Robotic Process Automation (RPA) is emerging as the leading technology for process automation and is giving companies the ability to make great strides in executing business activities more efficiently. While RPA is credited with being transformational due to its automation capacities, one of RPA’s under-discussed features is its ability to provide operational analytics.
RPA generates and gathers data that allows companies to analyze and act on advanced metrics. This not only helps organizations streamline business processes, but it also allows companies to more quickly achieve specific benchmarks and goals.
For example, we know RPA software robots can process an order, but they can also provide information about the amount of time it takes to complete the transaction and if any exceptions were generated during the process. This window into a company’s operational structure can be a key driver in leveraging lean processes and strategies if companies adequately review this data and implement its lessons.
But what exactly can we learn from these operational insights? And how can organizations make the most of these lessons? To discover the answers to these questions, we’ll discuss the different kinds of information RPA can provide on organizational operations, where this data comes from, and how companies should use operational analytics to streamline their processes.
RPA software robots can collect large volumes and varieties of information on customers and their buying preferences. At the same time, RPA generates data on business operations because RPA software robots record and monitor their own steps. This can tell companies a great deal about how efficiently their internal activities are executed - for example, RPA can reveal the time it took to process an order, the number of outstanding transactions, as well as the processes that generated exceptions and needed intervention by a human employee.
But it’s the analysis of this massive volume of data—on both customers and business activities—where RPA can become incredibly useful. There are two important kinds of analytics are worth noting here: Operational analytics and customer analytics. Operational uses data on a company’s business operations in order to drive operational improvements, while customer analytics deals with analyzing customer data in order to attract and retain customers.
In many cases, operational analytics is being emphasized over customer analytics. This strategic shift can be explained by the relative financial benefits delivered by each solution. In their whitepaper Go Big: Why Companies Need to Focus on Operational Analytics, Capgemini suggests that “Analytics in operations is increasingly seen as a strategic priority for organizations. Over 80% of respondents agreed that analytics in operations plays a pivotal role in driving profits or creating competitive advantage.”
Operational analytics allow companies to optimize their internal workings to create positive business outcomes, an improvement that benefits the company but also its customers.
When compared to the number of companies taking advantage of operational analytics, a much smaller percentage of companies focus on customer analytics alone. This is because, at least in the manufacturing sector, data-driven operational improvements account for $117 billion in revenue while use of data in the front office only accounts for $38 billion. Furthermore, RPA’s operational analytics can help to realize similar figures across a number of industries including healthcare, retail, and insurance.
The potential growth and development for a company as a result of operational analytics make it an attractive option for organizations that have already implemented RPA and are looking for ways to enhance their operations even further. Unfortunately, a majority of companies with RPA solutions already in place are not making use of operational analytics - at least not yet.
Alsbridge suggests that “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…[and] to apply the reporting and metrics provided by an RPA solution...”
Furthermore, research by Capgemini suggests just thirty-nine percent of the companies questioned had initiatives that integrated operational analytics with their business activities.
The biggest contributing factor to this lag in the use of operational analytics is making sure all employees are on-board. Changes that accompany implementation of new technology can be challenging for employees, especially when they impact existing tasks. RPA does most of the analytical work itself, but it’s up to the leaders and workers of the company to act on this information and make on-going decisions to improve business processes and automation capabilities.
These responsibilities did not exist before RPA implementation. Thus, it’s essential to push employees to realize the benefits of operational analytics and to persuade them to incorporate these considerations into their behavior. While this might be an additional challenge for companies implementing RPA, industry analysts suggest this will be well worth the added effort and will help companies realize the benefits of operational analytics and view this system as a value added proposition.
Through the use of RPA and operational analytics, organizations stand to gain valuable insight about the efficiencies and inefficiencies within their business activities. They’ll be able to identify bottlenecks and disruptions and update their workflows accordingly to leverage leaner, more responsive operations. More specifically, operational analytics will allow companies to achieve:
Increased process efficiency. By eliminating weak spots within processes, organizations will increase the productivity and accuracy of these tasks. These activities can include any back office tasks such as claims processing or purchase order issuing.
Improved employee utilization. Rather than being focused on back office tasks that can now be automated, employees will be able to best utilize their time and focus on high-value tasks, such as applying RPA analytics to streamline business activities.
Higher levels of activity. Reducing process bottlenecks frees time for software robots, meaning that they’ll be able to complete more tasks more quickly. This is crucial for the operations of companies dealing with large volumes of transactions on a regular basis.
While it’s of course important to recognize the other benefits of RPA such as cost reduction and accuracy improvement, it’s just as important for companies to make use RPA via its analytics capabilities.
Rather than getting caught up in the short-term benefits of RPA, companies need to evaluate the long-term benefits of operational analytics and gradual process improvement.
Only by integrating operational analytics, getting employees on-board with the technology, and responding to the insights and information gained will organizations be able to benefit from the advanced analysis RPA can provide. By acting on this information, organizations will be able to optimize the efficiency of business activities and increase the number of positive organizational outcomes.