How to Automate Data Entry Without Technical Skills
Nov 07, 2024
In the current world, data is vital in the management and running of businesses and companies. The emerging market size of data entry outsourcing services is USD 202.3 million, and the forecast market growth rate is 6.18% from 2023 to 2028.
However, data entry is time-consuming, monotonous, and error-prone. Are you exhausted from slow and tedious data entry?
Here comes automation of data entry!
It offers you efficiency when dealing with your data requirements. Spending a short amount of time will result in better organization. That’s the beauty of automation – it makes your business process easy and smooth.
Such automation saves time as your business does not have to work so hard to accomplish all its tasks.
Let us discover how it is possible to use automation for documenting your data entry and enhance your productivity.
Automated data entry can be described as entering data without typing it. They can use AI technology to automate those tasks that consume a lot of time and increase the business's productivity. An automated data entry system accomplishes a task by automating data entry to save costs and time.
This automation can be used for different data types, which may include customer data, employee data, product data, or even financial data.
The use of automation in data entry means that businesses will input data into their systems faster. This makes it possible for business organizations to offer their customers fast and efficient services.
The time taken to process large quantities of unstructured data has significantly been reduced through the use of data entry tools. It also minimizes the risk of errors. Here’s a step-by-step explanation of how it works, with examples:
The first phase involves collecting data attained from other sources such as scanned papers, PDF files, e-mails, or image data. This data has to be input into auto data entry software for further processing.
For example, a company has to perform hundreds of invoices daily, and physical examination can take hours. Instead of manually entering the invoices, the software collects them, simplifying the extraction work. A survey by Deloitte reported that 73% of organizations adopted automated systems to manage their documents, which has reduced their operational time.
Before extracting the information, it is essential to perform a pre-processing step. Difficulty in reading images or scanned files that are distorted or uneven and have imperfections, marks, or pixels that hinder data extraction.
On the preprocessing level, image distortion correction reduces imperfection, and their settings enhance text visibility. For example, if the scanned document is a blurry image or low contrast, the data entry applications fix it for ease of understanding. The studies illustrate that this phase can improve OCR accuracy by 15% and ensure better data extraction.
In the third step, the acquired documents are preprocessed before undergoing other complex processes, such as image to text conversion with OCR and Natural language processing (NLP) to extract valuable information from the document.
For example, if a company receives many customer orders in draft form, including images or PDFs, it becomes possible to extract data, such as the customer name, order number, and details. Optical character recognition technology refers to the conversion of printed or handwritten text into machine-encoded data. In general, OCR systems achieve an accuracy rate of between 80% and 90%; as machine learning is applied, the rate is steadily increasing.
After extracting data, it goes through a validation process. This step ensures that the information extracted is error-free. Validation comes in two ways: such as automatic verification through modified machine learning algorithms or manual inspection by network operators.
For example, in the loan processing system of a bank, data may be checked against a reference database to identify missing or inconsistent data. However, this does not mean that all of these results require manual validation in more complex cases. It is estimated that ML automation and AI have improved efficiency by 40% among various businesses.
The final step is data conversion to formats like CSV or XML, etc. This makes it easier to import the data into databases, spreadsheets, or analysis tools. For example, an inventory management system might use auto-population to import order data into a structured database. Almost 60% of business organizations benefited from automated data entry by experiencing time-saving and enhanced data quality.
All these procedures decrease manual efforts, reduce errors, and fasten the data processing automation workflow. Another factor that contributes to the increased use of IA is that, when performing extensive tasks, business entities can achieve greater levels of efficiency and accuracy.
Auto data entry is a game changer in business if the company wants to eliminate the problem of inefficiency and high error rates. With OCR automated data entry and auto data entry software, you can simplify the process and increase the efficiency and accuracy of data input operations.
Whether it is customer data, employee data, or financial data, it is designed to improve operations by enabling immediate validation and formatting. When data is entered using the right tools, the business organization saves more than 200 hours annually. It also saves money, increasing productivity and customer satisfaction. Automation will enhance organizational performance, while the business will be competitive in today's digitally driven world.