Traditional means of document processing depend on staff members to handle data manually. Whether it’s invoices, quotes, orders, remittances, or any document — everything has to be manually read and keyed in, day in and day out.
Back in the day, when the paperwork was limited, manual document processing made sense because it was feasible. But with data-driven companies scaling their businesses and with the option of automated tools, asking your staff to process the documents manually is no longer practical.
In this article, we discuss the major pitfalls of manual data entry and talk about automation as a sustainable alternative.
Manual Data Entry: Overview In 2021
Back in 2019, a mere 55 billion of the world’s whopping 550 billion invoices were exchanged electronically. All the rest were paper-based at some level and required manual processing. And that’s just the overview of various other intricate issues tied up with manual data processing:-
How prevalent manual data processing is in different Industries?
Companies are still dependent on spreadsheets along with ERP systems, among other business software that requires tedious labor for processing data. 23% of salespersons say manual data entry is the single biggest challenge when using their CRM. Not only that, as much as 84% of all small businesses are reliant on manual data entry.
On the other hand, until 2020, 31% of businesses have adopted automation in some form or the other, and almost 64.8% of businesses have invested more than $50 million in big data and AI in 2020 alone to improve productivity. This has been prompted by advances in big data processing and the growing confidence in AI-powered decision-making.
Is manual data entry accurate?
Despite taking a huge amount of processing time, manual data entry error rate is quite high in most industries - being deemed one of the biggest hindrances in their growth. According to a report from SiriusDecisions, contact records from 10-25% are comprised of critical data errors that directly impact a business’s operations.
Is manual data entry efficient?
Manual data entry, by definition, is a slow and time-consuming process. And since it’s not practical to expect your staff to work at a constant rate throughout the day - given fluctuation in productivity from boredom and fatigue - there are often delays in the turnaround time. The exercise itself is quite time-consuming and leaves little room for analysis and strategizing and much room for a higher manual data entry error rate. For instance, single data entry speed runs at about 10-15,000 keystrokes every hour. No matter how impressive you find it, it is still much slower than a machine.
That’s why financial institutions, among others, are now prioritizing digitization. UnionBank of the US, for instance, has reduced turnaround time for digitizing loan documents from 15 days to 5 days.
Is manual data entry the reason behind cash burn in your company?
A survey from Experian showed that 83% of organizations recognize data as the cornerstone of their business strategy, and yet a substantial 69% confess they are plagued by the issue of inaccurate data.
Businesses that rely upon manual tasks for mundane tasks like data entry are being left behind in all aspects, including performance, productivity, employee satisfaction, and yes — revenue.
There are significant hidden costs associated with manual data entry. Apart from costs for quality checks, and losses due to incorrect information from human error, there are fees for late payments, issues with cash flow, poor vendor relationships, and employee changeover relationships that can drive your revenue south.
Industries leading the way towards automated data entry
Here are some of the major industries that are pioneering automation in data entry and improving productivity. Let’s look at how they implement it and the benefits they derive from it.
Accounts Payable
The categorization of any amount owed by your company, your accounts payable (AP) process is responsible for managing debts and invoices. Here’s how this field is automating data entry:
- Invoice Capture - The accounts payable process starts from receiving vendor invoices, and uploading to automated invoice processing systems for data capturing.
- Seamless approvals - An automated accounts payable workflow is designed to route each invoice to the concerned authorities to reduce snags and the scope of error.
- No Manual Data Entry - With Intelligent OCR technology, an automated invoicing solution can scan printed data from documents, so there is no need for manual data entry. This is useful even after the invoice has been cleared.
BFSI
Banks combat redundant manual data entry, human error, and frauds. In contrast, bank statement software with Intelligent Character Recognition or ICR is now being used for processing handwritten surveys and bank cheques.
Similarly, for insurance, processing claims is a tedious task and traditionally demands a whole infrastructure to support staffing, training, IT systems, and whatnot. But with increasing digitization in the insurance sector, we now have what’s called Intelligent Document Processing or IDP. This technology is powerful enough to extract data from unstructured and complex documents, including forms that usually require manual processing because the generic OCR can’t analyze it meaningfully.
Logistics
Manual data entry and processing is a taxing affair both for the employer and the staff and also financially and mentally. With the logistics sector now pioneering automation in terms of document data extraction, here’s what makes it a cost-effective venture:
- Faster movement of goods - Automation optimizes resource utilization, thus ensuring all goods get manufactured, packed, shipped on time.
- Reduced labor charges - With AI taking over most of the straightforward and redundant tasks, there’s less room for base-level manual data entry jobs.
- Real-time insights - Through real-time data analytics, you always have visibility into your logistical processes and know when to optimize a particular sub-process.
- Improved accuracy - By integrating commodities, address books, etc., through ERP systems, there’s very little chance for errors.
- Improved coordination - Through automation, communication is largely streamlined and centralized, making coordination and task exchange a walk in the park.
Human-machine collaboration - the way forward
The quickest and the most effective alternative to manual data entry is an automated setup. With the help of AI and smarter OCR, you can complete redundant and otherwise time-consuming tasks without much effort. Yet, technology can only do so much.
Humans must train machines to perform clearly delegated tasks and proactively interpret the derived results. Adjustments or human input might be required if the outcomes are indeed counterintuitive.
Here's what machines can do:
- Learn everything about a data ecosystem and populate a fresh data catalog.
- Log and document enterprise data.
- Distill insights from breadcrumbs left behind by humans in terms of query logs, data values, etc.
- Elicit direct feedback and read from humans.
But truly, machines can only make an educated guess, but it’s the experts who will have to confirm the results. The goal is to train the machines, so they can make near-perfect guesses.
And when it gets down to the brass tacks, in the collaboration of AI and humans, it’s the latter that has to input the least and reap the most benefits. In the right setup, human-machine collaboration can result in error-free data extraction and processing.
Adopt automation in your data entry process with Docsumo
With Docsumo’s Document AI and Intelligent OCR technology, automation meets decisioning and analytics. Whether it’s pay stubs, invoices, or actionable data, all your high-volume, repetitive tasks get completed seamlessly. Our technology promises to drastically reduce turnaround time and improve accuracy.
Everything - from uploading documents to editing and validating fields to downloading CSV/Excel/JSON - our solution has it all sorted out. Let ‘s schedule a demo and let Docsumo take care of your data entry blues.