Data is the lifeblood of any business today, but for every innovative or exciting big data analytics operation that seeks to transform how a company uses its information, there may be hundreds of smaller, but no less essential, operations that depend on having the right data.
Many of these data entry or retrieval tasks can be repetitive, slow and boring for the workers who have to deal with them, and as a result, this can lead to more inefficiencies, a higher risk of errors and lower staff morale.
But this doesn’t have to be the case. By developing automated tools to take these processes out of the hands of manual workers, companies can boost efficiency, improve accuracy and reduce costs. And one of the best solutions for this is robotic process automation.
Robotic process automation, or RPA, is defined as a type of software program that is able to interact with, interpret and manage data. Its primary purpose is to automate many of the everyday activities that would be too labor-intensive, time-consuming or repetitive for a human worker to be efficient at performing.
In this way, RPA may be viewed as a 'virtual workforce', taking on unpopular but necessary back office jobs and freeing up human employees for more complex or creative tasks. The robots are programmed to follow a specific, precise set of functions and obey clear rules, so can be considered as a limited form of artificial intelligence, mimicking what a person would do in the same circumstance.
This means they can tackle tedious processes much more efficiently than a human operator would be able to. For instance, in the banking sector, a customer applying for a credit card may provide a range of information that has to be entered and verified, which is a tedious process for a human. But as this process is highly systematic, an RPA can gather all the required documents from the individuals, make the necessary credit and background checks itself and make a decision about whether or not an individual is eligible for a credit card without human input.
What's more, because the machine can be relied on to complete the process in exactly the same way every time, there is no chance of errors creeping in. Plus, the robot will never get bored at having to perform the same operations over and over again.
However, since they can only complete the set tasks they have been instructed, RPAs have their limitations. Therefore, they are best suited for use in applications where they are dealing with highly structured datasets that have clear fields and rules that they can follow.