Picture this: you’re faced with a seemingly endless stack of paper documents and told to enter them into your database. By hand. Four hours tick by and you’ve barely made a dent in the pile.
You’ve got a meeting in 30 minutes and a critical project to complete after that, all before you’re out for a week on vacation. But Janet from accounting needs these documents by the end of the day. When is this going to get done?
Let’s face it. Manual data entry is the stuff of nightmares. It’s the ultimate time-suck for you and your employees. And Heaven help you if you actually have to go back and find a singular piece of information amid the thousands of PDFs on your server. That’s enough to make a full-grown adult break down and cry. There’s got to be a better way.
Thankfully, there is.
Meet Data Capture
Essentially, data capture allows organizations to easily convert paper documents into digital, business-ready data. Digitally transformed documents are easily readable, editable, and searchable. Thus eliminating valuable man-hours spent sifting through and updating paper files.
What makes data capture possible? Machine learning, a branch of artificial intelligence (AI), is the key. This division of AI focuses on creating programming systems that are able to make decisions like humans, operating on self-learning algorithms and improving their processes without being repeatedly programmed. The goal of an application using machine learning is to reduce human involvement and help predict future outcomes based on past data trends.
It’s the same technology responsible for advancements such as self-driving cars and voice recognition technology. And, it’s here now to help you avoid endless hours of document processing.
Here’s a brief look at how.
Machine Learning and Document Transformation
Possibly the biggest benefit of data capture is saving time, effort and errors by turning paper documents into easily accessible, classifiable, and searchable online files. How?
Information extraction via supervised learning essentially infers an outcome based on a set of pre-programmed examples called “training datasets.” In human and animal psychology, this idea is called “concept learning” and allows us to distinguish different objects, events, and ideas from one another.
Through the use of supervised learning, machines are able to analyze documents and identify the type they are – PDF, image, text, spreadsheet, etc. They are then able to extract specific data and automatically index documents, linking to patients or projects based via language processing technologies.
While this document identification and processing can be done entirely without human intervention, active learning can be employed when resources for employing machine learning are limited.
Active learning is when a computer is only able to use a limited number of training datasets (usually due to budgetary restrictions). As the machine learns over time, these processes will eventually be completed without human intervention.
After documents are scanned, properly programmed machines have the ability to detect fraudulent information. The machine will analyze a document and ask the user if it classified the file and/or extracted the data correctly. Then, based on user feedback, it will refine its document processing rules until the task can be done without user assistance.
A whopping 72% of SMB decision-makers state that technology solutions can significantly improve business outcomes and/or help them run their business better. Machine learning is most certainly one of these technologies.
The scalability, customizable architecture, and easy integration of machine learning makes adopting this technology to improve company processes a no-brainer (machine learning joke, anyone?).
We specialize in making technology advancements easily accessible, helping businesses everywhere process documents faster and share them more securely. If you are interested in learning how your organization can harness machine learning to improve your business outcomes, reach out.