Deploying RPA requires careful planning, robust design principles, ongoing management, and possibly the use of more advanced technologies, such as AI and machine learning. Here are some best practices to ensure success.
As a market that is forecast to reach more than $18B by 2030, Robotic Process Automation (RPA) solutions are clearly seeing significant adoption by enterprises around the world. However, like any new tool, the rush to capitalize on RPA’s capabilities has sometimes resulted in less-than-optimal results. This is due primarily to applying the wrong technology to the wrong problem. In order to maximize the value of IT’s investment in RPA, a clear set of appropriate use cases and best practices has emerged that shows how and where RPA can have the most impact on your business, streamlining operations, reducing costs, and supercharging productivity. Let’s review the five most critical elements that need to be considered when shopping for an RPA solution.
Find the appropriate RPA use case
The key to identifying the ideal use case is to find processes that are capable of operating efficiently without being completely automated. Some applications, such as customer feedback analysis, sales, or quality control, cannot be completely automated since they require some level of human oversight. As a result, such use cases are not well-suited to being used for RPA.
Use cases that deal primarily with unstructured data are also not ideal for RPA technology. Unfortunately, that can be a sizable amount of a business’s data. Some estimates place the amount of unstructured data in the average enterprise at 80 to 90 percent.
Process complexity is another issue that needs to be considered. The more complex a process, the more prone it may be to failure when fully automated. IT needs to select processes that are relatively straightforward. IT can conduct process mining as a means of due diligence to identify and prioritize which processes will be best suited to automation via RPA.
Perhaps most importantly, IT should select processes that are going to deliver a clear Return on Investment once they’re automated. While RPA is cost-effective, it’s not free, so the applications or processes you choose to automate should deliver a clear return when done correctly.
See also: What RPA Is, What It’s Not, and Why It Matters
Screen-scraping or API? Choose wisely.
These two data extraction methods are critical to the success of any RPA implementation and yield similar results but take different approaches to meeting the same goals. Screen scraping, also known as terminal emulation, is a method for collecting visual data (scraping) from a User Interface (UI). That data is then automatically applied to other programs or systems without requiring a human to manually re-type the data. An API, as most of you are already aware, allows two separate programs to interact and exchange data automatically.
The API approach offers multiple benefits, including efficiency and ease of use. There is usually no question of legal issues around the data being gathered, as you have permission from the host to access the data. The API approach also eliminates issues such as JavaScript rendering and CAPTCHA avoidance, making it ideal for collecting large amounts of data quickly and effectively when compared to Screen-scraping.
That said, you are at the mercy of the API provider, which could limit the number of data requests in a given period or be based on geolocation. The API provider could also change policies, putting your ability to extract data at risk.
By comparison, screen-scraping or web-scraping can provide significant scale and time savings compared to using an API. It can also enable greater data accuracy and provide a more diverse set of data sources. It is often much more cost-effective as well. That said, one needs to be very careful with screen-scraping, as it could potentially violate copyright and trademark laws, as well as terms of service agreements for some websites. Some websites may also block scrapers, making it difficult to collect data.
Which approach you elect to employ is often dependent on the data sources being leveraged, as well as an organization’s approach to data governance, which is an excellent segue into our next critical element.
Establishing appropriate data governance
Implementing effective data governance is something all enterprises should make a priority and is a subject that could fill its own books. Good data governance is critical to ensuring everything from regulatory compliance to data privacy and customer satisfaction. Leading industry analyst firm Gartner estimates that by 2022, nearly 100 percent of corporate strategies will view data as an enterprise asset. Most importantly, it transforms an enterprise’s data into an asset and makes it usable by technologies like RPA.
As a result, it’s very important that organizations build programs with the basic tenets of effective data governance. An effective data governance program will establish roles, rules, and policies that will maximize the value of an organization’s data. This also means outlining the requirements for the development, implementation, approval, ongoing monitoring, and performance evaluation of automated applications and processes.
Ensuring RPA security
As with all applications, ensuring security and mitigating risk is critical to an RPA implementation’s success. A good first step is to assign specific identities to each RPA bot, which includes dedicated naming requirements and authentication procedures. This approach lays the foundation for accountability at the process level. IT should also deploy account restrictions for which applications can be accessed and well-defined permissions on behavior.
Once these parameters are established, IT should make sure the RPA system provides detailed and accurate logs of bot activity. Finally, RPA scripts should be regularly reviewed and updated to ensure accuracy as well.
Serving as the “last mile” of integration
The “last mile” in RPA refers to completing the final, often most difficult and intricate, parts of process automation. Issues such as integrating with legacy systems, process variability, and scalability can be major obstacles, but automating these steps also provides the greatest ROI. These challenges highlight the need for careful planning, robust design principles, ongoing management, and possibly the use of more advanced technologies, such as AI and machine learning, to support your own RPA deployment.
Taken together, these elements provide IT organizations with an effective blueprint for avoiding the biggest potential pitfalls for any first-time implementation of automation via RPA. When followed carefully, a business can begin reaping the productivity benefits and cost savings of RPA immediately.
