The use of automated document data extraction can transform your business. It is quite easy to get started with, but realizing what it can do for your business might take a while.
Do you or your employees have to process hundreds, thousands or even millions of documents per month manually? Is this a process you would rather get rid of? You’re not alone. Luckily there’s an answer: automatically extracting data from documents. This speeds up the entire process.
Are you curious about how this works? Or do you want to familiarize yourself with an overall better understanding of the extraction of data?
In this blog, you will gain a better understanding of the meaning, the techniques, the process, the importance and you will get an example and an answer to the question: “What is data extraction?”
The Meaning of Data Extraction
So, what does it mean to extract data from documents? It basically comes down to retrieving various types of data from one or multiple sources. These sources are usually poorly organized and completely unstructured.
Extracting the data allows you to process, store and analyze the data even further elsewhere. Those types of data are typically used to improve the company’s operations. It is the foundation for doing a critical analysis in the decision-making process.
There are three forms of the extraction of data. Manual, automated and human in the loop (which is a combination of the first two).
Now that the definition of data extraction is clear, let’s continue with the importance of the process.
Why is the Extraction of Data Important?
Imagine you are a bank, giving out mortgages to home buyers. By law, you are obligated to do KYC checks, register the buyer’s income and probably more.
To do so, customers send in documents containing this information. This information has to land in your database, or decision-making system.
Sadly, the data is unstructured resulting in the fact that you need a back office team to identify the presence of information on documents, such as the salary on the payslip. Besides that, the information needs to be entered into your digital systems.
This is a costly, consuming, boring and tedious task, but it doesn’t necessarily have to be. In fact, many companies are taking advantage of automated extraction solutions and techniques, which are AI-powered, to manage the data extraction process from the beginning to the end.
The main advantages of using an automated extraction solution are:
- Improved accuracy
- Increased employee productivity
- Reduced cost
- Time-saving
- Scalability
- Faster turnaround time
Improved Accuracy
Replacing manual- with automated data extraction dramatically decreases the possibility of human errors. Therefore, it leads to overall improved accuracy.
If entering large amounts of data is a daily task for most of your employees, the chances are high that there might be some inaccuracies and errors due to human mistakes. Without any verification layer steps, data entry has an error rate of 4%.
Automating the process of extracting the data from documents, will lead to more accurate data overall. Improved accuracy not only leads to better business decisions, but it’s also very beneficial for employees. This leads us to the next advantage.
Increased Employee Productivity
When removing manual data extraction and replacing this with an automated tool, employees can spend more time on important tasks. Some tasks can only be done by humans. Thus, let your employees do those and the tasks that can be automated done by an automated data extraction tool.
Not only will satisfaction increase because employees are released from tedious tasks, employees can also focus on more meaningful tasks. This will again lead to improved satisfaction, which will (in the long run) lead to improved productivity.
Reduced Cost
By choosing a data extraction tool, your business can both save money in the short and the long term. In the short term, your company can already save a lot of money by reducing manual data entry errors. In the long term, your company doesn’t need to worry about scaling and financing a large team to handle your company’s data needs. Hence, automated data entry and extraction systems are on the rise.
Time-saving
Studies show that intelligent automation usually results in cost savings of 40 to 75%. Time is money, and thus it might be one of the biggest selling points of a data extraction tool.
Scalability
When a company is growing, the amount of incoming and outcoming documents is growing as well. If the extraction of data from documents is still done manually, the number of documents will pile up.
This can be avoided by switching to an automated system. As a result of that, the company can scale up without having to worry about large volumes of data laying around, or having to hire a huge workforce.
Faster Turnaround Time
Because of automated data extraction, turnaround times can go from days or weeks to seconds. If a human has to manually check a document, only one document per time can be done. Besides that, people can only work 8 hours per day.
Challenges
If there are advantages, there also must be some challenges regarding data extraction. Two challenges are:
- Security of sensitive data can be very challenging. An example of sensitive data is financial data. Therefore, security in data extraction must be ensured. It is important to only work with software solutions that can prove that their security is tested on a regular basis and that they can comply with GDPR and other legislations.
- Another challenge is the coherence of extracted data from several sources. The challenge is even bigger if these sources are both unstructured and structured since you still have to make sure they work well together. AI-powered systems can be trained to combine data and make them suitable for operations after processing.
Luckily, most data extraction solutions come with an extended technical assistance team to help you overcome these challenges. Now, let’s continue with the types of data that can be extracted.
Types of Data
Data can be classified according to the structure of the source:
- Structured data: The data source already has a logical structure. Therefore, it already is very convenient for extraction. You do not have to work or manipulate it before the data extraction process. Examples are CSV and XML files.
- Unstructured data: Most data exists in an unstructured form. Sources of unstructured data could for example be PDFs, scanned texts, web pages, emails or images. Unstructured data has to be filtered for sensible extraction of data. Examples could be removing white spaces, duplicate results and other “noise” that has to be cleaned up from the document.
Types of Data Extraction Techniques
There are two different techniques regarding the extraction of data: logical and physical extraction.
Logical Extraction
Logical extraction is the most widely used technique. It can be divided into two subtypes:
- Full extraction: All data is fully extracted at the same time, without the need for extra (techno)logical information. Full extraction is a method used when the data has to be extracted and loaded for the first time. It reflects the data which is available at that moment in the source system.
- Incremental extraction: Since the last successful data extraction (given by a timestamp), the occurring changes in the source data are tracked. These changes are then incrementally extracted and loaded.
Physical Extraction
If extracting data from expired or restricted data storage systems using logical extraction is difficult, applying physical extraction techniques is the only way to get this data. Physical extraction can be split into two types:
- Online extraction: There is a direct connection between the source system and the final archive. With the method of online extraction, the extracted data is more structured than the source data.
- Offline extraction: The actual data extraction takes place outside of the source system. In offline extraction processes, the data is either structured by itself or it will be structured through extraction routines.
Categories of Extraction Tools
Data extraction tools automatically extract data from the source. The type of service and the purpose are very important parameters. In order to understand which category of tools would work best for your company, you have to understand the difference between the three:
- Batch processing tools: Can be interesting for companies that need to transfer data from one to another location, but challenges occur. Challenges could be data stored in obsolete forms, or legacy data. Batch processing can also be helpful for companies wanting to move data on-premise or a closed environment.
- Open source tools: Are preferred for companies on a budget. They can acquire Open Source software to replicate data provided, or extract data. Open source tools are mostly sufficient for smaller sized companies.
- Cloud based tools: the majority of the available extraction tools nowadays are cloud based. Cloud based tools excel in fast, reliable data extraction. By using cloud based tools, companies no longer have to worry about compliance and security issues in house. Besides that, it eliminates time delays caused by batch processing.
There are many cloud-based solutions available in the market nowadays, such as Klippa DocHorizon. Klippa specializes in data extraction and can help you turn unstructured documents into structured data.
Data Extraction Example
So let’s see what an extraction solution can do for you. We are taking a passport as an example.
Let’s say your customer uploaded this passport on the left in a KYC process and you use data extraction software to get the information you need. For example, the full name, the document number and the MRZ.
Within 3 seconds the system is able to turn the unstructured image into the structured data as displayed on the right image below.
Klippa’s Cloud-based Extraction Solution
Klippa DocHorizon is an Intelligent Document Processing solution. The software we build is made to automate business processes that involve documents. Our solutions help increase productivity and efficiency, but also reduce cost and human errors.
Klippa offers a comprehensive cloud-based document data extraction solution, which helps companies automatically process any document type within a matter of seconds.
How Does the Extraction Process from Unstructured Documents Work?
But how is data extraction done? The process of extracting data from a document can be explained briefly in a couple of steps. The process described is how the extraction process works at Klippa.
1. Uploading the document
First, the paper document has to be transformed into a digital document. Usually, this is done by scanning the document with a mobile phone. It can also be done by uploading a file to the system. The input can be in multiple formats, such as JPG, PDF, PNG, TXT, and more.
2. Image to TXT
Now that the upload is finished, the actual data extraction can begin. The only problem is that the computer cannot read what is on the document or picture yet. Therefore, it has to be transformed into a TXT file. In order to do so, OCR (Optical Character Recognition) technology comes into play. This technology extracts all data from the document, but it’s not yet structured.
3. Parsing to JSON
In the final step, a parser is needed to read and understand the text on the file. The parser converts the TXT file into a structured JSON file. After the conversion is finished, the data can easily be processed in the database. Besides JSON, other outputs such as XML, XLSX and CSV are also possible. Our OCR API is very flexible.
4. Verify the extracted data with third-party sources
Optionally, we can verify the extracted data with third-party sources. This could be your own database, but also Chamber of Commerce databases and anti-money laundering lists. This ensures the data quality is good and in line with regulations.
Data Extraction API
The data extraction solution above is being used by companies around the world and in varying industries. Examples of industries are financial services (e.g. in KYC processes), retail (e.g. loyalty campaigns), accounting, customs, and healthcare.
Of course, you could try and build a complete extraction pipeline yourself, but that is complicated and time-consuming. It is also costly to maintain, and often the ROI of building it will be very bad compared to using an existing service.
Therefore, implementing a third-party API for data extraction on documents is a good choice. Through our API, the solution can be integrated into any existing software. Therefore, the data can be extracted directly into the software.
Get in Touch with Our Specialists
If you are looking for a way to increase productivity, improve accuracy, save loads of time, enable scalability and reduce cost, Klippa’s extraction solution is the right choice for you.
Would you like to know more about the extraction process, the technique, and the method we use? Get in touch with one of our experts, or schedule a free online demonstration through the demo form below.