By Boomi
Robotic process automation (RPA) is one of the most effective ways to automate business processes. Creating a program to carry out tasks that previously were performed manually has seen benefits in just about every department across the organization.
But not every type of data plays well with RPA. In fact, RPA data analysis and manipulation can only be done effectively with structured data. Getting insights – and business value – out of other data types requires more advanced process automation tools.
Before understanding what tool to use, we need to explore the three types of data companies deal with and how each can or cannot be processed. Read on to learn more about different types of data and what tools are available in addition to RPA to help automate even more processes.
What Are the Three Types of Data in RPA?
Automating tedious, repetitive tasks with RPA has many benefits. It saves time, helps reduce errors, and allows employees to focus on other work – increasing their productivity.
Structured data
This data is formatted and organized in a standardized way and is usually housed in a database or a spreadsheet. This is the only type of data that can be analyzed and manipulated with RPA.
One example is customer records. Fields exist for first name, last name, email address, etc. Because a structure is in place, there is no question about what data exists in any given field. This uniformity allows the data to be easily understood by human users or machines, such as a search engine or an RPA bot.
Semi-structured data
Similar to structured data, this is organized with discrete elements that contain the data. However, those elements are not always uniform and may exist alongside or contain unstructured data.
One common example of semi-structured data is an email. It has many elements of structured data, such as the “to,” “from,” and “subject” fields, which are all easily understood by machine users. But it also contains unstructured data in the body of the email, which machine users can’t analyze. Still, automation for email content can be a significant productivity improvement for organizations looking to help their employees find ways to create more business value.
Unstructured data
This encompasses all types of information not organized into a uniform structure. This includes everything from images to audio files and even text documents.
Machine users cannot understand what the unstructured data represents because it does not adhere to a structured format. This makes automating processes significantly more difficult because a bot cannot be programmed to follow a simple set of rules when interacting with unstructured data.
How To Use RPA for Unstructured Data Analysis
While unstructured data may be a challenge for typical RPA bots, it also makes up a vast majority of data. Any company that wants to derive value from its unstructured data needs a way to automate processes that rely on unstructured data.
RPA cannot work with unstructured or semi-structured data on its own. For RPA technology to understand and analyze unstructured or semi-structured data, it requires artificial intelligence (AI), machine learning (ML), and natural language models (NLM) to help process the data.
These intelligent technologies can understand much more than an RPA bot can. They can transcribe audio, comprehend an image, and pull text from PDF files to make it searchable. Once the data has been transformed by these tools, business process automation tools can use data that was once virtually inaccessible to them.
Boomi iPaaS Enables Intelligent RPA
Data discovery and standardization is the first step in realizing the value of RPA with your semi-structured and unstructured data. Building intelligent process automation with Boomi can find and transform your data no matter where it lives, allowing you to automate even more processes.
Learn more about how Boomi can help your business do more by unlocking the power of integration and automation in “The SAP Integration Planner’s Guidebook.”