How process mining can unlock the value of hyperautomation
In search of efficiency and streamlining of operations, companies have turned to AI-based automation. As they pursue this, they need a way to look beyond their supposed processes to improve their actual processes. To achieve this goal, they see process mining as a key strategy.
In its simplest form, automation can take the form of robotic process automation (RPA), a technology that has seen tremendous growth. Another approach that is now gaining attention is hyperautomation, which Gartner describes as a business-focused and disciplined method to quickly identify, control, and automate as many business and IT processes as possible.
But identifying the business processes to automate is not easy. Factors such as cognitive biases, inaccurate assumptions, and a lack of detailed knowledge of ground operations can cloud decision-making and create barriers to innovation.
What is needed is an accurate understanding of how existing processes are performing and how they are working. Process mining can provide this.
Process mining: an essential precursor to automationProcess mining is a methodology that uses event logs (digital records created by information systems and accumulated over time) to extract valuable insights and actionable insights. By filtering, processing and organizing this data, it is possible to accurately capture every step of the processes involved and detect any deviations from their intended paths.
This allows organizations to accurately visualize business processes and their variations, and monitor them in real time. With automated process discovery and mapping, organizations can significantly optimize their workflows.
"Process mining plays a fundamental role in creating visibility and understanding before automation, and it lays the foundation for business operations resiliency, which helps you change operations in the face of changing commercial terms,” Marc Kerremans, VP analyst at Gartner, told VentureBeat.
He added that process mining is not just a fundamental part of creating visibility and understanding before automation. With its monitoring capabilities, it also visualizes how the different automation islands are connected and how they can be improved.
From automation to hyperautomationHyperautomation is a holistic approach to process automation. It involves the integration of various tools and technologies to improve an organization's ability to automate work. Process mining plays an essential role.
While RPA is the foundation of hyperautomation, its full potential can only be realized by pairing it with complementary solutions such as process mining, AI, analytics, and other advanced tools. Companies achieve greater efficiency when they automate more processes to extract useful insights for everyone involved in an organization's digital transformation efforts.
With effective process mining, analyzed data can be combined with AI/ML to generate data-driven insights that help organizations uncover the current state of their business processes and identify new opportunities optimization and automation. In addition, process mining is an integral part of the multiple stages of the RPA lifecycle.
Initially, it is used to identify processes suitable for automation and analyze how well RPA can be implemented in legacy processes and systems. Later in the process, it monitors and analyzes RPA performance to facilitate continuous improvement.
Process mining has become a valuable driver of successful RPA initiatives. His versatility in tackling the multiple stages of RPA implementation has proven particularly beneficial. Through process mining, companies can identify potential areas of automation within their business processes and prioritize them based on their potential return on investment.
Gartner sees an evolution in which advanced techniques such as root cause analysis, predictive analytics, and even prescriptive analytics are integrating AI for deeper and broader insights into how processes are performing. behave and will behave.
"These advanced techniques also support operational decisions such as which cases to prioritize, which additional resources to involve, and which tasks could be expedited," Kerremans said. "The other...
In search of efficiency and streamlining of operations, companies have turned to AI-based automation. As they pursue this, they need a way to look beyond their supposed processes to improve their actual processes. To achieve this goal, they see process mining as a key strategy.
In its simplest form, automation can take the form of robotic process automation (RPA), a technology that has seen tremendous growth. Another approach that is now gaining attention is hyperautomation, which Gartner describes as a business-focused and disciplined method to quickly identify, control, and automate as many business and IT processes as possible.
But identifying the business processes to automate is not easy. Factors such as cognitive biases, inaccurate assumptions, and a lack of detailed knowledge of ground operations can cloud decision-making and create barriers to innovation.
What is needed is an accurate understanding of how existing processes are performing and how they are working. Process mining can provide this.
Process mining: an essential precursor to automationProcess mining is a methodology that uses event logs (digital records created by information systems and accumulated over time) to extract valuable insights and actionable insights. By filtering, processing and organizing this data, it is possible to accurately capture every step of the processes involved and detect any deviations from their intended paths.
This allows organizations to accurately visualize business processes and their variations, and monitor them in real time. With automated process discovery and mapping, organizations can significantly optimize their workflows.
"Process mining plays a fundamental role in creating visibility and understanding before automation, and it lays the foundation for business operations resiliency, which helps you change operations in the face of changing commercial terms,” Marc Kerremans, VP analyst at Gartner, told VentureBeat.
He added that process mining is not just a fundamental part of creating visibility and understanding before automation. With its monitoring capabilities, it also visualizes how the different automation islands are connected and how they can be improved.
From automation to hyperautomationHyperautomation is a holistic approach to process automation. It involves the integration of various tools and technologies to improve an organization's ability to automate work. Process mining plays an essential role.
While RPA is the foundation of hyperautomation, its full potential can only be realized by pairing it with complementary solutions such as process mining, AI, analytics, and other advanced tools. Companies achieve greater efficiency when they automate more processes to extract useful insights for everyone involved in an organization's digital transformation efforts.
With effective process mining, analyzed data can be combined with AI/ML to generate data-driven insights that help organizations uncover the current state of their business processes and identify new opportunities optimization and automation. In addition, process mining is an integral part of the multiple stages of the RPA lifecycle.
Initially, it is used to identify processes suitable for automation and analyze how well RPA can be implemented in legacy processes and systems. Later in the process, it monitors and analyzes RPA performance to facilitate continuous improvement.
Process mining has become a valuable driver of successful RPA initiatives. His versatility in tackling the multiple stages of RPA implementation has proven particularly beneficial. Through process mining, companies can identify potential areas of automation within their business processes and prioritize them based on their potential return on investment.
Gartner sees an evolution in which advanced techniques such as root cause analysis, predictive analytics, and even prescriptive analytics are integrating AI for deeper and broader insights into how processes are performing. behave and will behave.
"These advanced techniques also support operational decisions such as which cases to prioritize, which additional resources to involve, and which tasks could be expedited," Kerremans said. "The other...
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