Data entry is one of the most ubiquitous tasks across the business world. It touches every business in every industry. But there are challenges. Data entry is time consuming and error prone. In some cases, a single data entry process might involve several people, systems and touchpoints, with data that is structured a multitude of ways, adding time and complexity to the task. Thanks to process automation technology, there are now new, efficient ways to address these challenges.
This use case looks at how business users can do more with data entry, streamlining processes—even when formatting is inconsistent—with the help of intelligent process automation (IPA), also called simply intelligent automation (IA).
Common Data Entry Challenges
One of the primary challenges with most data entry undertakings is that data can take on many structures, ranging from handwritten forms to emailed submissions to plain text and other electronic formats. Here’s how some of those challenges may play out for businesses, and how intelligent automation can step in to help.
Intelligent Automation for Data Entry
Improved processing times. Simply put, data entry is not necessarily our strong suit as a species. Repetitive tasks like information retrieval and input, especially when forced into a manual entry process, result in slow productivity that is susceptible to errors. Many of these errors can be prevented, and the process itself can be sped up, thanks to the help of robotic process automation (RPA), which can automate both retrieval and entry between various systems. However, RPA’s limitations come into play when there are various formats and structures. That’s where IA steps up, providing optical character recognition (OCR), natural language processing (NLP) and other smart document processing to “read” information, parse it, and send it to the next leg in the workflow.
Streamlined contract management. When it comes to contractual agreements, there are a number of things that can slip through the cracks, from missed automatic renewals to failure to properly invoice. All of these problems can contribute to loss of revenue. While RPA can automate some of these processes, the variance among formatting and structure is once again a fact. Using OCR, NLP and other cognitive techniques, IA is able to detect themes among different contracts, in some cases even learning contextually what some of them mean. Once identified, it can then send it back to an RPA bot, which can then properly route the data where it should go next, helping to alleviate revenue leakage.
Automated processing and reconciliation. Invoice processing is not only a huge draw on time, it’s also a data entry process fraught with potential error. Using OCR, NLP, computer vision and other cognitive techniques, IA can extract specific invoice data points and restructure as needed. IA technology can then route the structured data back in the RPA workflows, where it can be processed and reconciled before getting delivered into the hands of accounting teams who can use it to fulfill their responsibilities.
There are just three common examples of intelligent automation for data processing, but hopefully it’s becoming clear that the technology can be leveraged in many scenarios.
Getting started with intelligent process automation (IPA)
If you have a specific process in mind, or if perhaps you’re just beginning your RPA and IA journey, the EPSoft team is ready to help. Get in touch any time to schedule a free demo or business consultation.