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What Is Data Parsing?
Updated · Aug 22, 2023
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Data parsing is a method of converting data into a more structured and readable format. It does not only make this certain format easier to read or use but also improves its quality.
The most common example of data parsing is converting HTML from web pages into JSON or readable plain text.
With 95% of businesses considering it a need to navigate market trends and make informed decisions, the importance of data parsing can’t be stressed enough.
Data parsing methods have undoubtedly become indispensable for all modern industries. Continue reading to learn more about data parsing and its uses.
🔑 Key Takeaways
Data Parsing Definition
In plain terms, data parsing is converting raw, unstructured data to a readable format.
With the massive amount of data created daily, technologies come in handy to manage large datasets in ways people can understand.
Consequently, businesses and organizations can use tools to boost productivity and improve in general.
Unlike data extraction, parsing does not just gather information from various sources. This process actually organizes it and gives it meaning.
Data parsers can be built in many programming languages and are not limited to anyone. What’s important is a data parser’s specific purpose for converting any data type.
👍 Helpful Articles:
Data can come in different file formats like HTML, JSON, and CSV. Here are compatible tools, techniques, and programming languages to parse unstructured data:
There are two components in the data parsing process: lexical analysis and syntactic analysis. Here’s how they work:
This is when the data parser scans the data input (for example, an HTML file) character by character, trying to recognize every bit of information to gather “tokens.”
It is also the phase where duplicate codes and whitespaces are removed.
The recognizable “tokens” are sent for syntactic analysis to detect grammatical errors in the source code (input data).
Powerful parsers may also include semantic analysis that makes sense of the structured tokens and provides output accordingly.
Types Of Data Parsing
Data parsing comes in two types. These are grammar-driven data parsing and data-driven data parsing.
Let’s take a look at each one.
Grammar-driven Data Parsing
Grammar-driven data parsers train a set of formal grammar rules for structuring data. Sentences with unstructured data are broken up into a structured format.
This data parsing type can be limited, as it may rule out anything outside the set rules. Most of the time, these set rules are eased to make the process more inclusive.
Data-driven Data Parsing
On the other hand, data-driven data parsing uses statistical parsers and modern treebanks, which gives it broader coverage than a rigid grammar-rule approach.
It uses statistical methods to decide the most probable parse of a sentence, hence the word “data-driven.” More powerful parsers prefer this approach.
Uses Of Data Parsing
It is physically and mentally impossible for a human being to process all information the Internet has collected. This increases the benefits of data parsing, not limited to any industry. It is even harder to think of an industry that does not use data parsing methods in its business processes.
Here are just some of the use cases for data parsing:
75% of consumers now use social media platforms when looking for new products and services.
Large data sets from consumer behaviors are best collected and analyzed with data parsing methods.
Dealing with them manually only slows companies’ decision-making for each significant trend change. Not to mention that this is close to impossible nowadays since data that forms market trend information is considered "big data.”
📈 Market Trends:
A Business Intelligence trend for 2022 reported that 1 in every 3 companies will adopt decision intelligence by 2023 to grow more in the market.
Even small-scale businesses will have to deal with thousands of emails at some point. A timely business communications assessment can only be done through data parsing methods.
Like Google Search data, there is a way to filter search results by relevance using data parsing tools.
These tools are used to sort out relevant emails through keyword inputs. Moreover, it is worth mentioning that lead generation tools that collect emails from prospects also use data parsing methods.
Multiple volumes of files are sitting in every company’s cabinets and databases. The only reasonable way of processing them is through data parsing methods.
Some data parsing tools utilize OCR (Optical Character Recognition). This is used for parsing hard documents and PDFs.
Benefits And Challenges Of Building Data Parsers
You will eventually have to decide whether to build your parser. If not, you should consider popular data parsing tools. Here are some of the benefits and challenges that you must consider in this regard:
Benefits Of Data Parsers
Challenges Of Data Parsers
Generally, small and medium-scale businesses with no in-house developer team can purchase one from trusted providers.
Bigger businesses should consider building a parser if the complexity of their information needs requires it.
Businesses in most modern industries utilize parsing methods in one or more of their internal or external processes.
Building your parser or not is a question that has to be dealt with at some point in consideration of your company's resources. A good parser makes all the difference, saving you time. It can also give you an edge over your competitors.
Why is data parsing important?
Raw and unstructured data cannot be fully utilized. Using data parsing methods can save you a lot of time in data processing while ensuring relevant information is collected.
What is an example of data parsing?
It is most commonly used in web scraping when HTML is converted to more readable JSON or plain text. Data parsing methods can also be used to process hard documents or PDFs using OCR.
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