Review. Copy. Check. Submit.
The data entry process involves digitising information by copying it from one source (often in paper form) into an actionable format (e.g. an internal database for post-processing).
Continuous data entry is a back-office process for many businesses. Document-heavy professions, like insurance, healthcare and manufacturing, often rely on fast and accurate data entry. They might conduct it in one of two ways:
Both forms of manual data entry revolve around using an individual to copy information from one source to another. You can expect the process to yield errors at roughly a rate of 1%. However, some studies suggest the error rate might be closer to 3-4%.
Data entry operators have developed several techniques to mitigate errors. Examples of these techniques include:
By recording data in two different accounts, such integrity will (hopefully) ensure greater accuracy. For example, Accounts Receivable Reconciliation involves identifying discrepancies between the customer balance and the general ledger balance. Any errors could result from confusion between debit and credit accounts – or faulty data entry. Numerous studies have found that double data entry is more accurate than single data entry, though entering data twice may incur increased labour costs.
Similar to double data entry, comparing different accounts to a general ledger can help you identify discrepancies. For example, you might compare bank statements to your general ledger.
A study comparing four data entry methods notes that data entry operators with previous experience made ‘41% fewer total errors’ than their non-experienced counterparts. However, it’s important to note the errors they made when reconciling data sources were biased to assume their own entries were correct.
Despite these practical techniques, errors will persist. Tedium and distraction make errors inevitable, even for highly experienced data entry operators.
As a repetitive yet precise task, data entry is ripe for more sophisticated automation, even though such technology has existed for years. In particular, web-based or cloud Optical Character Recognition (OCR)-based data extraction tools have been available since the early 2000s (more on OCR later!).
Automating data entry isn’t a novel concept. But, with so many companies investigating the benefits of automation, there's never been a better time to assess whether automated data entry could be a good fit for your firm.
One of the primary motivations of anyone investigating data entry automation is capping and preferably reducing costs. The costs associated with manual data entry are numerous, meaning calculating the Total Cost of Ownership (TCO) might be challenging. Here are a few factors contributing to the TCO:
Firms dealing with an increased volume of manual data entry will likely need more full-time employees to handle the workload. It’s not just the cost of the full-time salaries that will inflate their operating costs, though – it’s also the unpredictable expenditure of:
These surplus costs make scaling difficult and rapid scaling extremely challenging (and cost-prohibitive). However, the cost of expanding automated data entry is usually minimal and predictable, especially for solutions that offer a per-page pricing model.
Back-office technology isn’t doing its job if it isn’t making the lives of its operators easier. Many industries (including insurance, healthcare and manufacturing) are facing staff shortages. To compensate, existing employees may need to complete more administrative tasks. Such an increased administrative burden could manifest as added stress, higher employee turnover and lower overall productivity down the line.
In our near-decade of operation, we’ve never encountered anyone who has enjoyed data entry. It’s a tedious task that can clog schedules and interfere with higher-value tasks.
Spurred by some of the discourse surrounding AI, fears of being ‘left behind’ motivate countless business decisions across many industries and sectors. Investing in a robust data automation solution will instantly give your firm a competitive edge – particularly if that solution is from a data entry vendor dedicated to constant innovation.
Various technological options exist to automate data entry, each with benefits and limitations. Let’s examine three of the most popular.
DataHeadhunters has an extremely detailed breakdown of how to use Python to automate data entry. In a nutshell, Python-automated data entry works by writing a script and then importing modules to perform specified tasks. Add in as much testing as necessary, and then you’ve got a functional Python-powered automated data entry solution.
OCR technology has a rich history in data entry circles. Dating back to the early 1900s, OCR was a landmark achievement, paving the way for AI-powered data entry automation.
There are a range of OCR solutions on the market. If you’re looking for a particular type (contracts, labels and packaging, prescriptions and utility bills, to name a few), there’s likely to be an OCR technology provider available.
OCR works well with documents with fixed structures (as opposed to unstructured documents). Examples of structured documents include:
For unstructured documents, though, interacting with OCR might prove a frustrating experience. Even slight variations in the structures of these documents can compromise the quality of the outputted data.
Also, OCR is inflexible for data entry operators handling multiple document types. That’s why we always recommend users of any automated data entry solution complete a Proof of Concept (PoC) before buying.
AI-led automation is more sophisticated than simply uploading scans to ChatGPT. The rise of virtual assistants like ChatGPT from 2022 onwards seemingly presents a use case for automated data entry. Just upload a scan and then copy and paste the data. Or, you can automate the delivery of this data via a tool like Zapier or DryMerge. Simple, right?
The convenience of ChatGPT belies its accuracy, though, which has historically been mixed from our experiments. We’ve found that ChatGPT generates approximately one error per uploaded page, and often, these errors can be quite slight. You might not notice if ChatGPT’s algorithms have changed a single digit after eyeballing the data.
Of course, there is a chance that these AI companies (i.e. OpenAI, Google, Anthropic, etc.) can improve the accuracy of their models. But, as they are built around generalised functions, their performance will always be inferior to smaller, more specialised models.
AI models that are purpose-built for data entry automation will perform better. They will have been tested and developed for a single function, meaning they will ultimately excel. Consider the Bruce Lee quote, ‘I fear not the man who has practiced 10,000 kicks once, but I fear the man who has practiced one kick 10,000 times’.
AI’s capabilities will only improve moving forward. Therefore, investing in AI (if leveraging the right AI solution) now means faster and more accurate performance.
As it stands, AI is already automating data entry processes to up to 100% accuracy. It can also complete these functions in seconds, not minutes. Usually, data extraction can be completed in real-time, meaning large data backlogs won’t build up.
‘AI’ tends to trigger fears of displacement. For example, I recently delivered a talk to local Cambridgeshire businesses, and many of the questions from the audience focused on the new role of humans in an increasingly AI-dominated business landscape. The tone was strongly of concern.
Businesses introducing AI-powered data entry automation would do well to ensure that AI’s role is well-defined ahead of its introduction in the workplace. Automation exists to alleviate employee stress, not increase it.
Another aspect of AI-powered automation worth considering is that not all AI tools are built equal. Conducting due diligence to locate data entry tools with the highest accuracy rates might be time-consuming – but ultimately worth it.
Ideally, you want to integrate the technology into a workflow that completes data entry end to end. End-to-end data entry ensures that as soon as the system receives the source (e.g. a document), it extracts the data into the required repository with no manual touchpoints.
Ideally, you would have guardrails around this workflow, meaning the system will flag any errors in the process. After all, you don’t want to action incorrect data accidentally. Let’s dive into how one of these workflows might work.
Converting image scans to Excel is one of our most popular use cases. Users want to ‘unlock’ the data from a scan so they can edit it in Excel. So, it’s possible to set up an end-to-end workflow instead of using manual data entry operators. Here’s an example:
Data entry can be fully automated. However, when deployed on a large scale, a workflow may necessitate human intervention to review flagged errors.
A well-trained AI-powered automated data entry solution should issue a (very) low error rate. For example, our proof of concept for DF Capital Bank delivered 100% accuracy. You certainly shouldn’t expect anything less than 99% accuracy.
New, unstructured document types might potentially compromise the error rate initially, but otherwise, you can hold an AI-powered solution to a high standard of accuracy. Another factor that might influence the error rate is the type of service the vendor offers – either a managed or self-service model.
Some automated data entry solutions will offer more than one service option. These will typically include:
A managed automated data entry solution will be human-in-the-loop, i.e. a human at some stage will validate the data before delivery to the client.
Self-service solutions mean the user completely operates the software. Often, self-service is helpful for companies looking for end-to-end control over their data flow.
As one of our clients – Rachel Taylor, Head of Change & Continuous Improvement at DF Capital – notes, “We wanted to do the in-house processing and annotation of the invoices ourselves and keep that in-house skillset and capability.” (Read the full case study here).
Some managed services can guarantee completely error-free data. Self-service solutions, however, are far less likely to extend this guarantee due to the possibility of user error.
Evolution AI worked with the Royal Bank of Scotland (RBS) to automate several critical back-office processes. In particular, we automated Know Your Customer (KYC) checks from bank loan applications. From the customer’s perspective, we made onboarding quicker. And from RBS’ perspective, we saved them an estimated 100,000 - 200,000 work hours each year.
We’re Evolution AI, and we’ve been automating data entry since 2015.
Contact our financial data project team if you’d like to see how automated data entry could save your business time and costs. Email hello@evolution.ai or book a demonstration call – (we’re always happy to show off our technology)!
