RPA In Our Own Words: Managing Unstructured Data
Today on the UiPath blog we’re featuring a new segment called RPA In Our Words where UiPath software developers, designers, and others within the UiPath organization speak directly to you about the pressing issues in today’s global automation landscape. Throughout the next few months, we’ll highlight a wide-range of important elements in today’s automation climate from a variety of unique perspectives.
Our first entry concerns managing unstructured data and how companies operating on a global scale can leverage automation to sort, decipher, analyze, and leverage unstructured data into unique competitive advantages within their given industry. Bogdan Nedelcov, a product manager at UiPath, will explain and offer some critical insights into this story.
How RPA manages and creates structured data from unstructured data
Unstructured data more or less means any kind of information that doesn’t conform to fixed-rules or constraints. This means data that is variable in length and that is completely unpredictable when it comes to anticipating how or in what form this data, or content, come into play. An email is a good example—an email can be any length, contain any number of content forms, and has little room for predictability in terms of the metadata in the email itself.
Today, Robotic Process Automation (RPA) is unable to directly manage unstructured datasets, requiring robots to first extract and create structured data using advanced capabilities such as optical character recognition (OCR) and natural language recognition (NLR). RPA robots are then able to sort and manage what is essentially a transcription from unstructured to structured data to help companies execute any number of processes. As with our email example, RPA robots can take this newly structured data extracted from a message and use it to help fill customer request forms, invoice generation & cross-checking, and other operational tasks.
The biggest challenges for RPA with unstructured data
Perhaps the biggest challenges for RPA with managing the transition between structured and unstructured data are both managing the sheer volume of data that requires this treatment and creating the right templates from which to facilitate the movement from unstructured to structured.
Before an automation robot can understand the data, a template, or roadmap in a sense, must be created which understands this particular transition. Because unstructured data comes in all forms, lengths, and contexts, creating enough of the right templates is something of a monumental task. For example, on an invoice, data fields for line items other than a company’s name, address, etc, have to be created manually because these fields vary quite a bit in nature given the situation or context. At UiPath, we are confident RPA will be able to accomplish these tasks with near 80 percent or higher efficiency in the very near future.
Another challenge for RPA with unstructured data is human or manual error in inputting the wrong information in coordination with an RPA robot. While a successful automation will still be able to be executed, the results of that automation may very well be incorrect or inaccurate. This is one area where I wish there was greater understanding about how RPA solutions work in tandem with human intervention.
The value proposition RPA provides companies in facilitating unstructured data
Yes, automation robots are a critical value proposition for facilitating the transition and management of unstructured to structured data, particularly when it comes to document processing and security. Because documents, especially invoices, and the information therein can come to companies in a variety of different ways (emails, snail mail, etc), companies can deploy an OCR engine to extract the necessary data based on predefined templates or structures.
This also works for documents like purchase orders as well. It allows companies to cross check invoices against purchase orders to make sure resources going out match resources coming in, creating greater stability and accuracy for financial management. This also helps manage a company’s security by helping alert managers to any exceptions or deviations relative to ledgers or bookkeeping. So, by enhancing a company’s ability to streamline how they manage invoices, purchase orders, or other financial documents, you increase the financial security and efficacy of business practices and ensure that Financial Compliance standards are met.
But perhaps the biggest value proposition for RPA with structured and unstructured data is the breakdown between structured and unstructured in today’s business world. About 90 percent of data types most commonly seen in the global business landscape fall into the structured category; however, with the tasks humans engage in, about 50 percent is in the unstructured category. If RPA can help eliminate or supplement working with human intervention with this unstructured category, this allows for companies to become more agile and competitive.
We’d like to thank Bogdan for his time and insights into why managing unstructured data is a critical functionality of an RPA solution. Check back to our blog regularly for more on the RPA In Our Own Words series.