Data Parsing to Extract Meaningful Information From Data Sources
As the amount of data available on the web increases, managing and organizing data becomes more difficult for organizations. However, converting the extracted data into a readable and correct format is crucial for accurate analysis and better decision-making. For this reason, parsing data is critical.
In this article, we will dive into data parsing, and its most common use cases, and discuss in-house and outsourcing data parsing solutions.
What is data parsing?
Data parsing is the process of converting data from one format to another in order to better understand and use it (see Figure 1).
How does data parsing work?
Each data parsing tool has its own predefined rules, it crawls text sources using its pre-written code, and then takes input data from large datasets. It builds a data structure (e.g. a parse tree or other hierarchical structure that provides a structural representation of the input data) based on predefined rules. Parsing data process includes two phases:
- Lexical analysis is the first step in parsing data. It takes raw data as an input, then scans and reads the source code in order to convert it into meaningful tokens. A token is a form of data that is splitted into its smallest units from a large unit of data. Each of the sub-units is called token. A sequence of tokens is sent as output to the syntax analysis.
- Syntax analysis takes the tokens generated by the lexical analysis as an input. In this step, the syntax analysis generates a parse tree to show the hierarchical structure between the components.
Figure 1: The process of data parsing
Why do companies need data parsing?
After data extraction, the collected data may be in different formats, e.g. structured, semi-structured and unstructured as it is gathered from different data sources. To extract value out of data, organizations need to analyze semi-structured or unstructured data. However, analyzing a large amount of unstructured data is a complex and old-school method. For this reason, converting extracted data into a readable and correct format in order to store it in your database is an important step for further analysis.
Top 4 data parsing use cases
1. Data parsing for processing unparsed HTML data into a readable format
2. Data parsing for data-driven email marketing
Organizations receive emails that contain critical data such as leads, contact details, and so on. As the number of emails increases, it becomes more difficult to keep this important information in an email database.
A data parsing solution eliminates the need to manually copy data from email lists into another document or application. Instead of manual data entry, a data parsing solution automatically extracts relevant information from emails, downloads the extracted data, and sends it to Excel, Google Sheets, or another application. It converts unstructured data information into usable data to gain a better understanding.
A data parsing solution scans email lists to find important data that can be extracted and fed into the CRM system. It ensures a clean and up-to-date email marketing database and eliminates excess data. This way, CRM managers can easily search for and reach target audiences.
3. Data parsing for resume data analysis
A data parsing solution helps recruiters to sort out resumes by specific criteria or keywords and extract the desired information. Then, the information is stored in a database with a unique entry for each application for further analysis. A recruiter can search the database by keywords and get a list of relevant applicants.
Data parsing solutions help recruiters and HR teams by enabling them to:
- Organize resumes without wasting time.
- Identify the most suitable resumes for their company.
- Extract all relevant information and organize it according to the needs of the recruiter.
4. Data parsing to get rid of paper-based financial reporting
The financial sector benefits from data parsing as it automates the extraction and aggregation of financial data. The growing number of accounts and customer data makes it difficult for accountants to track information and produce financial reports. Parsing data can offer businesses in the finance sector many benefits, including:
- Automatically scrapes and extracts key information of customers.
- Eliminates manual errors and automates the financial reporting process. Employees have more time to focus on analysis.
Building your own parser vs paying for a parsing solution
Building your own data parser:
- You can build your own parser in any programming language you like.
- You can make it compatible with any tools, such as a web scraper bot, that you already use. This reduces the potential integration problems.
- Building your own parser can be cost-effective if you already have a development team.
- If you don’t have a development team, building your own tool might be too expensive as it requires more maintenance and staff.
- You’ll need to buy and build a new server to run your own parser.
- Your parser needs constant maintenance. If you are parsing sensitive data, you need to ensure the security of your server.
Buying a data parser:
- It is low maintenance to use an existing parser.
- There are no additional overhead expenses such as human resources, servers, etc.
- Since it has been previously tested and adjusted to the needs of the market, you are less likely to encounter problems.
- Limited control over the entire work process.
- There may be integration problems with your existing tools.
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For guidance to choose the right tool, check out our data-driven list of web scrapers, and reach out to us:
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