Data Parsing

Every business-minded individual knows the value of accurate and clearly understandable data. Thanks to the information age, acquiring massive amounts of relevant data is easier than ever before, but that doesn’t mean it’s usable just yet. An important part of preparing data for utilization by your business is data parsing.

Once you’ve collected your desired data, such as through web scraping with the help of a reliable proxy, you need to wrangle it, but you’re still not done there. Sure, you’ve cleaned and homogenized that information, but data parsing will help you break it down and prepare it for analysis so it will better serve your business.

What is Data Parsing?

Data parsing is when a string of data is analyzed and separated into its constituent parts. This way, it may be converted into another, more easily understood, format. A common example would be changing raw HTML into plain text. This is important for taking that cleaned and consistently formatted data post-wrangling, and turning it into human-usable data relevant for your company.

While handling large datasets, this invaluable process can aid numerous aspects of your business. Whether it’s automatic data extraction, a means of cuttings costs, improved information visibility for your analysts, or just overall boosting general productivity.

Defining Parsing

Parsing has similar, but varied, meanings when discussing it from a computer science vs a linguistic approach. If you have any programming history, you may be familiar with compilers parsing code into machine language. Alternately, you may have experience with parsing as a grammatical exercise during your time spent studying sentence structure.

Linguistically speaking, parsing is taking a line of words and separating them based on formal grammar rules to better understand the exact meaning of the sentence. You can manually use techniques such as sentence diagrams to determine the relationship between each element of the sentence.

In computer science, parsing is using a program to break a data string into a data structure such as a parse tree or an abstract syntax tree by using Natural Language Processing (NLP). The result can be used to show the syntactic relation between each of its parts. Then, you can convert and re-arrange it as needed. Alternately, you can use parsing to extract specific parts out of the input, rather than forming a full parse tree.

Data Parsing Styles

You can use two methodologies in data parsing: grammar-driven and data-driven. Knowing what type of data you are working with is integral to deciding which approach you should take. Now that you know what they’re called, let’s go over them in detail.

Grammar-Driven Data Parsing

Grammar-driven data parsing, like the name implies, is a parser that utilizes a set of formal grammar rules to guide its process. It operates by taking unstructured data, separating it into appropriate fragments. Then, it rearranges it into a structured format following a pre-determined ruleset. 

Unfortunately, the main issue with this methodology is its lack of flexibility. Data that is originally written in conversational human language often has a rather ambiguous structure to it. This is often challenging to process for a computer program that is following a strict ruleset. Although, relaxing the initial set of guidelines can largely circumvent this problem, and you can then remove any remaining outliers. 

Data-Driven Data Parsing

Data-driven data parsing, or data-oriented processing, operates via a statistical model. This leads to the difference from grammar-based parsing wherein it will evaluate the frequency and probability of certain rules within the generated parse tree. It will calculate every potential parse. Then, it will compute each of its probabilities to derive the most likely final parse of a sentence.

Due to data-driven data parsing’s higher flexibility, conversational language-focused data is better handled in this fashion. A common application is Machine Translation, such as Google Translate, or as an auto-translate feature on some social media platforms like Facebook.

A major downside to this approach is the time, effort, and resources that go into setting it up and then maintaining it. For instance, the need for a corpus, which is a large and stringently structured set of manually annotated data. Machine Learning code requires training data before it is otherwise usable.

Data Parser Utilization

I gave a few examples along the way, but, let’s go over a few common use cases for data parsers. Though, by no means are the following categories the only business aspects that benefit from data parsing.

Workplace Optimization

As stated previously, you can use data parsers to give structure to unstructured data to make it readily usable. With these tools, businesses can optimize any form of workflow that is based on data extraction. This can range from things like investment analysis, social media management, marketing, internal test data, and so much more.

Finances and Accounting

After scraping through customer databases, companies can parse through this data to extract key information. You can use it to do things like quickly analyzing credit reports, creating investment portfolios, or determining income verifications. Similarly, you can use it to calculate things like interest rates and loan repayment periods.

Shipping and Logistics

Businesses that distribute products or services online can use data parsers on their customer information database to extract shipping and billing details when needed. Not only will it pull only the relevant data, but it can also handle any necessary formatting adjustments needed for creating shipping labels.

Customer Relationship Management

First, you build a database with customer information from various communication channels such as email, live chat, your website, scraping the web, and social media. Then, you can parse through to analyze your target demographic. Also, you can accurately estimate how to cater to their needs, greatly increasing customer satisfaction. After all, a happy customer is much more likely to be a loyal one.

Conclusion

Data parsing makes any business’ data easy to organize and use. While it not only provides expedited results on information-specific queries, it provides flexible scalability in this data-driven information age. With how rapidly trends rise and fall, fast access to relevant data in real-time is the edge you need to stay ahead of any competition.

With the importance of accurate data from numerous sources, you can start web scraping with the help of a reliable proxy. But, it’s only once you’ve data wrangled it and then parsed it into a useful format that it will truly shine. Now that you know about the benefits of data parsing, it’s time to start collecting data for your own business. Choose a proxy today to help you gather it.