Business processes serve as the foundation of any organization, dictating how tasks are completed and value is delivered to customers. However, many businesses grapple with comprehending, streamlining, and automating their processes, given the intricacy, variability, and dynamism of their operations. Consequently, this can result in subpar performance, inefficiency, non-compliance, and customer discontent.

Fortunately, a solution exists to help businesses surmount these challenges and achieve process excellence: the amalgamation of process mining and process automation. In this blog post, we will elucidate the concepts of process mining and process automation, explore their interrelation and complementarity, and demonstrate how they can be leveraged to revolutionize business processes and outcomes.

What is process mining?

Process mining is a technique that uses data science to discover, validate and improve business processes based on event logs from information systems. Event logs are records of the activities that occur in a process, such as starting a task, completing a task, sending an email, making a decision, etc. Event logs contain rich information about the process execution, such as timestamps, actors, resources, data attributes, etc.

Process mining can provide valuable insights into the performance, efficiency and compliance of operational processes, as well as identify opportunities for process automation. Process mining can answer questions such as:

• What are the actual processes that are executed in an organization?
• How do the actual processes differ from the desired or prescribed processes?
• How long does it take to complete a process or a task?
• How often do errors or exceptions occur in a process?
• How much variation or deviation exists in a process?
• Which activities or paths are most frequent or critical in a process?
• Which actors or resources are involved in a process?
• Which activities or tasks are suitable for automation?

Process mining can also provide recommendations and suggestions for improving or redesigning processes, based on data-driven techniques such as optimization, simplification or re-engineering. Process mining can help eliminate waste, redundancy and inefficiency, as well as enhance flexibility, scalability and robustness.

What is process automation?

Process automation is the use of software and technologies to automate business processes and functions in order to achieve organizational goals, such as increasing productivity, quality, customer satisfaction and profitability. Process automation can also enhance the agility and resilience of businesses in a dynamic and competitive market.

Process automation can be implemented at different levels of complexity and intelligence, depending on the nature and requirements of the process. Some examples of process automation technologies are:

• Robotic process automation (RPA): RPA uses software robots or bots to mimic human actions and interactions with applications or systems. RPA can automate repetitive, rule-based and low-value tasks that do not require human judgment or creativity.
• Business process management (BPM): BPM uses software platforms or tools to model, execute, monitor and optimize business processes. BPM can automate complex, structured and high-value processes that require coordination and collaboration among multiple actors or resources.
• Artificial intelligence (AI): AI uses software algorithms or models to perform tasks that normally require human intelligence or cognition. AI can automate cognitive, unstructured and innovative tasks that require reasoning, learning or creativity.

How are process mining and process automation related and complementary?

Process mining and process automation are closely related and complementary. Process mining can be utilized for process automation in different ways, such as:

• Process discovery: Process mining can help discover the actual processes that are executed in an organization, by extracting process models from event logs. These models can reveal the sequence, frequency, duration and variation of activities, as well as the roles and resources involved in each process. Process discovery can help identify which processes are suitable for automation, based on criteria such as frequency, complexity, stability and standardization.
• Conformance checking: Process mining can help compare the actual processes with the desired or prescribed processes, by measuring the degree of alignment and deviation between them. Conformance checking can help evaluate the effectiveness and compliance of existing process automation solutions, as well as detect and diagnose any problems or errors that may occur during process execution. Conformance checking can also help monitor and audit the performance and quality of automated processes, by using key performance indicators (KPIs) and metrics.
• Process enhancement: Process mining can help improve the existing processes or design new processes, by using data-driven techniques to optimize, simplify or re-engineer them. Process enhancement can help increase the benefits and reduce the costs of process automation, by eliminating waste, redundancy and inefficiency, as well as enhancing flexibility, scalability and robustness. Process enhancement can also help incorporate new technologies or innovations into process automation solutions, such as artificial intelligence, machine learning, robotic process automation or hyperautomation.
• Validate automation effectiveness: One way to do this is to conduct a follow-up process mining exercise after implementing process automation solutions, and compare the results with the baseline process mining exercise before automation. This can show the changes in process performance, efficiency and compliance, as well as the potential savings or benefits achieved by automation.
• Data integration and data pipelines: Data integration and data pipelines use software tools or platforms to collect, transform, and deliver data from various sources to different destinations. Data integration and data pipelines can automate the data ingestion, preparation, and delivery processes that ensure data consistency, quality, and availability for business processes. Data integration and data pipelines can also eliminate the need for manual data input or manipulation, reducing errors and saving time. Data quality is key to streamline your business processes and also serves as the ideal basis for future business intelligence solutions.

Process mining and process automation are powerful tools for digital transformation that can help businesses gain a competitive edge in today’s fast-changing world. By combining process mining and process automation, businesses can achieve a higher level of process excellence and customer value.