What is data parsing?

Data parsing is converting data from one format to another. Widely used for data structuring, it is generally done to make the existing, often unstructured, unreadable data more comprehensible.

For example, suppose a user views an HTML file that is likely to be challenging to read and comprehend. Data parsing will help convert that into a more readable format, such as plain text, which the viewer can easily understand.

The process is used in various industries ranging from finance to education and sports to retail, to extract relevant information without spending manual hours to get the correct data.

The need for data parsing

Similar to natural languages, computers often require translations to communicate effectively. Therefore, to help machines understand a data string that they do not recognize or comprehend in the current format, parsing is used to convert the data to a form that the device can understand and act on. It is similar to providing a translation so an English speaker can understand text in another language.

Data parsing is generally required to change unstructured and illegible data strings to structured and simple sets that a computer can easily understand.

Time and cost savings

Data parsing allows companies to structure data in a better way to ensure that access and readability are improved. As data is parsed, the workforce can understand it faster and save time executing their duties. As billable hours reduce, the organization can save on the cost of hiring and payroll.

Enhance visibility

Data parsing helps businesses improve visibility. Since the data is converted to a more legible format, the user interface is switched to a more friendly format. This allows users to view all necessary information, reducing the odds of missing any key data points.

Understanding the process of data parsing

Data parsing is performed with the help of a parser. A parser acts as an interpreter for the computer and is a tool that is used to break a string of data into smaller pieces. These smaller pieces of data are then separately analyzed and given the desired structure that the user wants. For example, an HTML parser can be used to analyze data in an HTML file, divide it into smaller pieces, understand the requirement of the details and then convert it into a more readable format such as CSV.

Use cases of data parsing

Some data parsing use cases are seen broadly across all industries and industry-specific uses.

Data parsing for emails

In today’s business landscape, the go-to channel for professional communication remains email. Today’s most critical and vast sets of information are shared through emails. However, as the number of emails and the length of the thread increases, it becomes increasingly difficult to make sense of the information that is being communicated.

Therefore, businesses use parsing to get a greater understanding of the data being sent over emails. Data parsing can help extract and condense relevant information from emails and replace the manual work needed. With the help of keywords and specific commands, a data parsing solution can help businesses extract all the required data from their email having to open the use of keywords and clear commands; a data parsing solution can help companies to remove all the needed data from their email without opening each thread individually.

Data parsing for professional resume and curriculum vitae processing

Recruiters receive scores of resumes and CVs each day from people vying for an opening at different firms. However, when many people are vying for only a few positions, it becomes tough for companies to segregate suitable candidates for the job. Although CVs may be structured in a reader-friendly manner, the very act of going through each one individually makes the process arduous and time-consuming, making the process lengthy and inefficient.

For this, recruiters and HR professionals use data parsing. It allows them to sort through resumes based on specific criteria, including keywords the firm is looking for. Keywords can be specific skills, qualifications, talent, or certifications that would make the candidate ideal for the job. Data parsing allows recruiters to create filters in their recruitment process only to accept applications from candidates who fulfill specific requirements or stand out from the rest of the group in specific particular ways.

Data parsing for investments

The investment world is full of information coming in from multiple sources. The stock market, bank rates, earnings, and currency affect the investment world significantly. Therefore, investors need to go through vast amounts of information to react to changes in the market in real-time. Even the smallest of delays can cause severe financial losses to investors.

Data parsing allows investors to analyze massive amounts of data and get all the required information in a more legible format. This reduces the manual work for investors and analysts and helps them analyze such quantities as would be impossible to comprehend by humans. Consequently, investors can save time and cost of operations and get ahead of the market.

Data parsing for market analysis

Almost every industry in the world is composed of multiple players competing against each other to get a more significant market share. Since the market is constantly evolving and consumers' preferences regularly change, businesses are always trying to catch up with the latest trends to get an edge over their competitors. However, with billions of customers worldwide, the amount of consumer data generated is too large to be analyzed manually, making it difficult to identify patterns and get relevant information necessary for strategic decisions.

Data parsing allows businesses to understand the market better by helping them extract necessary statistics from immeasurable amounts of data. Such statistics help companies identify trends in the market, get a sense of consumers’ behavior, and understand how the competitive landscape is changing. Parsing helps companies make moves in real-time to change with the market itself.

Understanding what a data parser does

A data parser is a tool used to execute the function of parsing. It is a program that understands the entity’s requirements and converts the format of the data based on the commands fed into it.

A business can build or acquire a data parser based on the market, industry, and enterprise needs.

Building vs. buying a data parser

Some companies prefer to build a parser in-house. This is usually done when the company has proper infrastructure and talent, typically a full-fledged IT department with programmers capable of developing IT tools.

Building a parser is often preferred because the organization building it has complete control over its workings. This allows them to tweak the parser to fulfill the firm's specific requirements. Further, it helps them build highly specialized solutions that perform highly efficiently in particular business environments.

Buying a parser is generally ideal for companies that do not have an in-house team or do not wish to deploy those IT teams to work on creating a tool. The market has plenty of options for data parsing, and an ideal solution for virtually any organization can be found.

Buying a parser gives companies the flexibility to choose from various available options. Further, these solutions are built by experts specializing in creating such tools. Also, these solutions are diverse and able to perform in multiple work environments.

There are benefits and challenges when building and buying a parser, and an organization needs to understand which option is ideal for their situation.

Development cost

One of the primary concerns of building a parser in-house is the cost. Further, creating a data parser may require the company to make additional hires, build infrastructure, and train employees.

On the other hand, buying a parser can be a cost-effective solution. Since many options can be found in the market, purchasing a parser is simpler and more affordable. Buying a parser also includes customer service support, as against building a parser which requires additional costs to maintain since the development was done in-house, adding to the charges.

Time to market

Time is another critical factor when factoring in the difference between buying and building a parser. Building a parser in-house requires several internal efforts such as research and development, infrastructure development, hiring, and testing. This causes companies to spend much time before the data parser is even ready to be deployed. Dual costs have to be borne for companies that develop a parser to replace a purchased one.

Buying a data parser, however, is generally much faster. Since the process only involves identifying the right solution and making the purchase, it saves much time for the company. Further, in case any errors are placed in the parser, a customer support team can be contacted for a purchased parser. However, when a parser is built in-house, any errors in the parser have to be dealt with in-house, which may take much more time.

Control and specialization

A central argument made by the proponents of building a parser is control of the solution. When companies create a parser, they can develop the solution in ways that are most suitable to the work environment of the enterprise. However, when companies buy a parser, they generally purchase a standardized solution built to function in multiple settings.

A purchased parser cannot provide the specialization and customization that a built parser offers. Further, when companies create a parser, they can adjust its functioning as the business evolves. The solution is only changed for a purchased parser when the developer sends any updates, making it more rigid than built parsers.

Which parser is the right solution for businesses?

Small-sized businesses

Small businesses usually have a smaller, minor team and fewer resources. Therefore, building a parser could significantly impact the organizations regarding the development cost. If small companies still choose to develop one in-house, it can be sub-par owing one in-house; it can be sub-par due to the lack of a large pool of talent, creating severe security issues, usability problems, or flawed parsing.

Therefore, for smaller companies, it is recommended to purchase a parser. Since the market has many solutions available, small companies are likely to find a solution that suits their needs.

Medium-sized businesses

Medium-sized companies fit in a zone where they may or may not require building a parser. This entirely depends on the amount of data the company has to deal with and the size and quality of the in-house IT team.

Should a business feel the need and have the resources to develop a solution in-house, they may do it. This is especially true in cases where they require a highly specialized solution that cannot be found in the market at a reasonable price. In cases where the business is looking for an affordable solution, the parser can be purchased.

Large-sized organizations and multinationals

Large businesses typically have larger IT teams and quality talent. These teams are composed of highly qualified personnel capable of delivering complex solutions. Therefore, larger companies should generally consider building their tools in-house.

Due to the organization’s scale and the number of resources available. This gives them greater control over the solution and helps them get highly specialized solutions to fulfill the particular needs of the organizations. Since the organization is large and frequently geographically diverse, maintenance and development are likely to cost less than purchasing a parser due to economies of scale.

Data parsing is a great way to draw critical pieces of information from a complicated data set. The process helps businesses become more efficient, reducing manual efforts with automation. This saves time and cost for organizations and provides more accurate results, eliminating the perils of human error.

Data parsing diagram