Whether it’s receipt scanner software or a full-service data extraction program, good data capture will always benefit from machine learning. With an adaptable approach to template creation and document types, data capture can change the way your business operates.
Here at Xtracta, we believe in the power of learning software. That’s why we’ve developed our data capture software to give businesses a versatile, adaptable, and intelligent companion in the data collection process.
So, why is data capture is made better by machine learning and how is it pushing the field forward as a whole? Find out below!
What are the elements of a good data capture program?
Good data capture software may have many unique qualities that sets it apart, but there are four key elements that provide the foundation for a solid program in this space.
Versatility
A good program can work for various industries, and across a wide variety of applications within those industries.
For example, good data capture software can adapt to read everything from a real estate contract to a set of divorce papers. While there is a lot to be said for creating unique programs for these applications, a single program able to adapt to many industries is perfect for implementation in a larger piece of software, e.g., Xero.
Learning Ability
When it comes to data extraction, one thing that has held many companies back is the necessity of templates. For each new document type fed into a data capture program, a new template must be created so that the program can understand the document’s format. This process takes up time.
With a learning program, on the other hand, new document formats are created based on the software’s experience with similar documents in its past.
Accuracy
Going back to review and redo is another time chewing part of the process, and one that’s prone to human error. A learning data capture program minimises time spent rectifying mistakes, allowing that time to be better spent elsewhere. This also guarantees accurate reporting, leading to decisions made based on trustworthy data.
Efficiency
Finally, a good data capture program must optimise the business into which it is implemented. If it takes more time to manage the system than it does to input the data by hand, then the system is inefficient and unnecessary.
How Machine Learning Contributes to Data Capture
Machine learning is defined as the ‘use and development of computer systems that are able to learn and adapt without following explicit instructions’ (Oxford Languages).
In other words, it’s a system that can interpret the data it’s presented with, extrapolating from the information to find more efficient ways of completing a task. In the case of data capture, this approach is extremely valuable, especially in large-scale businesses.
Each of the four tenets detailed above are supported by the presence of machine learning. Machine learning enables you to “set and forget” your automated data extraction software, leaving it to learn from every document processed, every instruction given by a user, and every new piece of data.
Thanks to its ability to improve automatically through experience, enhanced data capture systems such as these are highly adaptable to different industries, require minimal interruption by their users, deliver accurate data, and operate with extreme efficiency. Human error is almost entirely negated, and with minimal input from the user, programs like Xtracta can optimise administrative portions of your business with ease.
Talk to the Xtracta team about integrating a machine learning approach into your business’ administrative pipeline.
Put your staff’s time and effort where they are most needed and minimise time spent on administrative tasks. From invoice scanning and data capture to receipt OCR, our AI-enhanced data extraction software is perfect for large-scale companies looking to minimise cost and maximise accuracy. Discover more benefits and get started by talking to one of our friendly staff members today.
