Key Takeaways
- Manual data parsing is time-consuming and error-prone, while traditional OCR needs help with accuracy.
- AI-driven tools like Parseur overcome traditional limitations with advanced OCR, machine learning, and automation.
This article will walk you through data parsing, why it’s crucial for modern enterprises, how it works, common use cases, and what to consider when choosing a data parsing tool. We’ll also introduce you to Parseur, an automated data extraction and parsing platform to streamline your workflows.
What is data parsing?
There are many definitions of data parsing, as it is a vast concept, but we’ve tried to simplify it for you.
Data parsing is the conversion of data from one format to another, usually from unstructured data (raw HTML) to structured data (JSON, CSV). It converts the data into a more readable format.
A data parser helps to parse data and transform it into any format you need. However, only some data parsers work similarly; some have specific parsing rules to follow.
Definition of parsing
In a programming language, data parsing refers to analyzing data and structuring it according to specific rules.
Wikipedia defines parsing as "the process of analyzing a string of symbols, either in natural language, or data structures.
Parsing breaks this information down into meaningful components (such as names, dates, addresses, amounts), typically in a structured format like CSV, JSON, or XML.
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.
Types of data parsing
Data parsing can be categorized into 2 different types or approaches such as:
- Grammar-driven
- Data driven
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 rule-based methods and languages such as natural language processing (NLP). Regular expressions (regex) are used to identify and extract data based on recurring patterns and they are suitable for semi-structured data like logs or emails.
Why is data parsing important?
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.
Speed
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.
Accuracy
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.
Data modernization
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
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 Services
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
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.
The manual challenge in data parsing
Manual data parsing is still prevalent in many organizations, but it comes with significant challenges:
- Time-Consuming: Employees can spend up to 40% of their time manually collecting and preparing data.
- Prone to mistakes: Manual data entry can cost businesses an estimated $3 trillion annually in the U.S. alone.
- Lack of scalability: As volumes grow, manual processes become unsustainable.
Why does traditional OCR fall short?
While traditional OCR is effective at recognizing text, it struggles with:
- Complex layouts: Inconsistent document structures can confuse basic OCR systems.
- Error-prone outputs: OCR often requires extensive manual validation.
- Limited contextual understanding: It cannot interpret the meaning behind the extracted data.
AI in data parsing
Artificial intelligence (AI) transforms data parsing by overcoming the limitations of traditional methods:
- Advanced OCR algorithms: AI-powered tools accurately detect and extract text from scanned documents or images.
- Self-learning models: Machine learning allows AI parsing tools to adapt to new formats and improve performance over time.
- Integration with automation: AI-powered tools can seamlessly integrate with CRMs, ERPs, and other tools, ensuring a smooth data workflow.
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
What to look for in a data parsing tool?
When selecting a data parsing solution, keep the following criteria in mind:
- The tool should offer an intuitive interface, enabling non-technical users to quickly set up templates or rules.
- The tool should handle larger workloads as data volumes grow without compromising speed or accuracy.
- Your solution should parse multiple document types (emails, PDFs, images, spreadsheets).
- It should integrate seamlessly with CRMs, ERPs, database systems, or analytics platforms, ensuring a smooth data flow across your tech stack.
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
Wrapping up
There has never been as much information trapped in documents as there is today. In the modern business landscape, data parsing has become a pillar of efficiency, enabling businesses to unlock valuable insights and drive informed decisions. With AI at its core, tools like Parseur make data parsing more accessible, accurate, and faster than ever before, transforming the way organizations handle their data.
By automating the parsing process, Parseur eliminates the manual effort and guesswork, helping businesses streamline workflows and improve operational efficiency. There’s never been a better time to harness the power of automated data parsing.
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.
FAQs
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
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