaxing the grammatical constraints so that sentences outside the scope of grammar rules can be ruled out for later analysis. Text parsing is a subset of grammar parsing and assigns a number of analyses to a given string. It resolves disambiguation problems faced by traditional methods of parsing as well.
Data-driven data parsing uses a probabilistic model and bypasses deductive approaches of text analysis often used by grammar-driven models. In this type of parsing, the parsing program applies rule-based techniques, semantic equations, and Natural Language Processing (NLP) for sentence structuring and analysis. Unlike grammar-based parsing, data-driven data parsing employs statistical parsers and modern treebanks for obtaining broad coverage from languages. Parsing conversational languages and sentences that require precision with domain-specific unlabelled data fall under the scope of data-driven data parsing.
What does a parser do? It extracts data from documents, gives structure to it, and filters details.
Data parsing is used by different industry verticals to convert information into electronic formats from documents. The following are the most popular use-cases of parsing in industries:
Data parsers are used by companies to structure unstructured datasets into usable information. Businesses use data parsing for optimizing their workflows related to data extraction. Parsing is used in the fields of investment analysis, marketing, social media management, and other business applications.
Banks and NBFCs use data parsing to scrape through billions of customer data and extract key information from applications. Data parsing is used for analyzing credit reports, investment portfolios, income verification, and deriving better insights about customers. Finance firms use parsing for determining interest rates and loan repayment periods post-data extraction.
Businesses that deliver products/services online use data parsers to extract billing and shipping details. Parsers are used for arranging shipping labels and ensuring the formatting of data is correct.
Lead data is extracted from real estate emails by property owners and builders. Parsing technologies are used for extracting data for CRM platforms and process documentation in order to forward to real estate agents. From contact details, property addresses, cash flow data, and lead sources, parsers are very beneficial for real estate companies when it comes to making purchases, rentals, and sales.
A common question that keeps cropping up when document processing in organizations is whether or not you should build your own data parser. Custom text parsing software built for in-house teams is definitely tailor-made to meet specific parsing requirements within organizations.
However, the downside is that the whole staff has to be trained on how to use it. The costs of building a custom parse program can be steep since more time and resources are needed. Additionally, these solutions require a lot of planning and need their own dedicated servers for faster parsing. If you’re migrating systems, they may not be compatible with new technologies and will require upgrades.
The ideal scenario is to use a data parser that is compatible with legacy systems and designed for various use-cases. Docsumo’s data parser gives you complete control of your data extraction and is designed to work with all types of businesses, be it startups, enterprises, or large-scale organizations.
Data parsing makes information accessible for organizations and allows it to be read more easily. The converted data can be shared across clients efficiently and parsers are designed to make business operations agile and scalable by nature. With a good parser, much of the manual work involved in data extraction and cleanup gets automated and its importance cannot be understated.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.