Data Analysis – the Overlooked RPA Opportunity


No one disputes the efficiency benefits of Robotic Process Automation (RPA).  But many early adopters of RPA are focusing exclusively on immediate cost savings, and in doing so are failing to leverage the technology’s ability to drive significant and ongoing business improvement.

Once implemented, an RPA solution immediately produces metrics and data that provide critical insight into operational performance. A traditional six sigma program would involve months of data capture, analysis and reporting to achieve what RPA does essentially from day one as a matter of course. With a robot, in other words, the measurement and analysis of performance are built-in.

The key opportunity that many are missing is to apply the reporting and metrics provided by an RPA solution to improve that solution.   For example, when first deployed in a claims processing operation, an RPA solution might automate 70 percent of incoming claims, with the rest being tagged as exceptions and routed to human reviewers who are trained to adjudicate the exceptions based on the insurer’s criteria.

The question then becomes how to reduce the rate of exceptions and increase the automation rate to, say, 80 or 85 percent? In some cases, a simple revision of the robot’s script will reduce exceptions; in others, the script requires more complex changes. It’s also important to aim for a sweet spot of automation and recognize the exceedingly rare exceptions where it’s not cost-effective to invest the time to teach a robot to process the claim. Again, the data the robots provide can be applied to make these adjustments.

In addition to improving the performance of a specific function such as claims processing, performance data from RPA solutions can be applied to ensure enterprise-wide integration, balancing of workloads across the enterprise and agile responses to peaks and valleys in demand for resources.  Clever and diligent as they are, robots are not immune to interdepartmental backlogs, so if one group of robots falls behind, another group can stand idle for hours.

Optimizing a robot’s utilization rate – both within a particular function and across an enterprise – is essential to a successful RPA solution. The fact that robots can work round the clock and don’t command a wage doesn’t justify inefficiency.  Robots cost money to deploy and operate.  As the pioneers of RPA deployment step back and consider what “version 2.0” has in store, I suspect many will recognize the opportunity to leverage RPA data to drive business improvement, and will aggressively develop the expertise needed to analyze that data and make it actionable.