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All about data parsing

Reading time:
5 minutes
All about data parsing

All about data parsing

Reading time:
5 minutes
All about data parsing

3.5 quintillion bytes. That's the amount of data created every day in the digital age. This data is vital to business development, enabling informed decision-making, performance monitoring, problem-solving and much more.

However, this data is often in unstructured formats, difficult to read and understand. This is where data parsing comes in. But what exactly is data parsing, what are its benefits, and what tools can you use to do the job?


What is data parsing?

Data parsing, or syntactic data analysis, is a method of structuring data (converting a virtually unreadable unstructured format into a structured format that can be easily exploited). This transformation is carried out with the help of data analyzers via libraries or APIs. These are used, among other things, to collect data from a raw source and divide and classify it into coherent, intelligible parts.

Let's take the example of an HTML file, with its numerous tags, which can be transformed into easily readable plain text using data parsing.

What are the different types of data parsing?

To perform a syntactic data analysis, experts can choose between two methods.

Grammatical analysis

As the name suggests, this technique uses formal grammatical rules as the basis for the analysis process. With this approach, sentences extracted from unstructured data are transformed into an easy-to-understand format.

Unfortunately, this solution is not always effective and lacks precision. Indeed, some complex sentences that don't follow the strict rules of standard grammar may not be supported. They are then simply excluded from the analysis. The result: the appearance of inconsistencies, which can distort data analysis.

When this technique isn't enough, professionals turn to another approach called data-driven data analysis.

Data-driven data analysis

With this method, languages used in everyday conversations as well as complex sentences are taken care of. This includes jargon that is specific to a given field, but which is not labeled. To perform the analyses, this approach relies on :

- Treebanks: AI software capable of understanding written text.

- Statistical tools: for in-depth analysis to understand the different interpretations of a sentence.

To apply this method, experts can choose between two options:

- Learning-based technique: uses machine learning and natural language processing or NLP. This method makes it possible to extract data from any document.

- Rule-based method: uses a reference model fordata analysis andextraction. This method is suitable for structured documents such as purchase orders or tax invoices.

NB: Learning-based and rule-based techniques can also be combined. This combination results in a more flexible and efficient system. It is therefore able to handle different formats, without being limited by a predefined model.

The different stages of data analysis

How is data analysis carried out? Here are a few steps.

1- Data collection

To begin the analysis, we first need to collect the data. This can be done by inputting data via an API or by importing a file (CSV, JSON, etc.).

Alternatively, data can be harvested directly from a reliable, well-constructed data source(data lake or datawarehouse) or through manual data entry.

2- Data breakdown

The collected data will be separated into several parts to facilitate analysis. This process identifies the rules that the analyzer will follow for conversion.

These rules can be established according to grammar (parsing). In this case, they must be determined according to language syntax and structure. Alternatively, the guidelines in question may be set according to the data collected (data-driven data analysis).

In doing so, the data is further divided into words, phrases or data structures. This essentially depends on the analysis technique chosen. And it's these elements that are called tokens.

Before proceeding with the analysis, further guidelines can be included to ensure that only relevant data is extracted:

- Selection of key information (e.g. numbers, names, etc.)

- Exclude unnecessary elements (e.g. punctuation or other special formats that pollute data)

- Organization of relevant information in a more orderly format (e.g. JSON file or table)

3- Analysis process

Depending on the directives, the data will be extracted and organized in a database or in a structured format (CSV, JSON, XML). This makes them easier to understand and use. The next step is verification and validation. These control processes are necessary to detect any errors or inconsistencies.

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The benefits of data parsing for transport and logistics

In today's connected world, data parsing is proving to be a great help to companies specializing in logistics and transport.

This new approach facilitates the management of invoicing and shipping data. It relieves companies in the sector of a number of time-consuming and tedious tasks, such as :

- Invoice processing

- Package management

- Checking the conformity of transported goods

- Identity verification (KYC)


Which tool for data parsing?


The Docloop interoperability platform uses state-of-the-art technologies(IDP, OCR, AI) to extract and process all kinds of data, especially in the field of transport and logistics, ensuring accuracy and efficiency even for complex documents.

Why Docloop and not elsewhere? This platform specializes in transport and logistics. It is trained to handle a variety of documents specific to this sector. As a result, errors during analysis are easily spotted.

Even hard-to-decipher mid-pages and large tables are perfectly within the reach of this platform.