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What is process mining?

Process mining is a method of applying specialized algorithms to event log data to identify trends, patterns and details of how a process unfolds. Process mining applies data science to discover, validate and improve workflows

By combining data mining and process analytics, organizations can mine log data from their information systems to understand the performance of their processes, revealing bottlenecks and other areas for improvement. Process mining leverages a data-driven approach to process optimization, allowing managers to remain objective in their decision-making around resource allocation for existing processes.

Information systems, such as enterprise resource planning (ERP) or customer relationship management (CRM) tools, provide an audit trail of processes with their respective log data. Process mining utilizes this data from IT systems to create a process model or process graph of the real process. From here, the end-to-end process is examined, and the details of it and any variations are outlined. 

Specialized algorithms can also provide insight into the root causes of deviations from the norm. These algorithms and visualizations enable management to see if their processes are functioning as intended; if they aren’t, they arm them with the information to justify and allocate the necessary resources to optimize them. They can also uncover opportunities to incorporate robotic process automation (RPA) into processes, expediting any automation initiatives for a company.

Process mining focuses on different perspectives, such as control flow, organizational, case and timestamps. While much of the work around process mining focuses on the sequence of activities—i.e., control—the other perspectives also provide valuable information for management teams. Organizational perspectives can surface the various resources within a process, such as individual job roles or departments, and the time perspective can demonstrate bottlenecks by measuring the processing time of different events within a process.

In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the Process Mining Manifesto (link resides outside ibm.com) in an effort to advance the adoption of process mining to redesign business operations. While proponents of process mining, like the IEEE, promote its adoption, Gartner notes that market factors will also play a role in its acceleration. Digital transformation efforts will prompt more investigation around processes, subsequently increasing the adoption rate of new technologies, such as artificial intelligence, task automation and hyperautomation. The pace of these organizational changes will also require businesses to exercise operational resilience in order to adapt. As a result, enterprises will increasingly lean on process mining tools to achieve their business outcomes.

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Types of process mining

Wil van der Aalst, a Dutch computer scientist and professor, is credited with much of the academic research around process mining. Both his research and the above-mentioned manifesto describe three types of process mining: discovery, conformance and enhancement.

Discovery: Process discovery uses event log data to create a process model without outside influence. Under this classification, no previous process models would exist to inform the development of a new process model. This type of process mining is the most widely adopted.

Conformance: Conformance checking confirms if the intended process model is reflected in practice. This type of process mining compares a process description to an existing process model based on its event log data, identifying any deviations from the intended model.

Enhancement: This type of process mining has also been referred to as extension, organizational mining, or performance mining. In this class of process mining, additional information is used to improve an existing process model. For example, the output of conformance checking can assist in identifying bottlenecks within a process model, allowing managers to optimize an existing process.

Process mining vs. data mining vs. business process management

Process mining sits at the intersection of business process management (BPM) and data mining. While process mining and data mining both work with data, the scope of each dataset differs. Process mining specifically uses event log data to generate process models, which can be used to discover, compare or enhance a given process.

The scope of data mining is much broader, and it extends to a variety of data sets. It is used to observe and predict behaviors, with applications in customer churn, fraud detection, market basket analysis and more.

Process mining takes a more data-driven approach to BPM, which has historically been managed manually. BPM generally collects data more informally through workshops and interviews and then uses software to document that workflow as a process map. Since the data that informs these process maps is generally qualitative, process mining brings a more quantitative approach to a process problem, detailing the actual process through event data.

Why is process mining important?

Increasing sales isn’t the only way to generate revenue. Six Sigma and lean methodologies also demonstrate how the reduction of operational costs can increase your return on investment (ROI).

Process mining solutions help businesses reduce these costs by quantifying the inefficiencies in their operational models, allowing leaders to make objective decisions about resource allocation. The discovery of these bottlenecks can not only reduce costs and expedite process improvement, but also drive more innovation, quality and better customer retention.

Since process mining is still a relatively new discipline, it still has some hurdles to overcome. Some of those challenges include:

  • Data quality: Finding, merging, and cleaning data is usually required to enable process mining. Data might be distributed over various data sources. It can also be incomplete or contain different labels or levels of granularity. Accounting for these differences will be important to the information that a process model yields.

  • Concept drift: Sometimes processes change as they are being analyzed, resulting in concept drift.

Utilizing advanced process mining solutions is key to unlocking efficiencies and driving organizational transformation.

Advantages and limitations of process mining
Advantages of process mining
  1. Enhanced transparency: Process mining offers a data-driven view of operational processes, surpassing traditional business process mapping. This deep visibility is crucial for identifying inefficiencies and compliance issues and understanding the actual process flow.

  2. Simplified process analysis and enhanced efficiency: Process mining utilizes event-log data to quickly analyze business processes, enabling the visualization of multiple variants and streamlining operations to reduce cycle times and costs. This approach simplifies management and facilitates the automation of routine tasks.

  3. Data-driven decision making: Process mining facilitates objective decisions using IT systems data. This approach is key in precisely identifying and resolving issues such as bottlenecks and deviations.

  4. Process optimization: By continuously monitoring process performance metrics, such as KPIs and SLAs, process mining identifies opportunities for optimization and automation across various operations.

  5. Customer-centric process view: It offers detailed insights into customer journeys by aligning external customer interactions with internal operations, highlighting areas for improvement in customer experience.

  6. Process standardization: It supports standardizing processes across an organization by identifying variations and aligning them with the optimal process model. This helps ensure consistent performance and quality.

  7. Better customer experience: Streamlining processes and enhancing efficiency leads to improved service delivery, fostering greater customer satisfaction and loyalty.

Limitations of process mining
  1. Data quality and availability: Effective process mining relies on high-quality, complete data. Inaccuracies can distort process models and lead to incorrect insights. Engaging data analysts in the initial stages can ensure the integrity and completeness of data used for process mining.

  2. Inability to capture tasks: Process mining may miss manual tasks outside IT systems that are not recorded in event logs, limiting its scope in workflow optimizations. By integrating task mining with process mining organizastions can address this gap and enhance the analysis of workflows and task-level optimizations.

  3. Integration hurdles: Some IT systems pose integration challenges with process mining due to the lack of connectors or data format issues. Pre-packaged solutions designed for specific systems or processes can simplify integration, making the process more seamless.

  4. Concept drift: As processes evolve, it can be difficult to keep process mining models updated. With outdated models there is a greater risk of outdated analyses. Advanced process mining solutions analyze processes in near real-time, which helps keep models current and relevant.

  5. Complexity in large organizations: In larger organizations, the volume and complexity of processes can amplify the challenges of process mining, affecting insight extraction. By adopting object-centric or multi-level process mining techniques, organizations can better manage and analyze complex processes.

  6. Potential resistance to change: Significant changes in process management due to process mining can meet resistance from employees accustomed to existing workflows. Effective change management is critical for successful implementation and adoption. Implementing effective change management strategies, including staff training and engagement, can facilitate smoother transition and adoption.

Process mining use cases

Process mining techniques are used to improve process flows across various industries. Since process maps highlight the key performance indicators (KPIs) that impact performance, they spur businesses to reexamine their operational inefficiencies. The value and versatility of process mining solutions are illustrated by the following use cases:

  • Education: Process mining can help identify effective course curriculums by monitoring and evaluating student performance and behaviors, such as how much time a student spends viewing class materials.

  • Finance: Financial services, institutions, and procurement operations use process mining software to improve inter-organizational processes and account auditing, increase income and expand their customer base.

  • Public works: Process mining is used to streamline the invoice process for public works projects that involve various stakeholders, such as construction companies, cleaning businesses and environmental bureaus.

  • Software development: Engineering processes can be disorganized, and process mining can help identify a clear, documented process. It can also help IT administrators monitor the process, allowing them to verify that the system is running as expected.

  • Healthcare: Process mining provides recommendations for reducing patients' treatment processing time.

  • E-commerce: Process mining can provide insight into buyer behaviors and accurate recommendations to increase sales.

  • Manufacturing: Process mining enhances supply chain and manufacturing business operations by assigning appropriate resources based on product attributes. Insights into production times and resource allocation, such as storage space, machines or workers, allow for more efficient management and operational transformation.

  • IT service management (ITSM): Process mining can optimize service delivery and incident management processes. It enables IT teams to analyze service workflows, identify inefficiencies and improve response times. This helps enhance overall IT support and customer satisfaction.

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