'Automated workflows' might seem like a buzzword, but it stands for significant value amongst the companies dealing with tons of paperwork. Paperwork that just keeps piling up.
What if the minutest details of your invoices or contracts could magically find their way onto a computer, automatically falling in place to form a cohesive structure you desire?
Well, this magic (and more) is exactly what automated data capturing is about.
In this article, we're going to discuss how to extract valuable information from a sea of noisy documents and use it to optimize your operational workflows.
Data capture involves collecting organized or unorganized information and converting it into data that can be read and processed by a computer.
Essentially, you extract information from digital or paper documents and convert the captured data into a structured form so that it can be further utilized for storing, editing, and processing.
Data can be captured in two ways - a) Manual data capture and b) Automated data captureLet's evaluate the pros and cons of traditional vs. modern data capturing techniques:
Manual data entry or document capturing is the timeworn method of gathering information from various sources and entering it into a computer or manual files by hand. Even in the digital era, surrounded by technology, we can argue that manual data entry has its upsides.
The most obvious being how setting up a manual data collection process is easy and allows businesses to keep their data entry costs at a minimum.
Further, it raises employment opportunities, as even individuals with basic qualifications can perform manual data capturing. Another positive is that some documents are not intelligible for computers. For example, the muddled script of an old handwritten letter can only be deciphered by the human gaze.
Certain company documents are highly classified or sensitive, and it is preferred that these be handled under human supervision.
However, as is true with most age-old methods that have been replaced by technology, manual data entry is prone to errors and can be time-consuming, especially if you're dealing with a copious amount of data collection.
Also, dynamic business environments demand that data be at your fingertips at all times. With manual data entry, it can be challenging to look for important information amidst a huge pile of documents.
Moreover, utilizing data further to gain insights can seem convoluted since information look-up in itself can be a tedious affair.
Automated data capture is the process of consolidating data and converting it into electronic files with the aid of tools and software such as Optical Character Recognition (OCR). The artificial intelligence of these tools, combined with machine learning, allows them to read files quickly and translate their content into handy digital files.
It is needless to say that computer software can collect and store data at flashing speed without committing any errors.
Moreover, if your document capturing solution provides cloud storage, you can easily save the collected information online without worrying about data losses. Also, it'll be much more convenient to access all the data as it'll simply be a search-and-click away.
Data entry automation solutions are always attractive due to the value and utility they offer. However, this often comes at the cost of your data's safety. Any information, once it is online, can be accessed by a cybercriminal willing enough to go through the pains of stealing it. This is why companies these days make relentless efforts to safeguard information.
Furthermore, automated data capturing solutions might not be practical or economical for businesses that don't have a lot of data to work with or are looking to cut costs.
Nevertheless, most businesses will find that data entry automation is more lucrative than risky. So let's delve deeper into how automated document capturing works.
Out of a range of data capturing technologies available today, let's explore the ones that revolutionized the landscape:
OCR extracts texts from documents, images, or files by scanning them. The information collected is converted into a machine-readable form so that it can be further used for processing, editing, recording, etc.
The technology essentially identifies text and characters inside images (for example, the image of a scanned document) and then translates it into a digital format.
In simpler terms, it is a form of data entry automation where information is directly pulled from scanned images and stored in a computer with the help of an OCR device. There is no manual data entry involved.
Another common example would include scanning a paper document and converting it into a PDF or Microsoft Word document and storing it on a digital device to edit or refer to later on.
Intelligent Character Recognition is a subset of OCR which specifically scans documents with handwritten text, identifies data from complex handwriting styles, and translates it into a computerized format.
Though ICR is essentially a branch of OCR, it is considered to be more advanced, as it deals with handwriting recognition which is much more intricate and detailed.
No two handwritings are the same. An ICR software needs to scan, read, interpret, and commit to memory a myriad of handwriting patterns.
It contains an AI-based self-learning system called the neural network. When this system is introduced to a new document, it automatically learns the unique font, style, and pattern of the script on that document and updates its database with the knowledge. This helps the software predict new types of handwriting patterns with more accuracy.
ICR technology is continually evolving, as there are always new patterns to be learned and more precision to be achieved.
Modern-day businesses use ICR software to draw out information from hand-filled forms and save it digitally.
Document processing is not a new discipline for many businesses. It is the process of capturing information from documents (digital or physical) and storing that information on a computer to draw value and insight from it.
Much like manual data entry, manual document processing can't be relied on for smooth and precise data extraction. Human errors are common, and processing hundreds of documents for information verification, further analysis is a challenging feat to achieve, no matter how efficient your workforce strives to be.
Intelligent document processing automates all tasks involved in securing data from documents. And wait, the 'intelligent' in IDP is not just for automation. IDP technologies also extract unstructured or semi-structured information and categorize it for easier analysis. Meaning, you can retrieve organized information in minutes and focus on the next step in your workflow.
IDP consists of five steps:-
This step involves document capturing. Technologies like OCR, ICR, OMR, etc., are used to scan physical documents. In the case of e-documents, inbuilt integrations of an IDP solution are used to import data.
The IDP software pre-processes the captured document to improve its quality. For example, it will straighten the scanned image of a hand-filled invoice or increase its brightness if it is underexposed. This ensures that the data captured is readable and accurate.
The IDP platform will identify the information in the captured documents and classify it accordingly. For example, a bank account application usually contains a set of documents which include a form, identity proofs, residence proofs, etc. IDP will recognize each document for what it is and send it in the appropriate workflow.
Next, required data is pulled from classified documents and is entered in the pertinent database for further use. The type of data being extracted is also described in this step- names, numbers, addresses, etc.
All the retrieved information is verified for authenticity and accuracy. IDPs employ external databases and glossaries to validate collected information and check for discrepancies. Any inaccuracies found are sent for human evaluation and corrections. This step also helps IDP software to update their database and improve their AI algorithms.
Let's look at a few common use-cases for automated data capture to understand its relevance in today's world.
Accountancy firms benefit from automated data capturing heavily by:
Insurance companies need to collect a lot of information from their customers.
Automated document capturing helps these companies by:
Data capturing technologies can efficiently handle large amounts of banking data by:
HR is another discipline that heavily relies on data for day-to-day operations. Through document capturing solutions, HRs can:
There's a lot to consider while choosing a document capturing solution. Prioritize the following and pick a solution befitting your needs:
Your data entry solution shouldn't have a steep learning curve. Of course, working with document capture solutions need some kind of assistance from the vendor in training and educating about its features, but the training should be so smooth that even a non-technical background person can easily be able to handle it.
Don't fall for 100% error-free claims. Data capturing technologies are always evolving and learning from new patterns or past processing mistakes. A solution that promises an accuracy level between 95-98% is considered to be at par with industry standards.
The success of a document capturing solution is centered not just around its accuracy rate but also its ability to validate data automatically. If all fields have to be evaluated manually for discrepancies, then it kills most of the basic objectives of buying into a data entry automation software- ease of use, time-saving, minimal human intervention, etc.
Cheap rates don't necessarily mean that the services offered are subpar, and enterprise-level pricing doesn't guarantee top-drawer experiences. Keep this in mind while choosing your document capturing solution, and measure your cost against the value/features offered.
Valuable and sensitive data is always vulnerable to cyber attacks. Your solution should follow all the prescribed protocols like GDPR compliance, OWASP practices, end-to-end encryption, limited admin access, etc. Also, look for data storage policies that allow you to do away with old data, which is sensitive, but no longer useful.
Docsumo guarantees a 50% increase in efficiency and a 70% reduction in processing costs. Let's see how:
Pull clear details from structured, unstructured, or semi-structured data swiftly. Docsumo's APIs are pre-trained for common document types like forms, invoices, bank statements, and identity cards.
You can classify documents conveniently without having to open individual PDFs or images. Docsumo's intelligent classification sorts large documents into their corresponding types intuitively, without you having to write custom rules.
Its easy-to-follow interface allows you to keep track of all the documents your customers have submitted with clarity without needing to reach out to them for confirmations.
Docsumo categorizes all data points and table line items using NLP. You can gain an insight into the properties of the captured data and utilize it for processing or analysis at the later stages of your workflow. Moreover, Docsumo turns disorganized information into valuable details that can help you make data-driven decisions with certainty and confidence.
You can verify captured information with Docsumo's external APIs and vast database and identify mistakes easily. Docsumo facilitates entity matching across documents to ensure that the information your customer has entered is correct. Moreover, you can check for the legitimacy of the information procured as Docsumo is quick to detect document fraud such as incorrect metadata, font changes or, added layers.
Integrations are important to streamline your operation workflows, which is why they are an inherent part of Docsumo's interface. You can use plug-in APIs and input and output connectors to easily synchronize the platform with other essential software like Zapier, Webhooks, Salesforce, Google docs, etc.
Docsumo is GDPR compliant and follows all protocols stipulated by OWASP. All server requests are transferred over HTTPS, and data exchanges are encrypted with AES 256.
For your satisfaction, Docsumo gives you the option to delete data from its servers (instantly or periodically) once data processing is complete. Its advanced user management also empowers you to keep an eye on who can access your data.
Docsumo promises to achieve 98% accuracy during information capturing and processing in one go. It has considerable experience in working with clients in financial services, commercial real estate, accounts payable, lending, insurance, and logistics.
In this digital era, automation is not only a necessity, but most modern-day businesses are built on it. No one likes manual processing where it can be avoided.
Through intelligent data capturing, you can harness the power of the vast data available at your fingertips and use it to scale your business. Moreover, you can raise your team's productivity by clearing monotonous paperwork from their schedule and enabling them to contribute effectively.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.