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SmartData Collective > Software > Marketing Automation > Managing Data Tasks with Robotic Process Automation
Big DataData ManagementMarketing AutomationWorkforce Data

Managing Data Tasks with Robotic Process Automation

Rehan Ijaz
Rehan Ijaz
5 Min Read
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Analysts predict that by 2020, Robotic Process Automation solutions will see a compounded annual growth rate of 60.5 percent. Employers are increasingly looking to robotic software to automate many of the data tasks currently completed by humans, for clear reasons, but the process isn’t as cut-and-dry as it first appears.

Contents
What is Robotic Process Automation (RPA)?Will Robotic Automation replace workers?So, what is the ultimate impact of RPA on organizations’ efficiencies?

To understand the true ramifications of RPA when it comes to data tasks, we have to look a little closer at what the technology means – and where it still needs improvement.

What is Robotic Process Automation (RPA)?

Let’s just start with the obvious here. Robotic automation is all about finding repetitive tasks, often with high volume, and automating them. The best example of this is data entry. Instead of using a human to input the items, software robots are programmed to do the tasks independently. In the past, organizations needed to employ entire teams assigned to inputting critical data that could then be used for analysis and to make critical business decisions. With robotic process automation, algorithms are assigned based on uniform things that happen and actions are then determined. Predictable tasks are targeted and then assigned actions based on the needed outcome. Instead of an employee typing in every detail, robotic software streamlines it and removes the need for an actual person to do it all.

Will Robotic Automation replace workers?

The current RPA technology does not have the potential to replace an entire workforce. Why? Because most workers don’t have a job that consists solely of repetitive tasks that need no other brain power. The human element makes a difference in the quality that is produced. There are some tasks that robotic automation can do better, and faster, but those tasks are limited in scope.

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Right now, RPA is not designed to overthrow entire workforces – at least not if the company leaders want to keep their customers and clients happy with the quality that is produced. For example, an analyst may be required to deal with large data sets in an effort to think critically about the business and make recommendations to marketing and sales organizations. Robotic process automation could complete time-intensive data entry tasks giving the analyst more time to complete critical thinking activities that require a human. The organization still needs the analyst role but gains efficiencies from the employee while increasing the quality of their work.

So, what is the ultimate impact of RPA on organizations’ efficiencies?

Until robotic software has the power to reason, it has to be just one component of a larger workforce. Robotic Process Automation is best used alongside human workers who can analyze the data tasks and ensure quality standards are met. Just like innovations of the past, workers fear that robotic process automation may claim jobs and change the mix of necessary job skills. In IT support roles such as software testing, data entry, and support desks these fears may be realized in the near future. For most IT professionals, such as software developers, RPA will be a relief from mundane tasks.

The biggest selling point of RPA is that it has the ability to take some of the tedious data tasks off the plates of workers, improving overall company productivity. Humans get the chance to do more cerebral tasks when they aren’t bogged down by the data that robotic software is capable of completing. With less time stuck in data tasks, more innovation can happen. Companies should strive for RPA implementation that benefits workers and allows them more time to do what they do best: act human.

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ByRehan Ijaz
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Rehan is an entrepreneur, business graduate, content strategist and editor overseeing contributed content at BigdataShowcase. He is passionate about writing stuff for startups. His areas of interest include digital business strategy and strategic decision making.

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