Data parsing is the process of converting data from one format to another. Widely used for data structuring, it is generally done to make existing, often unstructured and 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.
This process is utilized across various industries ranging from finance to education and sports to retail, extracting relevant information without spending manual hours to obtain the correct data.
The Need for Data Parsing
Similar to natural languages, computers often require translations to communicate effectively. 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 into structured and simple sets that a computer can easily process.
Time and Cost Savings
Data parsing allows companies to structure data more effectively, ensuring 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, organizations can save on hiring and payroll costs.
Enhanced Visibility
Data parsing helps businesses improve visibility. Since the data is converted to a more legible format, the user interface becomes more friendly. This allows users to view all necessary information, reducing the chances 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 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 requirements 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, while others are industry-specific.
Data Parsing for Emails
In today’s business landscape, email remains the go-to channel for professional communication. The most critical and vast sets of information are shared through emails. However, as the number of emails and the length of threads increase, it becomes increasingly difficult to make sense of the information being communicated.
Therefore, businesses use parsing to gain a greater understanding of data sent over emails. Data parsing can help extract and condense relevant information from emails, replacing the manual work needed. With the help of keywords and specific commands, a data parsing solution can help businesses extract all required data from their emails without opening each thread individually.
Data Parsing for Resume and CV Processing
Recruiters receive scores of resumes and CVs each day from candidates vying for openings at different firms. However, when many people are competing 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, going through each one individually makes the process arduous and time-consuming.
For this reason, 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 to only accept applications from candidates who fulfill specific requirements or stand out from the rest of the group in particular ways.
Data Parsing for Investments
The investment world is full of information coming in from multiple sources. Stock markets, bank rates, earnings, and currency fluctuations affect the investment world significantly. Therefore, investors need to process vast amounts of information to react to market changes in real-time. Even the smallest delays can cause severe financial losses.
Data parsing allows investors to analyze massive amounts of data and get all required information in a more legible format. This reduces manual work for investors and analysts and helps them analyze quantities that would be impossible for humans to comprehend. Consequently, investors can save time and operational costs while staying ahead of the market.
Data Parsing for Market Analysis
Almost every industry in the world is composed of multiple players competing against each other for greater market share. Since the market is constantly evolving and consumer preferences regularly change, businesses are always trying to catch up with the latest trends to gain an edge over 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 market trends, get a sense of consumer behavior, and understand how the competitive landscape is changing. Parsing helps companies make moves in real-time to adapt 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 data based on the commands fed into it.
A business can build or acquire a data parser based on 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 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 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 their 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. These solutions are built by experts specializing in creating such tools and are diverse enough 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. 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, whereas building a parser requires additional maintenance costs since the development was done in-house.
Time to Market
Time is another critical factor when considering 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 considerable time before the data parser is even ready to be deployed.
Buying a data parser, however, is generally much faster. Since the process only involves identifying the right solution and making the purchase, it saves significant time for the company. Further, in case any errors occur in the parser, a customer support team can be contacted for a purchased parser. However, when a parser is built in-house, any errors must be dealt with internally, which may take much more time.
Control and Specialization
A central argument made by proponents of building a parser is control of the solution. When companies create a parser, they can develop the solution in ways 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. For a purchased parser, the solution is only changed when the developer sends updates, making it more rigid than built parsers.
Which Parser is the Right Solution for Businesses?
Small-Sized Businesses
Small businesses usually have smaller teams and fewer resources. Therefore, building a parser could significantly impact the organization in terms of development cost. If small companies still choose to develop one in-house, it can be sub-par due to the lack of a large talent pool, creating potential 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 so. 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, purchasing a parser makes more sense.
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 particular organizational needs. Since these organizations are large and frequently geographically diverse, maintenance and development are likely to cost less than purchasing a parser due to economies of scale.
Conclusion
Data parsing is an excellent way to draw critical pieces of information from complicated data sets. The process helps businesses become more efficient by reducing manual efforts through automation. This saves time and costs for organizations while providing more accurate results and eliminating the perils of human error.