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Data Parsing
Natasha Gomes

What is data parsing, we can simply put is converting unstructured data into a specified data format. When data needs to be processed for the desired outcome, converting a string of data to be compatible with current requirements is essentially parsing.

For example, parsing would be implemented if an organization wants to review all its stationery expenses in a year by combing through invoices in different currencies. Different regions have specific formats of depicting numeric data which can affect how the software interprets them. For accuracy, only numerical values without any special characters (commas, periods) will be extracted for further processing. This conversion of a string of data into a format readable by a machine is one of the applications of parsing.

Another simpler application of what is data parsing would be converting a raw HTML file that is difficult to interpret into a readable text file. In today’s age where companies deal with huge bucketloads of information, understanding and interpreting data can pose a major challenge. Industries such as Healthcare, E-Commerce, Banking, and Finance, require instant access to customers’ information that humans can comprehend in real-time. What is data parsing provides an effective solution to these problems.

How Parsing Data Adds Value

In a digital-first world, most people’s interactions with businesses occur online. From a company’s perspective, only hoarding customers’ data doesn’t add any value. The unstructured data must be converted into a structured intelligible form for computers to interpret and process, which humans can then understand.

Moreover, incorrect data attributable to human error or incompetency can have enormous repercussions. In 2013,  US organizations were hit with over $7 billion in IRS civil penalties due to incorrect income and employment values reports. The same report shares insights that 27.5 percent of working professionals reported incorrect data had been manually entered into their enterprise software at their workplace.

Though this case alone makes a convincing argument for the importance of parsing, there are more benefits to it than from a legal viewpoint.

Fast and Accurate

Employees don’t have to put in countless hours sorting through manual data to retrieve insights useful to the business. Not only it is a painstakingly intensive process, but a waste of time and resources. Parsing techniques provide the same outcome in a fraction of the time and with higher accuracy.

Cost-Efficient & Low Investment

A parsing tool is a relatively low investment compared to the alternative of having a team only sorting through data. A software or skilled developer crew performing parsing as one of their functions can yield better results and over time create more impact.

Parsing itself creates easily understood information, hence no specific skillset or extra training is required to read the parsed data.

Greater Visibility

One of the main applications of parsing is to simplify data and understand the big chunks of information an organization may have. Incoherent data can be transformed into intelligible insights that can help multiple business functions – marketing, sales, finance, and even HR.

Data Parsing’s Relevance in Today’s Market

Most industries require parsing data in some shape or form. Real estate, Financial Services, and the up-and-coming Food Delivery sector rely on well-structured client data for their operations. Transforming data into a readable format for easy access is a game changer for most businesses. Parsing gives companies a competitive advantage over their peers as there is no trade-off between manpower constraints and staying cost-effective.

Extracting Valuable Information from Emails

For an organization receiving several hundred emails daily, going through every email to filter its relevance – important communication or junk email – and obtaining essential information from the message is quite a tedious process. It is also immensely time-consuming with a high possibility of human error.

Parsing data can scan emails without opening each email thread by searching for keywords that are of interest. It can condense this information into relevant chunks and provide a greater understanding of what’s being communicated without having to read every email. A parsing solution relieves the ineffective method of manual labor by providing the required data from the email to the user within minutes.

Tailored Marketing Campaigns

Businesses receiving a high volume of customer data through their platforms cannot realistically look through the data of each user to make informed decisions. Parsing can provide a remedy by pulling required information from customer data to be grouped into similar persona sets. This allows business to customize their marketing campaigns and target individual customers based on their profile, likes, and dislikes, reaching their desired audience demographic. Alternatively, the same solution can be used for product data to perform analysis and competitor benchmarking.

Financial Reporting

The business and finance sectors require accountants to sift through customer data and develop realistic financial models. A data parsing tool would meet the requirements of scraping and extracting useful data, used by accountants to analyze and create financial reports. As any error in the data can completely skew the reports, parsing provides a full-proof solution to manual sorting.

Scanning Resumes

Another useful and important application of a parsing solution is to scan the curriculum vitae (CV) of candidates for a job listing. Recruiters can be drowned in resumes for a single available position, making it increasingly difficult to review each one and select suitable candidates for interviews.

Parsing alleviates their burden by utilizing specific requirements to look for candidates who are a close match. These requirements can be certain skills, experience, and education, defined by the recruiter to refine the results and only select talent meeting the criteria.


For companies interested in web crawling to obtain real-time data that can optimize their business strategies, parsing would be one of the solutions implemented in the process. The applications of such a tool support a wide range of requirements and industries. Parsing solutions can improve overall efficiency and provide reliable data for analysis.

When integrated as a part of the web crawling process, the results can be optimized to serve the business needs and goals of your organization. To know more about our web crawling software, please get in touch with

Frequently Asked Questions

What do you mean by data parsing?

Parsing is the process of taking input data in one format and transforming it into a more readable, structured format that is easier to process and analyze. Essentially, parsing involves breaking down data into smaller elements and organizing these elements in a way that adheres to certain rules or formats. This process is crucial in data processing workflows, especially when dealing with large volumes of data or data from disparate sources.

Key Aspects of Parsing:

  • Syntax Analysis: This step involves checking data against a predefined syntax and understanding how the data is structured. It is common in programming where compilers parse code to check for syntax errors and to understand the program structure.
  • Data Conversion: Parsing often includes converting data from one format to another. For example, extracting data from a JSON file and converting it into a CSV format.
  • Data Extraction: Parsing can be used to extract specific data from a larger dataset. For example, extracting prices and product names from an HTML document of an e-commerce site.
  • Error Checking: During parsing, the data can also be checked for errors. If data does not conform to the expected format, the parser can highlight these errors, which can then be corrected or handled appropriately.

What is an example of parsing?

A common example of parsing is the process of reading and extracting data from a webpage. When a web scraper collects data, it parses the HTML code of the page to find specific information such as product descriptions, prices, and reviews. This involves breaking down the HTML structure into manageable parts to identify the exact pieces of text that correspond to these elements. The parsed data is then often reformatted into a structured format like a CSV file or a database, making it easier to analyze and use in various applications. This process exemplifies how parsing can transform raw, unstructured data into organized and accessible information.

What are the steps in data parsing?

Parsing is a structured process that involves several key steps to efficiently transform raw data into a usable format. Here’s a breakdown of the typical steps involved in parsing data:

1. Data Collection

The first step in parsing data is to gather the raw data that needs to be parsed. This could be from various sources such as web pages, files (like CSV, JSON, XML), databases, or APIs.

2. Data Inspection

Before parsing, it’s crucial to inspect the data to understand its structure, format, and quality. This helps in planning how the parsing should be carried out and identifies any potential issues like data corruption or format inconsistencies.

3. Defining the Parsing Rules

Based on the inspection, define the rules or use existing algorithms that determine how the data will be parsed. These rules depend on the format of the data (e.g., JSON parsing rules will differ from CSV parsing rules) and the target format or structure you need.

4. Actual Parsing

Using a parsing tool or software, the data is processed according to the predefined rules. This involves extracting and transforming the data elements from the raw data. For structured data like JSON or XML, this might involve traversing nested fields. For unstructured data, this could involve applying regular expressions or text processing techniques to extract information.

5. Data Transformation

Once the data is extracted, it often needs to be transformed into a format suitable for storage, reporting, or further analysis. This could involve converting data types, renaming fields, or reformatting the data into tables.

6. Validation and Quality Checks

After parsing and transformation, it’s important to validate the parsed data to ensure it meets the required quality standards. This might include checking for data completeness, accuracy, and consistency. Any errors detected at this stage need to be corrected, which may involve revisiting earlier steps.

7. Storing or Outputting the Data

The final parsed and validated data is then stored in a database, data warehouse, or other storage systems, or it might be outputted directly for use in applications, such as in a report, a data visualization tool, or as input to another software system.

What is data parsing in ETL?

Parsing data in the context of ETL (Extract, Transform, Load) is a crucial step within the “Transform” phase of the process. ETL is a data integration process used to gather data from various sources, transform the data into a suitable format, and then load it into a database, data warehouse, or other storage system. Parsing plays a key role in ensuring that the data from these different sources is correctly formatted and standardized before it is stored or analyzed. Here’s how parsing data functions within each phase of ETL:


During the extraction phase, data is collected from various sources, which could include databases, CRM systems, flat files, APIs, or even unstructured data sources like web pages. The data at this stage often exists in different formats and may not be immediately suitable for processing or analysis.


This is where parsing data is most prominently involved. During the transformation phase:

  • Parsing: Data is parsed to interpret its structure. For instance, if the data is in JSON or XML format, parsing involves breaking down the nested or hierarchical structure into a more manageable and flat structure suitable for database storage.
  • Normalization: Parsing helps in normalizing data, which involves converting it into a common format that is consistent across all data sources. This might include standardizing date formats, numeric formats, or string formats (like ensuring all phone numbers or postal codes follow the same pattern).
  • Data Cleaning: As part of parsing, data is also cleaned which involves removing or correcting inaccuracies, filling missing values, and deleting unnecessary information.
  • Data Integration: Data from multiple sources might be merged. Parsing ensures that data being integrated aligns correctly, such as ensuring that different naming conventions or value ranges are standardized.


After parsing and other transformations are complete, the data is loaded into a target data store. This could be a data warehouse, database, or a data lake, depending on the needs of the organization. The parsed and transformed data is now in an appropriate format for analysis, reporting, or further processing.

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