” Do you still have the invoice that the supplier gave us two months ago? How many P.O.s do you still have to process? Why is accounting taking so long to do this? ”
These questions often cause frustration for all stakeholders involved, from the manager to the data entry operator (or data clerks). In this Big Data era, data capture and data entry has never been as widespread a task as it is today, yet few organizations have bothered to look at the possibilities of automation, especially through Machine Learning (ML).
What is data entry?
Data entry is the process of inputting an organization’s data into a database, often in an ERP, a CRM, or a dedicated internal software. It is a very important process that usually takes a long time. Indeed, it is a repetitive task that requires a lot of concentration at each execution. Here are some examples of recurring documents for which data is usually manually entered:
- Invoices
- Purchase orders
- Contracts
- Receipts
- etc.
Data can come from a variety of different sources and formats. Remote working now being a part of our daily lives, documents that are traveling through an organization can be very diversified. PDFs, Word and Excel documents, as well as images (ex: .JPEG, .PNG, etc.) are all examples of documents that can be difficult to process due to their variety.
Operators have more on their plate today than ever before. The documents’ formats processed come from different sources and this makes the task even more complicated. It is no longer enough to simply copy and paste from one document to another. Operators often have to clean, format, edit and organize data to ensure accuracy, and this can consume valuable time.
The problems with manual data entry
Manual entry has been the modus operandi for internal data management. Formerly being the norm, operators are now losing interest in this work. In fact, a study conducted in Quebec shows the psychological and physical impacts of this profession. Automatic extraction provides a tangible solution to real challenges experienced by these operators. This task is time-consuming and can be alienating for the people in position. The main challenges of manual data entry are :
Performance and errors
Manual data entry is subject to human errors. These repetitive tasks and the lack of added value they represent for the employee can quickly become demotivating. The efficiency and the motivation of each operator, like any human being, is not the same from one day to the next. Thus, tasks performed by manual data entry are always subject to human errors.
Speed limitations
In the Big Data era, it is no longer conceivable to continue these practices. Organizations today have several hundred documents to process each month. The slow processing speed and manual reviewing processes cannot cope with the ever increasing volume of data circulating.
Limited security
Although data is subject to multiple validations and verification checks, it is exposed directly to humans. Any bad intent can lead to the misuse of data for personal gain. Sometimes unintentional, data leaks can be very costly to an organization, but also to the operator. both of whom can be exposed to legal action.
Employee alienation
These tasks are time-consuming and are generally considered annoying to employees. In fact, they can present risks to their physical and psychological well-being. Eye strain, tenosynovitis, and emotional stress have all been associated with excessive data entry work. In addition, the contribution an employee could make to the organization by focusing on value-added tasks, such as improving communication with suppliers and negotiating expiring contract agreements, should not be overlooked.
The advantages of automation
Artificial Intelligence has completely changed this reality, to the benefit of employees and organizations.
Automated data capture is rapidly making its way into organizations of all sizes, mainly because of the many benefits it offers. Here are just a few of them:
Speed
AI (Machine Learning) powered solutions such as DataExtract, enable rapid automated data extraction and entry. They offer a fresh new approach to data processing, and while some operators can achieve very high keystrokes per hour (KPH), the speed generated by the technology can never be matched.
Accuracy with high volumes
Accuracy is always higher than the manual approach, especially when dealing with large volumes of data from different sources. Unlike the manual approach, there is no compromise between speed and accuracy with automatic data extraction and capture.
Consistency
One of the big advantages is that the technology works consistently and streamlines the entire data capture lifecycle. Operators and clerks can sometimes, intentionally or not, be distracted and therefore, lose consistency. Such disruptions do not occur if the process is automated and controlled.
High security
This technology provides robust security mechanisms that ensure sensitive data is protected from being leaked and/or misused. In most cases, the data is stored in cloud solutions, allowing enhanced data security. To maximize this security aspect, it is best if the cloud is hosted in the same country as the organization using this type of technology.
Quick ROI
This is an area of application where generating value can be done very quickly. The potential ROI of this process can range from 100% to 300% after just one year of use (depending on the number of resources assigned to the task and the volume of documents processed) and can reach up to 500% after the second year. (For a better understanding of these numbers, see page 6 of this document).
To further elaborate, here are some additional benefits that automated data extraction and capture can bring to organizations:
- Reassignment of staff to higher value-added tasks;
- Reduction of errors and inconsistencies in data entry;
- Improved working conditions, using the power of advanced technologies;
- Start implementing AI in your business to improve business processes without taking risks.
Conclusion
Today, automated data capture mechanisms are very effective and are already helping many organizations to accelerate their growth. This process is a great way to take the first next steps towards integrating AI into their operations. The time freed allows organizations to reassign employees to spend more time innovating and developing strategies that will create better products and improve the customer experience.
Some companies are implementing hybrid approaches. There is always the option of an AI-driven, human-supervised approach. This approach combines the power of technology and human intelligence – ultimately, the only goal is to improve your processes!