Data Management software is essential to providing organizations with critical insights about their customer’s behavior. Robotic process automation (RPA) is a process in which software programs perform repetitive Data Management tasks, such as data validation, email responses, normalization, and metadata organization.
Put another way, RPA automates the mundane. It does this by observing and imitating a human’s behaviors as they interact with a graphical user interface (GUI). For instance, a series of tasks recorded in a GUI (such as buttons clicks and cursor moves) can be transferred to an RPA wireframe, which then translates them into code. Those tasks can then be performed automatically by robotic process automation, without human intervention.
Robotic process automation is a rapid, easy-to-install, low-cost way to automate existing processes, and improve Data Management. Vadim Tabakman, Director of Technical Evangelism at Nintex, said:
“RPA can help save an organization money by automating any repetitive task that a human does with keyboard and mouse, as well as tasks in legacy systems that can’t be accessed via APIs and web services. RPA bots accelerate ‘low-hanging fruit’ processes in every business, like opening email and attachments, filling in forms, reading from and writing to databases, making calculations, collecting social media statistics, and extracting data from documents, all very quickly.”
Machine learning algorithms can also optimize the RPA process for some graphical user interfaces. This can be done by applying them to perception problems, such as visually recognizing a problem in a product as it moves down a conveyor belt. With the graphical nature of robotic process automation, image recognition capabilities are well-suited for RPA assignments.
The Graphical User Interfaces
A GUI is generally considered to be much more user-friendly than other interface systems, such as the ones using text-based commands. It is a software system that uses interactive visual components (for example, a pointer and icons). This is accomplished by displaying icons/symbols that communicate information and represent responses the user can understand. These objects include buttons, icons, and cursors, and can change size, color, or visibility when interacted with. The graphical elements can sometimes be enhanced with sounds or with visual effects, such as transparency and drop shadows.
One of the GUI’s greatest strengths is its user friendliness. It is much easier to learn than other operating systems. This is because text commands don’t have to be memorized, as the system uses icons and clicks. Nor do users need to be familiar with programming languages. GUI systems have dominated today’s computer market with their easy-to-use controls and modern appearance.
RPA allows people who do not write code to create workflows using wireframes, such as a GUI. An RPA can provide users with a GUI interface, which they can use to organize, arranging the steps in a data-processing workflow. Robotic process automation also allows people to interact with other parts of the software through the GUI. (An RPA also records its interactions with humans, so that appropriate responses can be automated.)
Machine Learning and Robotic Process Automation
Machine learning models, such as image recognition, can be inserted into RPA workflows to perform machine perception tasks. With this combination, ML can perform tasks (for example, visual inspections) that humans normally perform in less than a second. RPA (also referred to as “software robotics”) is a form of process automation technology. It can be used for a variety of tasks, ranging from assembly line inspections to email responses.
RPA can also be used to improve the data’s quality. As the amounts and types of data continue to grow, robotic process innovation provides useful solutions for improving the results for all areas of analytics. RPA, when augmented by ML and AI, can streamline the input (images and documents) while improving the process by monitoring the RPA logs.
Mining RPA Logs
Robotic process automation also keeps a record of the various transformations it makes. These records can be very important regarding regulatory compliance, maintaining transparency, and process optimization. RPA has also expanded into the cloud, performing analytics, especially in retail applications.
As the need for transparency grows, mining RPA logs will become more and more important. Current concerns about privacy and illegal (or unplanned) use of business records make it important to monitor automated processes. This concern has already surfaced in the financial technologies and other regulated industries.
RPA’s potential for solving Data Management problems has significant potential. It can be used to maintain Data Quality in complex circumstances. Having the ability to analyze RPA logs can be quite helpful for a business wishing to improve its Data Quality. It can determine where efficiency was lost, or where processes have been performing below expectations.
RPA and Data Management
Some interesting new possibilities are emerging as robotic process automation has been merged with Data Management. Data Management includes a large number of repetitive tasks as data is collected and processed, which can benefit from automation. RPA can be applied to data repositories, making tasks such as normalization, data cleansing, or the updating of metadata much more efficient. These tasks tend to be unique and are extremely repetitive, providing an ideal situation for applying RPA.
Combining RPA with other techniques can create some very sophisticated data handling solutions. For example, RPA can be used to draw out information from OCR documents, which can then be used to create metadata, or reduce content for big data research or machine learning processes. Applying RPA to data repositories and data processes will make them more efficient. Manual tasks, which are repetitive but also unique, may result in errors and are time-consuming. RPA, however, will deliver automated, fast, efficient work — without human error. Some of the benefits of using this automation process in Data Management include:
- Replacing manual keying (human typing of forms)
- Basic operations (data input, transfer, delete, etc.)
- Mining RPA Logs
- Data extraction
- Improvement of Data Quality through automated tasks
- Screening for irregularities
- Providing input verification for manual processes
- Data back-up
Blue Prism: This tool offers a flow chart with drag-and-drop features that automate a variety of business processes. It is compatible with any platform and with any application. This tool is ideal for medium- and large-sized businesses.
UiPath: It provides assistance for Citrix and is user-friendly for non-developers. It can handle complex processes and is useful for automating any desktop or web apps. This tool allows global enterprises to design and deploy a robotic workforce.
Automation Anywhere: This is capable of using all core capabilities and provides cloud services. It also combines standard robotic process automation with intelligent elements, such as language understanding and handling unstructured data.
Pega: The Pegasystems automation tool will support a variety of usage scenarios. It consolidates architecture using a business rules management system and an analytics decision management mechanism. It can be utilized with Windows, Linux, and Mac, and designed for the cloud.
Jacada: The Jacada RPA is designed for communication centers, establishing interactions, and for customer services. Jacada has combined robotic process automation with desktop automation, increasing accuracy, productivity, and customer satisfaction. This tool supports the automation of redundant tasks that are error-prone and time-consuming.
The Limitations of RPA
Robotic process automation is technology that is fairly quick and easy to install. Unfortunately, it doesn’t always work out. Launches and pilot projects sometimes become “stuck,” for a variety of reasons — which can be avoided. Problems often start with the assumption RPA can be used as a Band-Aid, placed on top of other software. It is designed to automate tasks, not to repair end-to-end business processes. RPA imitates repetitive human behavior that doesn’t require understanding, insights, or knowledge. Codified rules tell the software/computer to perform tasks previously done by humans.
- For a business process to be automated, it must be deterministic (have predictable results or responses). Some business processes are deterministic and standardized, while others are not. Responding to “new” events is still something software and RPA cannot do. Responding to new events is something only humans do reasonably well.
- GUIs can be difficult to integrate. Coding against a GUI often contains hidden pitfalls that APIs don’t have.
- RPA can be slow, comparatively speaking. From a software perspective, RPA is inefficient. However, when compared with human response speeds, RPA is quite fast. (Human response times occur on a scale of seconds, while software response times occur on the scale of microseconds.)
According to Matt Calkins, CEO of Appian:
“While RPA is a pretty good option for efficiency, it still does not handle exceptions well. This is why you need an integrated solution, such as with business process management, workflow, AI, and case management.”
Big Data and RPA
RPA works quite well with big data. It is a tool that both generates Big Data, and provides an analysis, helping to locate bottlenecks and discover other problems. Additionally, RPA can extract data from documents, and reduce content to a usable format for big data research or machine learning processes. The insights gained through the collaboration of RPA and big data help in recognizing issues within the business’s processes and procedures, and allows organizations to streamline their operations. RPA records every step of the process, and provides opportunities for improvement. Robotic process automation solutions can gather data and report on the analytics.
The application of modern data analytics to RPA-generated data helps in unlocking additional procedural insights and provides a substantial understanding of the organization’s structures and workflows. It can be used to identify precise steps for improving an organization’s procedures and processes. This big data approach helps in removing subjectivity and “gut feelings” from the decision-making process, allowing organizations to make decisions based on hard evidence.
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