Many organizations are going through a digital transformation for many reasons, whether it is to automate repetitive tasks, to better utilize their valuable resources, or to optimize production. Depending on the use case, there are advantages and disadvantages to using an application programming interface (API) over a robotic process automation (RPA) platform or a combination of the two. Especially with the recent pandemic, having these automated processes integrated in operations allows the organization to receive data that might have required to have someone onsite. What many organizations need to realize, is that both of these technologies should be considered for their digital transformation, but for different applications.

Simple versus complex integrations

The first step is to have a plan and identify what process would benefit from being automated. RPA is ideal for simple integrations. RPA platforms are optimal to implement in order to increase efficiencies and to address routine tasks, and really aren’t ideal for complex ones. Because a simple change can cripple an RPA integration, the best tasks for RPA to handle are simple, repetitive, and rules-based. An API on the other hand by definition, is a set of commands, functions, protocols and objects that can be used to create software or interact with other systems. While RPA and API technologies can be complementary, API management tools can handle more complex data and application integration issues without a lot of user intervention or maintenance. Simply put, APIs manage tasks and data on the backend and RPAs manage more front-end data.

As more businesses want to link systems and data together, APIs make that happen. Have you ever searched for flights on a website that grabs data from all of the major airlines to see availability for the best price? Those are several active APIs at work to bring that data together in one place. An application ideal for RPA is within the supply chain and automating delivery schedules and customer service. Since RPA is capable of pattern recognition, it can greatly reduce the amount of manual data entry along with any other human errors that might coincide with those tasks-and a faster pace.

Both of these technologies have a lot to offer an organization, it’s just important to know what would produce the most ROI otherwise the digital transformation that takes a lot of work to implement to begin with, won’t produce the maximum, desired results.

Understand API and RPA challenges

Having an API strategy across an enterprise can definitely benefit the organization, but there are some challenges. One of the difficulties of APIs is that in order to use them, programmers need to connect the API method calls to solve the issue and to see the value – this takes a lot of time and effort. And if something needs to be changed with the API, this will also suck a lot of time out of your programmer’s schedule and will be costly. There are many aspects CIOs need to consider with their IT crew to make an API strategy successful and that means prioritizing API development based on the organization’s business strategy to see how that strategy can come together and benefit the end user and add value. Depending on the organization’s infrastructure, cloud migration may be another hurdle. More businesses are changing to a hybrid cloud environment meaning that the API needs to be able to collect data that is both on-premise and in a private cloud environment.

Another challenge is historically, APIs were in a language that only programmers and IT professionals could comprehend. Now, it’s critical to develop an API taxonomy that stakeholders and business owners can translate so they can take part in the design and development process- maximizing ROI. According to McKinsey, a sound taxonomy reduces API analysis time by 50 to 75 percent, increase adoption by 30 to 50 percent, and increase value realization by 25 to 50 percent.

Similar RPA challenges exist when there’s not a solid strategy before the implementation phase. Goals and processes need to be understood well before an RPA bot is in place. Questions that need to be asked are what is the new workflow with the RPA platform? What is the maintenance schedule? How can we now better utilize the existing worker who used to manage this process to better utilize their talent and skills within the organization? Most of all- who is going to measure that success and what will that data-collecting process and analysis look like? The RPA’s architectural framework is critical to consider to ensure the tool you’re using is the best one for the process you want to automate. Most importantly, RPA plugins may be reusable for another process within the organization- significantly cutting software development time which is a big challenge whether consider RPA or an API.

Depending on the organization’s budget, API development is costly. The good thing about RPA is that it could possibly be a good temporary fix to integrate data if an API isn’t available for a particular application as long as they are routine actions that are to be automated. It’s also critical to note that with an API, security is a big strong point, RPA- not so much. A well-designed API can validate actions. However, an RPA tool that has unrestricted access to mirror human actions can be vulnerable to an attacker and obtain access and possibly cause harm.

Maintenance

Whether you’re working with APIs or RPA platform, maintenance can be an issue if you don’t have a solid strategy in place. The API gateway is the location for all of the APIs that can either be a centralized platform or can also be cloud-based. In the cloud, an organization may overcome some challenges by having several different teams take the lead for certain sets of APIs. API development can either be in the cloud or kept on-premise.

When implementing an RPA solution with no outside partner, it’s vital that everyone within the organization be aligned with automated processes so management can identify progress and for other teams to identify any issues and be proactive before something in the process breaks. Whether an organization is implementing APIs or RPA processes, a team of people need to be leading the charge in these efforts to ensure they’re running properly. Since RPA bots rely on UX to complete tasks, errors can be made if something in the UX changes. Any regulatory changes within business processes will then require changes in your RPA bots. RPA will add a new responsibility on to process owners possibly managing automated processes rather than people who used to be doing them.

With the current pandemic, CIOs are looking at automating processes now more than ever to decrease risk and keep operations running efficiently. Gartner predicts that by 2024, organizations will lower operational costs by 30 percent by combining hyperautomation technologies with redesigned operational processes. By integrating automation into certain processes, it requires a level of understanding to ensure its success, otherwise it’s set up to fail. A digital transformation identifying and integrating one or both of these processes is definitely a cultural change, but with the right automation technologies in place for certain processes, it would take business optimization to a whole new level.

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From ongoing staffing needs to a rich, 24/7 onsite/nearshore model, our Data Science premium support teams are tailored specifically to meet your needs. Hire an IT resource that’s relatively within the same time zone, cost-effective and easier to access than most offshore services.

Since we’re highly proficient Data Mining and Big Data specialists, our Nearshore team can extract the critical knowledge and insights you need from your structured and unstructured data.

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