We’re all seeing less paper. Many industries still use it but more and more business documentation is digital… and there’s a lot of it. These digital images contain valuable information that can provide business insights and aid in decision making. The first step in this process is always document indexing. It’s the process of understanding what it is so an organization knows what to do with it, how to treat it, and what information it contains.
So why would any organization continue to apply manual processes to the indexing of digital documents? As strange as this ‘backward’ appearing step is it continues to be the standard process.
In the early days of outsourcing manual indexing was the best practice. If there was more volume an outsourcer would simply add more bodies to the production line. It alleviated internal pressure inside the company to process and index these documents by sending them to the outsourcing experts whose business it was to perform this back-of-house task. At the time it was cost-effective and was the “gold standard” of document indexing.
The main problem with manual indexing is the human element. When you introduce people you introduce human errors and omissions and there are several reasons this occurs. It could be a result of poor training, faulty processes, or simple document fatigue. As you can probably imagine staring at documents all day could lead to simple mistakes that the same employee wouldn’t make at the start of a shift.
Aside from error rates, common volume fluctuations could lead to slower turnarounds and reduced ROI. A common saying in the manual outsourcing world is “What would you like, cost reduction, quality output, or timely turnaround..? Pick two.”
By moving away from manual processes and outdated technology there as a solution that allows you to have all three. With the advent of AI and Machine Learning (ML) automated indexing that minimizes or eliminates manual processes is possible.
Document Indexing Process
By utilizing ML algorithms document indexing process happens on a real-time basis. It can open, review, and find index specific data points and perform indexing functions much faster than the traditional manual process.
This happens by using a set of pre-defined algorithms for rules-based indexing that locates identifying data points, or by using rules in conjunction with ML cluster mapping to identify and sort like-images. Best of all, the software learns and begins to make more and more complex decisions without human intervention or aid.
Advantages of Automated Indexing
Machine Efficiency versus Human Efficiency
ML quality and quick turnarounds easily translate into cost efficiencies compared to the costs of performing or sourcing manual document indexing. In a recent iTech study ML-powered automated indexing was able to perform indexing functions and extract multiple data points from freight invoices in an average of 1.7 seconds. When compared to manual indexing, this less than the amount of time it would take a human to simply open the image. ML was able to process 2100 documents at the same time it took an expert keyer to process 30. You would need around 70 document indexing experts to equal the ML output and the accuracy would still not be as good.
The traditional manual indexing process requires more people, more cost, and it is more error-prone than ML automated indexing. It’s time to reconsider manual processes and move document indexing to an outsourcer that provides an automated platform. “What would you like, cost reduction, quality output, or timely turnaround..? Why not get all three?”
For more information on how iTech can help with automating your document indexing please give us a call or send an email. iTech also provides robotic process automation, ML-based data capture, ML-based OCR services, and more.