An incredible amount of 3.5 quintillion bytes of data is created every single day in 2023. Pretty mind-blowing, right? This data is essential to the growth of organizations, as it makes people’s life easier, resolves problems in organizations and drives innovation.
However, there is a problem: most data is stuck in unstructured formats such as scanned documents or handwritten papers. This makes it pretty much impossible for businesses to use data effectively.
What makes it challenging is that businesses need these raw data files and transform them into other formats to pass them on from one software to another. To do so, they need to find a solution that makes data accessible for all kinds of entities. This is where data parsing comes into the picture.
At this point, data parsing might feel like an abstract concept to you. This is why in the next paragraph, we will explain what data parsing is, continue with presenting the different types of data parsing and clarify why data parsing is so essential.
What is Data Parsing?
Data parsing is the process of converting data from one format to another. For example, let’s say you have a PDF file, and you would need it as a JSON file. In this case, you would need a data parser that can parse raw PDF data into a machine-readable format.
In general, parsing of data is applied as the next step after data has been extracted from a document. Most of the time, extracted data is in one format and needs to be converted to a different format, so that it can be saved in your database or passed on to third-party software.
The conversion of one file format to another is possible with the help of a sub-field of AI, called Natural Language Processing (NLP), in which a string of symbols, special characters, and data structures are analyzed. Based on user-defined rules, information is first structured and then organized, which gives the extracted data meaning.
Important to keep in mind is, that depending on the contextual structures of the extracted data, different data parsing approaches can be applied. Let’s have a look at how these different approaches work.
Various Types of Data Parsing
Generally, data parsing takes two different approaches: Grammar-driven data parsing and Data-driven data parsing.
Grammar-driven data parsing
As the name suggests, grammar-driven data parsing bases the parsing process on a set of formal grammar rules. This works by fragmenting sentences from unstructured data and then transforming them into a structured and easy to understand format.
Nonetheless, this approach has one problem, it lacks robustness. To overcome this issue, grammatical restrictions are often eased. That means that sentences that don’t fall within the scope of the usual grammar can be excluded from the data parsing analysis.
As grammar-driven data parsing has its limitations and inconsistencies, an additional way of data parsing was found. This is where data-driven data parsing comes into play.
Data-driven data parsing
In general, data-driven data parsing makes use of smart statistical parsers and modern treebanks to cover as many languages as possible. This allows you to parse conversational languages and sentences that demand high precision, even though they are unlabeled and domain-specific.
Note: A treebank improves NLP models, so that an AI software is able to comprehend written text. The statistical parser can make use of the NLP model, to understand the possible different meanings within a sentence, and returns the most likely one.
In data-driven data parsing, two approaches can be performed:
- Rule-based approach
- Learning-based approach
The rule-based approach is suitable for structured documents such as tax invoices or purchase orders. The defined rules help the user to determine a template that is used as a reference for the parser to extract data from a document.
The major disadvantage here is the strict reliance on pre-defined templates, which means that even a slightly different document format will lead to a data parsing failure. So what could be a way to parse data more flexibly?
The answer is: A learning-based approach to data parsing. This approach relies heavily on Machine Learning (ML) and Natural Language Processing (NLP) and is generally used to extract data from any kind of document.
Because the model is trained with a diverse set of unstructured documents, the ability to easily recognize important fields and extract data from them is improved.
In practice though, a combination of both, rule-based and learning-based approaches, are used to perform data parsing. This combination allows you to process any document with any kind of layout, and doesn’t limit you to one layout only.
With this in mind, let’s have a look at how data parsing is used in different industries.
Use Cases of Data Parsing
Data parsing is used in several industries to convert data trapped in unusable formats into business-ready data. For readability purposes, we will focus on four industries only, but keep in mind that this list is far from exhaustive:
- Financial Industry
- Transportation & Logistics
Banks and other financial institutions are dealing with millions of customer documents such as ID cards, bank statements, and onboarding applications. All these documents need to be analyzed, and relevant information stored in the bank’s database.
Similarly, any kind of business is dealing with invoices and receipts that are often manually processed and saved in different formats (PNG, PDF etc.). This makes it very difficult to search through any data and therefore work with them efficiently.
To improve financial processes, a data parser can be used in the following cases:
- Automated data entry
- Customer onboarding
- Document completeness check
- KYC automation
- Automated invoice processing
- Converting PDF to Excel
- Extracting Data from PDF
Don’t worry if your case is not listed here. There are many more use cases for the financial industry.
The healthcare industry is often confronted with a shortage of resources, long working hours, and enormous administrative tasks. This can quickly lead to mistakes in patient records, follow-up treatments and prescriptions, which translates into severe harm or even death of the patient.
Additionally, the patient onboarding is packed with all kinds of documents, which forces healthcare employees to spend a lot of time on putting data from forms into computers.
In the healthcare industry, a data parser could be useful in the following cases:
Lawyers are expensive, which means law firms definitely want them to use their time to solve cases instead of sorting through endless amounts of documents. But because lawyers receive all kinds of documents from clients in various formats, they spend a lot of time sorting through them. This makes them very inefficient and slow.
Additionally, lawyers serve several clients at the same time. Therefore, it is essential that all documents are properly organized and classified. Otherwise, it is almost impossible to keep an overview and track of the different cases.
On top of that, most customer documents entail sensitive information that need to be protected from data breaches and fraud.
In the case of the legal industry, data parsing can come in handy in the following ways:
- Data collection & organization
- Document classification
- Automated data extraction
- Anonymization of information
Transportation & Logistics
Any business that sells products or services online needs to deal with a large amount of shipping and billing information. Therefore, shipping labels, packing slips, proof of delivery, etc. need to be managed.
Here, a data parser can be used in cases like:
- Automated data entry
- Compliance checks
- Automated invoice processing
- Document fraud detection
- Package management
Looking at these different use cases, it becomes obvious that data parsing is beneficial for several industries. By automating data parsing, the process can be improved and made even more efficient. Let’s have a look at how data parsing can be automated.
How to automate Data Parsing?
Nowadays, you are most likely forced to reduce time, human effort and expenses for your business anywhere you can. To achieve this, automation seems to be the only solution. Like seen in the presented use cases, data parsing itself already brings great benefits such as business workflow optimizations. In order to improve data parsing though, we can automate the process.
Let’s have a look at the different ways to automate data parsing:
- Classic OCR Software
- Web Applications
- Robots & RPA
Classic OCR Software
Classic OCR software is a rather simple solution to automate processes. It has all basic functions and instructions to get the job done. But its features are limited.
Therefore, a classic OCR software is usable for smaller files and to convert a simple PDF to JSON for example. However, tasks like parsing through tables or reading through images can’t be performed, as they require more powerful libraries, which consume more computing power and data.
Web applications are often used for user interfaces (UI) to automate the data parsing process. To operate on certain types of files, a specific backend language such as Python or Java is chosen. All communication between the UI, backend, and other databases happens mainly through the database.
If the website is operated on a powerful cloud solution, OCR can be integrated to perform data parsing procedures. Nevertheless, this solution might be time-consuming as it conducts many steps and requests all across the web.
Robots & RPA
Robotic Process Automation (RPA) is one of the latest developments that enable automation. Instead of humans conducting manual tasks, robots take care of automating those tasks. They are equipped with intelligent algorithms that enable them to learn and minimize errors with every iteration. This is why RPA is used in accounting.
One of the main advantages is that these robots can be connected with different data sources, APIs, and other third party integrations, which allows you to parse data differently.
Now that we talked about how data parsing can be automated, let’s have a look at the benefits of data parsing.
The benefits of data parsing
Next to the most significant advantage of data parsing, being able to navigate through a tremendous amount of data, more benefits apply:
- Saving time → Data parsers help businesses to convert data into another format and automate the process that would otherwise be done manually. The result is that business operations are run faster, and that human resources can be used for more valuable tasks.
- More accessible data → Data parsing makes data more accessible and increases searchability. Business professionals are able to access all information necessary out of the huge amount of data at hand.
- Modernizing data → It can be the case that stored data of businesses is years old and therefore, not available in modern formats. But this data might still contain valuable information that is needed for the business. Data parsing can quickly change the format of this data and allow businesses to use the information effectively.
After going through what data parsing is, in which cases it is used and which benefits it can bring, you might be wondering how to get access to a data parser. One option could be to build your own parser. But is that really a smart idea?
Building your own parser or not?
In order to answer the question, we will walk you through the pros and cons of building your own parser. After this, you should be able to make an informed decision.
Pros of building your own parser
- Gives you more control → You are more in control and can decide how to update or maintain your data parser. On top, if you are dealing with very sensitive data, you might prefer to not share your information with third-party data parsers.
- Customizable according to your needs → When building your own parser, it is specifically customized for your company. That way, it helps in-house teams meet your organization’s specific parsing requirements.
Cons of building your own parser
In general, to build your own parser, you will need a team of developers that has the ability to understand and write parsing application. Finding developers with these necessary skills can be quite the challenge. But this not the only difficulty. Let’s see what other cons of building your own parser apply:
- Expensive → Building your own parser is expensive, as a lot of time and resources are required. On top of that, you will have to hire and train a whole in-house team to build your custom parser.
- Staff training → You will have to train your entire staff how to use the data parsing technology.
- Maintenance → A data parser requires regular maintenance, which means you would have to spend more time and money.
- Infrastructure → Building a data parser needs a lot of planning and its own dedicated servers. This means, you might need to build or buy a powerful server that is fast enough to parse information.
For most organizations, the cons outweigh the pros, simply because it is expensive and extremely difficult to find experienced people to build a parser. If that’s the case, there is no need to despair. We have another option for you. You can empower your organization with a data parser that has been built by thousands of developer hours.
Data Parsing with Klippa
Klippa is one of the companies that can be used to parse data from any kind of document. In order to parse data, an Optical Character Recognition (OCR) software is needed.
Klippa DocHorizon, our AI-based OCR software, can be used to parse data from any kind of document your organization needs to process. With OCR technology, you can accurately extract relevant information from unstructured data formats and convert that data into your desired format.
Next to that, DocHorizon can classify document types, verify and anonymize data, all the while eliminating manual data entry. Out of the box, DocHorizon already recognizes a wide range of documents in more than 100 languages.
Do you want to transform your data that is stuck in unusable formats to business-ready data? We would gladly show you how to do that with our solution. Just book a free demo down below or contact one of our experts.