They say robots are conquering the world, and there is some truth in it. Statista expects the robotics market to grow at an annual rate of around 37.4%. If the trend remains, by 2025 the market size will reach almost 500 billion U.S. dollars. At the same time, data analytics is also looming large: according to BI consultants from Iflexion, the technology brings innovation to smart homes, connected hospitals, and transportation businesses, to name but a few application areas.
Yet the most significant thing about these two worlds, robotics and data analytics, is that they breed the third, a no less striking technology ”robotic process automation (RPA). This software automates human processes and is especially useful for rule-based, structured, non-intuitive tasks based on data. Be it processing of large amounts of customer data or updates of massive databases ”RPA would tackle those in minutes.  Â
With all of its capabilities, RPA started attracting business owners’ attention predictably soon. As most of these businesses continuously seek ways to reduce costs and production times, robotic automation is gradually becoming their nostrum, especially considering more and more success stories surfacing.
Indeed, as stated in a McKinsey interview with an RPA expert, six case studies of the technology implementation show a return on investment that varies between 30 and 200 percent in the first year.
How RPA advances data analytics
RPA streamlines robotics, but it also brings data analytics to a whole new level. With RPA, organizations can tackle bigger amounts of data in minutes, deriving even more benefits from it. What is more, robotic automation aids in collecting cleaner data, helps business people get a better idea of what’s on in their operations, and allows generating reports automatically. Here are the details.
RPA accelerates complex business processes
In companies where time-consuming, mundane work like processing routine claims and handling data entry tasks is common, automation is a saver. That’s why banking and insurance domains seem particularly passionate about RPA: credit card and claim processes usually take a wealth of staff’s time.
Let’s take banking as an example. Traditionally, for providing a credit card or a loan to customers, bankers need weeks to validate customer data and approve the application. Bankers usually make decisions based on a range of parameters, including credit history, application validity, background, and credit score checks. Unfortunately, looking through all of these is not a matter of two days.
The long waiting time is disappointing for customers and also costly for bankers. RPA bots tackle this as they talk to multiple systems and data sources simultaneously, collecting all necessary data and unifying it, all within minutes.
An excellent illustration here would be a case of an insurance firm, mentioned in a McKinsey interview cited above. The firm needed at least two days to handle 500 premium advice notes; with RPA, it can now be done in about 30 minutes.
Generally, by liberating employees from data management routine, RPA enables them to make more decisions in a given period of time. It’s no less good that staff can focus on more creative tasks like customer inquiry processing.
RPA keeps data clean
It’s not without reason that business owners avoid poor-quality data with errors, duplicates, or inconsistencies. Among far-reaching effects of dirty data are business people’s inability to produce objective reports and forecast customer behavior. As a result, because some dirty data once butted into a database, businesses can lose time, money, and customers.
The ability to keep data clean is one of the greatest RPA benefits usually mentioned in terms of business operation. As long as rules drive RPA software, it’s incapable of making random errors by its very nature. That’s different among humans, though: typos and mistakes caused by inattentiveness are common for most of us.
So, as RPA is programmed to be precise, it returns correct and consistent results only. That’s because RPA bots track data in the same format, using the same units.
For example, when collecting their customers’ data and tracking their last and first names, business owners might want all of the customers’ names to follow the Last Name, First Name format, not First Name, Last Name . With RPA, all customers would be automatically tracked like Smith, William and Johnson, Johanna. The same will go for emails, phone numbers, and addresses ”RPA keeps it all consistent and clean.
RPA bots can work 24/7; they don’t take sick leaves and have no downtime, which makes them available for immediate use, on weekdays and weekends alike.
RPA takes over-reporting
Reporting is widely known to be a time-consuming task. However, with RPA’s assistance, it might take much less effort and, again, time. Using RPA bots, businesses can work out parameters and rules applicable to a particular type of report so the software could create them by automatically filling in necessary data.
This limits human intervention into reporting to exception handling, checking the accuracy, reviewing drafted reports, and monitoring logs generated after the automated process is complete.
With RPA taking over this process, business people can also enjoy more high-quality reports free from occasional mistakes and typos.
A couple of other RPA perks, brieflyÂ
These were basically the most prominent advantages of RPA bots. However, robotic automation conceals a bigger potential.
As RPA aggregates great amounts of data in record times, it allows for a better understanding of ongoing business processes. Insurance claim processing, for example, consists of several steps: an insurer has to check a policy holder’s details, assess claim commences, decide on the payment, and more. Under the pre-RPA set-up, this data could have come in a whole variety of forms, and an insurer would have to look in different places to find it: paper-based claims, email inboxes, in-house documentation, and guidelines. RPA allows generating data for every step automatically, which delivers a detailed and better-structured data trail overall.Â
Possessing such well-structured, comprehensive and quality data, business owners can build better simulation models when trying to bring a novelty to the operations. In such a way, RPA aids in answering what-if questions and introduces hard facts to where decision-making was mostly based on gut feeling.
Taking the guesswork out of analytics is good, but what if one is looking for ways to optimize the process but doesn’t know exactly what they want to change? Here’s where the blend of machine learning and RPA comes in. There are quite a few ML algorithms capable of telling a business owner what they can change in their business process to maximize the benefits. Feeding ML algorithms with RPA-generated audit trails, a business owner can get more objective, data-driven recommendations.
RPA: Robopocalypse or a Blessing?
RPA is great, but this is definitely not the robots will take all our jobs’ tale. In reality, the technology’s benefits are not to be exaggerated: to be effective, robotic automation needs a decent set-up and regular monitoring. The good news is, only humans can do this for now.
All in all, the future of humans vs technologies’ relationships is about to become clearer and better, and robotic automation will totally make its mark in it.
