Using RPA Analytics to Improve Business Processes

4 min read
RPA Analytics

RPA software gives small businesses and large enterprises the ability to make their business processes more effective by improving compliance and bringing down the time required for implementation. However, it can do a lot more than that. One of the great, but often overlooked, benefits of RPA is its capability for analytics.

The digital workforce of software robots is easily traced and auditable, which means that RPA software accumulates a large amount of data about the automated business processes that can be used to analyze and streamline operations. As a result, RPA analytics allows the company to continuously improve its performance against set automation goals and benchmarks and maximize its ROI.

What is RPA analytics?

RPA analytics is the set of tools, technologies, and techniques for discovery, interpretation, and communication of important patterns in data about the performance and health of your automated business processes. Another critical application of RPA analytics is applying the discovered data patterns for making decisions related to changing and improving the existing RPA solutions.

The combination of RPA and data analytics allows us to quickly identify bottlenecks in the automated business processes and proactively correct them. One of the benefits of this solution is that it allows excluding “guesses and assumptions” from the process:

  • the RPA tool gathers a large amount of data about the process execution
  • the data analysis tool processes and visualizes the data so it can be easily interpreted by SMEs
  • As a result, SMEs make their decisions based on solid data, not “gut feelings”
Example: An RPA bot reads data from scanned invoices and puts it in the SAP application. If an exception occurs, it is sent to an employee to process through a manual task. Using RPA operational analytics, an SME can see how long it took the bot to extract the data from the invoice, how many exceptions were sent to employees, how long it took to record the data in SAP and if any error occurs, and whether there was any downtime for the bots. Based on this and other data, the SME can tune in the business process to make it more effective.

However, how can you make sure that you pay attention to the important metrics in the operational analytics you receive and make the most out of that information? You need to know what to measure.

What to measure?

Robotic process automation data analytics collects, analyzes and visualizes a lot of data about your automated business processes. However, there is no use digging deeply into data that might not be valuable. To continuously improve the performance and accuracy of the processes, you must define what you need to measure and track. Usually, the following important metrics are gathered in RPA operational analytics:

1. Speed. Speeding up the execution of processes is one of the main goals organizations want to achieve through implementing robotic process automation. Monitoring the speed of your RPA robots will help you find the bottlenecks in the process execution, like: how long it took to execute the process each time, which tasks took up the most of processing time, what data (document, etc.) volume was processed within the process execution, and other similar metrics.

2. Accuracy. To be able to improve the accuracy of the automated processes, it is important to track any issues that occur during the execution, such as:

  • the number of process executions and transactions with issues
  • tasks that fail during the execution and the reasons for failure
  • exceptions that are sent to employees for manual processing

3. Capacity. Tracking the capacity of your RPA solution in the operational analytics will allow you to see its workload and understand where optimization may be required. It includes analyzing:

  • the volume of transactions per process execution
  • transactions by work type
  • resource utilization (CPU, RAM, HDD space) by different components of the tool (RPA, OCR, and others)
  • availability of bots during execution
  • bot session details
  • statuses and sessions distribution by volume

Depending on your solution and automated business processes, there might be other metrics that you should track.

Automation analytics in WorkFusion

Intelligent Automation Cloud (Enterprise Edition) is delivered with built-in automation analytics that allows you to get real-time workforce insight into the automation processes, teams, and operations, as well as make grounded decisions based on predictive analytics, showing how your processes will perform in the future.

Apart from advanced RPA operational analytics, you will get insights into the whole Intelligent Automation solution with help from artificial intelligence (AI) analytics in the pre-built dashboards. Analytics tracks the critical metrics you need to know: processes overview, the speed of execution, the solution’s capacity, the performance of the AutoML (machine learning) and RPA components, and manual task processing.

Automation analytics in WorkFusion

In case you want to see more data about your business processes, you can create custom dashboards.

How to learn automation analytics

Given the importance of RPA and AI analytics in Intelligent Automation, we have built a special course in Automation Academy available to WorkFusion customers and partners. It gives students the knowledge required for efficient work with analytics in Intelligent Automation Cloud, including:

  • the complete overview of the reporting mechanism
  • the main features of the tool’s analytics
  • key metrics, filters, and actions available in the analytics dashboards
  • analytics components, licensing, and compatibility matrix

After completing the course, a process owner will be able to get the most out of WorkFusion automation analytics even without a strong technical background.

Interested in learning WorkFusion Analytics?*