Typically, data entry automation today is related to any software that extracts information from PDFs, images, videos, and other forms of media.
However, data entry automation has been around for ages, even before the invention of computers. Whenever anyone needed to automate data entry in ancient Rome, they could not use OCR or other systems; however, they needed to solve the scalability problem.
They eventually learned that building standards are the best way to do that. A similar thing happened with money - Rome legionnaires had to pay for food in many territories; hence, they needed a standardized means of exchange.
Standardization alone isn’t enough. Humans needed an interface. That's how the standard form was invented. So, when there was a need to collect or submit information, the first data entry automation was done by creating an interface to a standard form.
Creator: DEA / G. DAGLI ORTI
An automated data entry system has several blocks:
Interface to Data storage
Data Entry Automation Secret
The secret of data entry automation is to build a proper data storage system. As information enters your company, you want to have a single source of truth. Hence the point of automating manual data entry is to build a proper foundation. Tools come and go, and technologies evolve, but storing data in one place is foundational.
The best example of storing data before computers is a filing cabinet of mailboxes.
Whenever mail arrived, you knew where to find it. In fact, there was no training required; filing cabinets were absolutely clear.
Before anything is done, we must ensure that the information is maintained in one place; this will be our first building block. For example, an automated invoice process will ensure all invoices are kept in a single folder in your company’s invoices folder. You can add an extra form to submit photos to this folder. You can also create a special mailbox called invoices and forward emails with invoices there so that automation can save the file to the folder.
A Problem with Forms
Forms are used today in many places. You may even use forms to accept information from your peers. An example of this inaction can be seen when you ask all suppliers to upload an invoice with a valid purchase order to your system. This creates an automated data entry.
Another example would be creating a work validation form - the Head of operations in a property management company can create a form in a tool like Asana or ClickUp for the cleaning team to upload photos after some work has been done. This would create an automated data entry process for work validation.
However, the problem is that our work processes are so complicated that there is virtually no way to force form submittals. Information arrives at our company via multiple channels, and forms are just one channel.
If your client skips the forms and sends an email, you cannot justify not doing the work if the form was not submitted. And many people don't bother sending forms as it is much easier to send an email.
This is why you must assume that information always arrives via multiple channels.
Once you've decided on the structure of the data storage system, the next step is choosing the best option for automating the next step.
In this step, we are building a system where we will extract information from a source and save it to some standard form like a row in Excel or Airtable.
The main choice you make here is a trade-off between accuracy and flexibility. In general, it looks like it is better to have 100% accuracy. Still, in reality, there are two things to consider: it is almost impossible to reach 100% accuracy without a human check, and starting at 90%, each percentage point of accuracy increases your costs exponentially.
Thus, typically, if you have less than 50,000 events per month, it is recommended not to look for 100% accuracy. Therefore, extracting data becomes a process.
This process looks like this:
Get source file
Detect content type (invoice, bill, contract, etc.)
Run the corresponding software module
Write data to a required tool(s)
Set the flag for human check.
This way, you remove the biggest time consumer - typing data into tools.
Once the data has been extracted, it has to be written somewhere in a standard format. This format is digital and allows you to make further analyses. You can now search, build dashboards, create graphs, and more because the information is now ready for any machine analysis and exists in a user interface for humans.
While it is super easy to present manual data entry as an OCR or A.I. exercise, it is a very important process. Data in your company is extremely valuable as it provides the foundation in operations analysis. Building a good foundation is beneficial for your company - it will allow you to get a better understanding of what was done, what is currently happening, and forecast what will happen in the future.