Data capture solutions: Traditional OCR vs cognitive
We compared the two data extraction approaches so you don't have to. Read about the main differences and learn what the right solution for your company is.
What is data extraction?
Paying invoices, verifying packing lists, and keeping records for auditing, all of these business processes have one thing in common. These tasks, and many more, require documentation. In the old days, businesses kept thousands of paper documents in dozens of file cabinets that were each labeled to make the search for that particular document easier. In the age of digital technology, companies create and receive these same documents in digital formats.
While this may mean less physical paperwork for employees, it turns out that 90% of organizations continue to use manual methods to process documents like invoices. Knowing what data extraction is starts with realizing that every document in a business needs to have its data extracted and entered into the business system so that the data can be used for its purpose.
Requiring employees and data entry clerks to manually read and retype all of the data from the countless documents that a business receives is a flawed method. Not only are employees performing tasks that cause burnout, but the data itself is subject to human error, high costs, and long turnaround times.
How is data extraction done? For businesses that realize that manual methods are inefficient, the best way to extract data from their digital documents is with data capture software. Software for data extraction can automatically extract the data from business documents and input it into a table or other format for ease of editing.
What is a data extraction table? Simply put, it is a table where all of the data from a document is imported and organized. With a data extraction software that has AI capabilities, companies can extract data from documents automatically and with human-like accuracy. Rossum is an example of a data extraction tool that can relieve employees of the repetitive task of data entry and save businesses money.
Why is data extraction important?
Data extraction is the process of copying data from documents and entering it into a business system for the purpose of editing, using, and storing the data. You may be wondering: why is data extraction important? The answer is that data extraction is vital for businesses because they not only often need to edit data, but they need to store data properly for auditing purposes. While it is possible to keep a copy of documents and store those, this does not make it easy to get a complete picture of the data or to look at data across multiple areas.
For instance, if a business keeps documents for their purchases and does not perform data extraction, the business would have several documents for a single order, including the purchase order, invoice, receipt, quality inspection sheet, and more. If something goes wrong, and the company wants to check on its purchases over the course of the past twelve months, the business would have to retrieve dozens or even hundreds of documents from where they are stored. On the other hand, if the company used data extraction to store the data in a business system, they could check on the purchases for the last twelve months by simply pulling up a spreadsheet with all the data in it.
Data extraction techniques
In order to extract data from documents for editing and storage purposes, there are a few different ways to do it. Data extraction techniques vary depending on the goals of the extraction as well as the unique needs of the business. Many companies choose to follow the Extract, Transform, Load (ETL) process for data extraction. This step-by-step process describes how data extraction occurs. The business must extract the data, then transform it into the desired format, and load it into the business system. Techniques for performing this process include the use of software that can be implemented in the company.
The most efficient data extraction technique is to use an automated software tool that can accomplish each step of the ETL process. For the Extract stage, Rossum is a software that can extract data from all kinds of documents and can learn from human validation so that less involvement from employees is required over time. When it comes to the Transform step, Rossum can import the data into the format used by the business. Finally, Rossum completes the Load stage by automatically loading the data into the business system. The best data extraction technique can make the work easier, faster, and more accurate.
Types of data extraction
To fully understand data extraction, it is beneficial to know the difference between the various types of extraction. At the highest level, there are two primary types of data extraction. One is structured data extraction, and the other is unstructured data extraction. For unstructured data, such as data located in PDF files, images, and emails, more complex methods of data extraction are necessary than with structured data.
Extracting unstructured data may require the use of Optical Character Recognition (OCR) tools in order to make the data digitally editable. Instead of simply extracting the data and importing it into the proper format for the business system, unstructured data would need to be analyzed and reformatted before it could be used.
For structured data, the types of data extraction are broken down into two more areas: full extraction and incremental extraction. Full extraction refers to the complete extraction of all the data in the document. This is usually the type of extraction that is done when a completely new table or document is received. The second type is incremental extraction, which is a form of extraction that requires a logical process that can track changes in the data. Unlike full extraction, incremental extraction is used to extract only updated data without extracting everything.
Document data extraction
Businesses that receive documents must perform document data extraction. For business processes, companies extract data from several kinds of documents so that they can keep the business running smoothly. Organizations have to work with data from invoices, purchase orders, receipts, claims, and more, but data extraction for the purpose of record keeping can seem unnecessary.
The reality is that keeping data from all of these documents can be vital for the stability and improvement of the company. For example, the data in an invoice can reveal how much money was spent on specific goods a year ago in comparison to how much is being spent on the same goods today.
Companies that want to use their data to improve can create a data extraction form. A basic form will include a description of the data for analysis. Essentially, the data is collected and described so that it can be analyzed later on.
Document data extraction is a process that not only makes business processes easier but can reveal areas for improvement so that the company can become even more profitable. With AI-powered software like Rossum, document data extraction can become an automated process so that employees can focus on work that will grow the business.
Data extraction example
Data extraction is a process that can seem complex. Taking a look at a hypothetical data extraction example can be helpful for knowing how it works. Using the most complicated type of data extraction, unstructured extraction, assume that Company A receives a scanned PDF file of an invoice. The data contained in this invoice, the date, invoice number, company information, description of goods, price per item, and total amount, must be extracted and imported into the business system. Company A uses an Intelligent Document Processing platform, such as Rossum, to extract unstructured data from documents. Uploading the PDF invoice into the software, Company A runs the program and receives the extracted data in the chosen format or loads it into the business system.
In this hypothetical scenario, the unstructured data from the invoice is automatically extracted with very little human involvement. The data was imported into the proper format and can now be used by the business or kept as a record in a database. The only reason why this situation is simple is that the software, Rossum, employed by Company A is robust and capable of accurately detecting unstructured data as well as importing it into the corresponding fields in the business system.
Data extraction software
Rather than extracting data manually, many companies choose to utilize data extraction tools. These tools can range from coding libraries to comprehensive software packages. If your business is interested in hiring a developer or would like to create a program for data extraction, there are data extraction Python tutorials available online.
Another coding language that could be used to create a data extraction program is SQL. As with Python, finding SQL data extraction tool tutorials online is simple. The issue with these tools is that you either would need coding knowledge or a developer to create the tool, and the amount of time it would take to do this may cost the business.
Instead, companies can use data extraction software. Unlike creating a tool with code, platforms have been developed for businesses that need to extract data from day one. Rossum is an Intelligent Document Processing platform that was designed to extract data from business documents.
Unlike manual or template-based platforms, Rossum uses AI to read documents so that the platform will never create extra work. Additionally, with Rossum, companies can store their data in the cloud so that it can be accessed from anywhere. Data extraction should not be performed with manual methods but with software that can do the hard work.
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