Data entry is a time-consuming and repetitive task. There are different types of data entry, but all of them have the same goal, that is to transcribe an existing document or section of data into a more convenient digital format. Data entry tasks for a company or organization is handled by professionals. When a company employs people for performing data entry tasks, there is the potential for human error. Partnering with experienced data entry companies helps reduce data entry errors. The most common data entry errors are transcription and transposition errors. In fact, these mistakes can be quite costly. Due to these errors, data analysts have to painstakingly assess the accuracy of incoming information.
The good news is that every data entry operation can greatly benefit from the adoption of machine learning and robotic process automation (RPA).
Robotic Process Automation and How Machine Learning Can Help
RPA is the use of software with artificial intelligence (AI) and machine learning capabilities. It can handle high-volume, repeatable tasks that are usually performed by human staff in large organizations. RPA software can assist in various routine office jobs. With RPA, companies can automate repetitive or programmable tasks, thus employees do not have to do them, and they can focus on more important tasks. The following are some of the benefits of RPA.
- Helps reduce the burden on employees
- Enables better customer service
- Ensures business operations and processes comply with regulations and standards
- Allows processes to be completed much more quickly
- Provides improved efficiency by digitizing and auditing processed data
- Provides cost savings for manual and repetitive tasks
- Enables employees to be more productive
Machine learning (ML) and AI (artificial intelligence) can be used to systematically improve the entire operation by learning more efficient and accurate traits over time. The merger between machine learning and RPA is referred to as intelligent process automation (IPA). Combining process automation with RPA and ML can improve your business.
Applying Machine Learning and RPA to Data Entry
Machine Learning and RPA, when used together, helps automate laborious and repetitive work. This in turns frees up workers for more important tasks. As automation systems never get tired or grow weary, get burnt out as well as never make mistakes and always follow their programmed responses, they can improve product quality and tend to be much more accurate and effective than their human counterparts. RPA can be used for mass data entry or document generation like mass emails. It can also analyze and process lists and other data.
Knowledge workers can also benefit from RPA. ML programs use the discovered data to improve the process. Hence, machines can learn to perform time-intensive documentation and data entry tasks, which allow knowledge workers to spend more time on higher-value problem-solving tasks.
RPA and machine learning help reduce date entry errors, improve performance and boost security. This is better for security as fewer people have access to sensitive data, especially when an automated system can convert, move or translate information without anyone else ever touching it.
Incorporating RPA and Machine Learning
In order to incorporate RPA and Machine Learning, you will first need to assess which processes and operations can be automated with the technology. Then, you have to train your workforce including data entry professionals to operate alongside such tools. Finally, you will have to develop an infrastructure that supports their use and provides the necessary access to all incoming data channels, sources and platforms.
A practical way of meeting your data entry challenges is to outsource data entry tasks to a professional data entry company. Using advanced, best-in-class technologies such as optical character recognition (OCR) and intelligent character recognition (ICR), reliable data entry service providers can automate the data entry processes. This allows companies to save time and money, reduce administrative burdens, and the employees can focus more on their core tasks.
