Intelligent Process Mining for Better RPA Results

Intelligent Process Mining for Better RPA Results

When looking for new ways to increase efficiency, process improvement has always been the top focus for enterprises. In the digitally abled world in which we strive, automation has taken charge to supply improved efficiency at an extensive scale.  

If your organization has entered the realm of digital transformation and wishes to make that big leap, Robotic Process Automation (RPA) is the way to go. RPA helps to automate high volume, repetitive tasks to improve process accuracy, service efficiency and better cost savings.  

 But what if RPA turns disruptive? 

Although RPA is seen as the go-to method to improve business processes, if it’s deployed in isolation, the chances of operations getting disruptive are high. This translates into highlighting one of the biggest challenges to RPA testing services is spotting the appropriate process to automation, since automating a lawed or dysfunctional process can amplify inefficiencies.   

Intelligent process mining is the ideal remedy for augmenting RPA. By extracting vital information from data, it is a technique for identifying, evaluating, monitoring, and improving business processes in order to get rid of bottlenecks and redundancies. 

 Let’s examine intelligent process mining in detail and see how it helps to improve automation processing. 

 What Exactly is Intelligent Process Mining?

The phrase “intelligent process mining” is widely used to refer to process mining that is AI- or ML-powered and emphasizes the use of specialized software that makes use of artificial intelligence. Reconstructing a virtual picture of the business process is one of the main advantages of intelligent process mining for global organizations. Process mining solutions make use of log information produced by enterprise systems like CRM, ERP, HCM, etc.  

There are three levels to process mining which are identified as:  

Process Discovery– It can be called the most widely implemented version of process mining. Discovery covers the utility of event log information to independently create a process model. Such a process basically leads to the building of a simulation (digital twin).  

Process Conformance– The goal of conformance is to determine whether or not the finished process model has been implemented in practice. Any variances from the desired model can be identified by comparing the process description to a current process model that focuses on event log data. 

But what if RPA turns disruptive?  The final level of process mining involves extending or improving a model using data from an event log or any other data sources.  

ImpactQA_-_Process_Mining.jpeg (1)

Value Add to RPA Using Intelligent Process Mining

Looking at the emerging intelligent automation technologies, the imperativeness of intelligent process mining is gaining supreme prominence. To support this statement- according to a Gartner poll, eighty percent of finance experts believe finance must dramatically accelerate its deployment of digital technology such as RPA and artificial intelligence to better satisfy the business by 2025. In fact, CFOs consider investing in process mining to be critical in maximizing the technology’s returns when using RPA.   

The power of process mining provides a complete blueprint of a company’s existing (as-is) arrangement of processes. The RPA (Robotic Process Automation) team can use this knowledge wisely to construct effective automation. 

Process mining software plays its part in providing pre-automation historical data points together with the upstream and downstream effects of automation. On the other hand, RPA solutions lets you evaluate post-automation markers of precision and productivity. 

With intelligent process mining, machine learning and AI aid in the automation of process mining activities and the simplicity of modelling and prediction attributes. You might find it a mix of data analytics and intelligent test automation; however, it’s not similar. Machine learning methods, for example, enable users to analyze and uncover business processes using Java, Python, R, and other programming languages.  

When human interventions are involved, the advantage of constructive process intelligence combined with RPA is nothing short of providing value. This includes:  

  • Recognize any inefficiencies that are not known or clearly evident  
  • Determine ineffective human-digital manual tasks or vice versa 
  • Detect robotic process enhancements that can eventually free up digital workforce cycles  
  • Deliver quantitative data focused on the financial impact of digital workforce based on process 
  • Strike a comparison on the basis of cost, efficiency, and productivity 

Suggested Read

Top Challenges to Enterprise Test Automation & Ways to Break Them

 Utilize the Right RPA Opportunities

At the moment, the most persistent business difficulties revolve around comprehending processes and determining the priority of high-grade vs low-grade activities. According to recent statistics, the various methods involving the identification, prioritization and documentation account for about half of the bot development lifecycle. 

Here are four important frequent RPA difficulties addressed by an Intelligent Process Mining system, which improves both implementation and at-scale sustainability: 

  •  Delivers a unified end-to-end view of real process execution across several business applications in order to identify prime automation opportunities as well as any side effects. 
  • Allows for measurable, data-driven return on investment calculations based on: the number of transactions; the number of process stages; the process AHT/TAT (time); and the cost per transaction. 
  • Identify high-value automation candidates quickly using actual process execution data that shows all process variants as well as time and cost impacts. 
  • Eliminates the need for time-consuming, costly, and often subjective manual process review.

Final Say

Regardless of the number of systems utilized to hold your data, a Process Intelligence platform can help you enhance every area of Robotic Process Automation — from start to finish. Converting process data from a variety of IT systems into actionable insight leads to more accurate automation decisions that are made faster and at a reduced cost. Intelligent Process Mining provides the assurance that data-driven decisions will have a long-term influence on any area of service delivery. In brief, Intelligent process mining technology allows you to ensure that your processes run at optimal efficiency in less time and at a lesser cost.  

To run and administer such high-end automation technology for your enterprise systems, you need a skilled process architect. ImpactQA provides RPA testing services managed by experienced professionals. One of the primary advantages is the incorporation of intelligent process mining for accurate automation. Contact us today to discuss your project requirements!  


Subscribe to our newsletter

Get the latest industry news, case studies, blogs and updates directly to your inbox

2+3 =