Mastering Process Mining: A Comprehensive Exploration

Process mining is a data-driven approach to understanding and optimizing business processes.

It involves analyzing event logs and other data generated by information systems to gain insights into how processes are executed, identifying bottlenecks, and areas for improvement.

By applying process mining techniques, organizations can uncover hidden opportunities to streamline their operations, reduce costs, and improve customer satisfaction.

Process mining tools and applications are used across a wide range of industries, including healthcare, finance, manufacturing, and logistics. The techniques are particularly useful in complex, data-intensive environments where traditional process improvement methods may fall short. Process mining can be used to analyze both structured and unstructured data, enabling organizations to gain a complete picture of how their processes are executed.

What is Process Mining?

Let’s start with the basics

Definition of Process Mining

Process mining is a data-driven technique that helps you analyze and optimize your business processes.

It uses event logs from your information system to create a process model that visualizes the steps in your process.

This model can be used to discover, compare, and enhance your processes.

How Process Mining Works

Process mining works by analyzing your event logs to create a process graph that shows the steps in your process.

This graph can be used to identify bottlenecks, inefficiencies, and other areas for improvement.

Process mining algorithms can also help you discover new process steps and variations that you may not have been aware of.

Image source: UiPath

Three Key Components of Process Mining

The three key components of process mining are discovery, conformance, and enhancement.

1. Discovery: This component involves analyzing your event logs to create a process model that shows the steps in your process. This can help you understand how your process works and identify areas for improvement.
2. Conformance: This component involves comparing your actual process to your process model to identify any deviations. This can help you identify areas where your process is not being followed correctly and take corrective action.
3. Enhancement: This component involves using your process model to optimize your process. This can involve identifying bottlenecks, inefficiencies, and other areas for improvement and making changes to your process to improve efficiency and effectiveness.

How to Use Process Mining?

Process mining helps you capture information from enterprise transaction systems and provides detailed, data-driven insights about how key processes are working.

Steps in Process Mining Process

Here are the steps you should follow to use process mining effectively:

1. Data Extraction

The first step in process mining is to extract data from the information systems. This data should contain all the relevant information about the processes you want to analyze.

This data can be in the form of event logs, transactional data, or other sources.

2. Data Preprocessing

Once you have the data, you need to preprocess it to ensure that it is clean and ready for analysis.

This step involves filtering out irrelevant data, removing errors, and ensuring that the data is in the correct format.

3. Process Discovery

The next step is to use process discovery algorithms to generate a process model from the event logs.

This process model should accurately represent the actual processes that are happening in your organization.

4. Conformance Checking

After you have generated a process model, you need to use conformance checking algorithms to compare the actual process with the process model.

This step helps you identify any deviations from the expected process.

5. Process Enhancement

Once you have identified any deviations from the expected process, you need to use process enhancement algorithms to optimize the process.

This step involves identifying bottlenecks, inefficiencies, and other areas for improvement and developing strategies to address them.

As-Is vs To-Be Process Mining

Process mining can be divided into two categories: As-Is and To-Be process mining.

• As-Is process mining involves analyzing the current processes in your organization to identify areas for improvement
• To-Be process mining involves designing new processes that are more efficient and effective than the current processes

In conclusion, process mining is an essential tool for modern business process management. By following the steps outlined above, you can use process mining to identify areas for improvement in your organization and develop strategies to address them. Whether you are using As-Is or To-Be process mining, process mining can help you optimize your processes and improve efficiency.

Why Is Process Mining Important?

Process mining is important because it provides insights into your organization’s operational processes and helps you optimize them. By analyzing log data from your IT systems, process mining tools can help you identify deviations and inefficiencies in your business processes, which can lead to improved performance and business outcomes.

Benefits of Process Mining

Here are some of the benefits of process mining:

• Improved efficiency: Process mining can help you identify bottlenecks and inefficiencies in your workflows, enabling you to take actions to improve efficiency. By optimizing your processes, you can reduce costs and increase output.
• Insight into actual data: Process mining provides insight into your actual business processes, rather than just the models you have created. This means you can see how your processes are actually performing and identify areas for improvement.
• Transparency: Process mining provides transparency into your business processes, enabling you to see how activities are performed and how long they take. This can help you identify areas where transparency is lacking and take steps to improve it.
• Performance analysis: Process mining can help you analyze the performance of your business processes, enabling you to identify key performance indicators (KPIs) and track them over time. This can help you measure the impact of process improvements and identify areas for further optimization.
• Process optimization: By analyzing your business processes, process mining can help you identify areas for optimization. This can include streamlining workflows, reducing bottlenecks, and improving the overall efficiency of your processes.
• Digital transformation: Process mining is an important tool for digital transformation, enabling you to identify areas where technology can be used to improve your business processes. This can include automation, machine learning, and other technologies that can help you streamline your workflows and improve efficiency.

Process Mining Techniques

In this section, we will discuss the different process mining techniques that are commonly used.

Process Discovery

Process discovery is the first step in process mining.

It involves analyzing event logs to create a process model that represents the end-to-end process. This model can be used to identify variations in the process, understand the flow of activities, and identify bottlenecks and inefficiencies.

Conformance Checking

Conformance checking is the process of comparing the actual process execution with the expected process model.

This helps organizations identify deviations from the expected process and take corrective actions to improve the process.

Conformance checking can be used to identify bottlenecks, inefficiencies, and compliance issues.

Image source: Fluxicon

Process Enhancement

Process enhancement is the final step in process mining. It involves using the insights gained from process discovery and conformance checking to optimize the process.

This can involve automating certain tasks, reengineering the process, or introducing new technologies such as robotic process automation, artificial intelligence, or machine learning.

Process Mining Techniques and Algorithms

There are various techniques and algorithms used in process mining that help to discover, monitor, and improve real processes.

Heuristics Miner

Heuristics Miner is a process discovery algorithm that uses heuristics to discover a process model from an event log.

This algorithm is based on the idea that a process model can be represented as a Petri net, and the behavior of the process can be discovered by analyzing the event log.

Heuristics Miner uses various heuristics to identify the most frequent and relevant behavior in the event log, and then constructs a process model accordingly.

Heuristics Miner is particularly useful when the event log is noisy or incomplete. It can handle missing data and can also deal with concurrent activities.

The algorithm is fast and can handle large event logs. However, the resulting model may not always be the most accurate representation of the process.

Control-Flow Algorithms

Control-Flow Algorithms are a family of process discovery algorithms that use the control-flow perspective to construct process models. These algorithms focus on the order in which activities are executed in a process and use this information to construct a process model.

There are several types of Control-Flow algorithms, including Alpha, Heuristic, and Genetic.

• Alpha algorithm constructs a process model by identifying the most common sequence of activities in the event log.
• Heuristic algorithm constructs a process model by identifying the most frequent and relevant behavior in the event log.
• Genetic algorithm constructs a process model by evolving a population of process models based on a fitness function.

When Should I Use Control-Flow Alorithms in Process Mining?

Control-Flow algorithms are useful when the event log contains complete and accurate data. They can handle large event logs and can produce accurate process models. However, they may not be suitable for noisy or incomplete event logs.

Process Mining Tools and Applications

In this section, we will take a look at some of the popular process mining tools and their applications.

Celonis

Celonis is a process mining tool that uses data science and AI to provide insights into business processes. It offers a range of features such as process discovery, visualization, and optimization. Celonis can be used to identify bottlenecks, inefficiencies, and compliance issues in business processes. It is also useful for identifying process variations and their impact on performance. Celonis has been recognized as a leader in the Gartner Magic Quadrant for Process Mining.

Image source: Celonis

UiPath

UiPath is a Robotic Process Automation (RPA) tool that also offers process mining capabilities.

It uses AI and machine learning to analyze process data and provide insights into process performance. UiPath can be used to identify process bottlenecks, inefficiencies, and compliance issues. It can also be used to optimize processes and improve their performance. UiPath is a popular tool for process automation and has been recognized as a leader in the Gartner Magic Quadrant for RPA.

Image source: UiPath

IBM Process Mining

IBM Process Mining is a process mining tool that uses process analytics to provide insights into business processes. It uses AI and machine learning to analyze process data and provide visualizations of process performance. IBM Process Mining can be used to identify bottlenecks, inefficiencies, and compliance issues in business processes. It can also be used to optimize processes and improve their performance. IBM Process Mining is a popular tool for process optimization and has been recognized as a leader in the Gartner Magic Quadrant for Process Mining.

Image source: IBM Process Mining

Minit Process Mining

Minit Process Mining is a process mining tool that provides insights into business processes. It uses process analytics to identify bottlenecks, inefficiencies, and compliance issues in business processes. Minit Process Mining can be used to optimize processes and improve their performance. It offers a range of features such as process discovery, visualization, and analysis. Minit Process Mining is a popular tool for process optimization and has been recognized as a leader in the Gartner Magic Quadrant for Process Mining.

Image source: Minit

Nintex Promapp Process Mining

Nintex Promapp Process Mining is a process mining tool that uses process analytics to provide insights into business processes.

It offers a range of features such as process discovery, visualization, and analysis. Nintex Promapp Process Mining can be used to identify bottlenecks, inefficiencies, and compliance issues in business processes.

It can also be used to optimize processes and improve their performance. Nintex Promapp Process Mining is a popular tool for process optimization and has been recognized as a leader in the Gartner Magic Quadrant for Process Mining.

Other Process Mining Tools

There are several other process mining tools available in the market. Some of the popular ones include Disco, ProM, and QPR ProcessAnalyzer.

These tools offer a range of features such as process discovery, visualization, and analysis. They can be used to identify bottlenecks, inefficiencies, and compliance issues in business processes. They can also be used to optimize processes and improve their performance.

Examples: Business Use Cases For Process Mining

What are some common use cases for process mining?

Process mining techniques can be applied to various industries and departments to optimize business processes. Some common use cases for process mining include:

• Identifying bottlenecks in processes
• Improving process efficiency
• Reducing operational costs
• Enhancing customer experience
• Increasing compliance and reducing risks

Finance Process Mining

Process mining can help finance departments to streamline their operations, identify inefficiencies, and reduce costs. For example, process mining can be used to optimize the accounts payable process by identifying bottlenecks and reducing the time it takes to process invoices. It can also be used to detect fraudulent activities and ensure compliance with regulations.

Healthcare Process Mining

Process mining can be applied to healthcare to improve patient care and optimize operational processes. For instance, it can be used to analyze patient flow and identify bottlenecks in the process. It can also be used to monitor the performance of medical staff and ensure compliance with regulations.

Manufacturing Process Mining

Process mining can be used in manufacturing to optimize production processes, reduce costs, and improve product quality. For example, it can be used to identify bottlenecks in the production line and optimize the flow of materials. It can also be used to monitor equipment performance and detect defects in the production process.

Sales Process Mining

Process mining can help sales departments to optimize their processes and improve customer experience. For instance, it can be used to analyze the sales pipeline and identify opportunities for improvement. It can also be used to monitor the performance of sales representatives and ensure compliance with sales regulations.

HR Operations Process Mining

Process mining can be applied to HR operations to optimize employee onboarding, performance management, and other HR processes. For example, it can be used to analyze the recruitment process and identify bottlenecks. It can also be used to monitor employee performance and ensure compliance with HR regulations.

Process Mining Compared to other data techniques

When it comes to analyzing business processes, there are several techniques available, each with its own strengths and weaknesses. In this section, we will compare process mining with other data techniques to help you understand the differences.

BPM vs Process Mining

Business Process Management (BPM) and process mining are two techniques that are often compared. While BPM focuses on modeling and improving processes, process mining is more concerned with analyzing existing processes and identifying areas for improvement. BPM is a more structured approach, while process mining is more data-driven.

	BPM	Process Mining
Definition	The management approach to optimizing business processes	The analysis of event logs to improve business processes
Focus	Process optimization	Process analysis and optimization
Data Sources	Business process models and documentation	Event logs from IT systems
Purpose	Designing and optimizing business processes	Improving process efficiency and effectiveness
Techniques	Process modeling, simulation, optimization	Discovery, conformance, enhancement
Output	Process models, process improvement plans	Visualizations and process models for optimization
Limitations	Limited to available process models and documentation	Requires event logs, limited to process analysis

Data Mining vs Process Mining

Data mining and process mining are two different techniques that are often confused with each other.

Data mining is the process of extracting insights from large datasets, whereas process mining focuses on analyzing event logs to gain insights into operational processes.

While data mining is used to identify patterns and trends in data, process mining is used to identify patterns and trends in processes. Thereforece, data mining is often used for predictive analysis, while process mining is used for process optimization.

	Data Mining	Process Mining
Definition	The process of extracting patterns and insights from large datasets	The process of analyzing event logs to improve business processes
Focus	Historical data analysis	Real-time process analysis
Data Sources	Structured and unstructured data	Event logs from IT systems
Purpose	Predictive modeling and decision-making	Process optimization and improvement
Techniques	Clustering, classification, regression, association rule mining	Discovery, conformance, enhancement
Output	Insights and patterns for decision-making	Visualizations and process models for optimization
Limitations	Limited to available data, potential for biased results	Requires event logs, limited to process analysis

RPA vs Process Mining

Robotic Process Automation (RPA) and process mining are two techniques that can be used together to automate and optimize processes. RPA is focused on automating repetitive tasks, while process mining is focused on analyzing and optimizing processes. RPA can be used to implement the changes identified by process mining.

	RPA	Process Mining
Definition	The use of software robots to automate repetitive tasks	The analysis of event logs to improve business processes
Focus	Task automation	Process optimization
Data Sources	User interfaces and APIs	Event logs from IT systems
Purpose	Automating repetitive tasks	Improving process efficiency and effectiveness
Techniques	Recording and replaying user actions	Discovery, conformance, enhancement
Output	Automated tasks and processes	Visualizations and process models for optimization
Limitations	Limited to repetitive tasks, potential for errors	Requires event logs, limited to process analysis

Task mining vs Process mining

Task mining and process mining are two techniques that are often used together. Task mining focuses on analyzing user interactions with software applications, while process mining focuses on analyzing business processes. Task mining can provide more detailed information about how users interact with software, while process mining can provide a broader view of how processes work.

	Task Mining	Process Mining
Definition	The process of analyzing user behavior to identify and improve task performance	The process of analyzing event logs to improve business processes
Focus	Task performance optimization	Process optimization
Data Sources	User interfaces and APIs	Event logs from IT systems
Purpose	Improving task efficiency and effectiveness	Improving process efficiency and effectiveness
Techniques	Recording and analyzing user actions	Discovery, conformance, enhancement
Output	Insights and patterns for task optimization	Visualizations and process models for process optimization
Limitations	Limited to available data, potential for biased results	Requires event logs, limited to process analysis

Business Intelligence vs Process Mining

Business Intelligence (BI) and process mining are two techniques that can be used together to gain insights into business processes.

BI is focused on reporting and analysis, while process mining is focused on understanding how processes work. BI can be used to provide a high-level view of business performance, while process mining can provide more detailed insights into specific processes.

In other words, business intelligence uncovers what went wrong, and Process Mining can reveal how / why it went wrong.

I think this summarize it well

Let’s summarize the comparisson

	Business Intelligence	Process Mining
Definition	The use of data analysis tools to gain insights into business performance	The analysis of event logs to improve business processes
Focus	Historical data analysis	Real-time process analysis
Data Sources	Structured and unstructured data	Event logs from IT systems
Purpose	Reporting and decision-making	Process optimization and improvement
Techniques	Data visualization, reporting, OLAP, data mining	Discovery, conformance, enhancement
Output	Reports, dashboards, KPIs	Visualizations and process models for optimization
Limitations	Limited to available data, potential for biased results	Requires event logs, limited to process analysis

Challenges and Solutions in Process Mining

Process mining is a powerful tool for analyzing business processes and identifying areas for improvement.

However, like any tool, it comes with its own set of challenges. Here are some common challenges with process mining, along with examples and potential solutions:

Data Quality

One of the biggest challenges with process mining is ensuring the quality of the data being analyzed. If the data is incomplete, inaccurate, or inconsistent, the results of the analysis may be unreliable.

Example: In a supply chain management process, if the data on inventory levels is incomplete or inaccurate, it may lead to incorrect insights on the process.

Solution: To address this challenge, it’s important to establish data quality standards and ensure that all data sources are consistent and accurate. This can be done by implementing data validation rules, conducting regular data audits, and using data cleansing tools.

Data Privacy

Another challenge with process mining is ensuring the privacy and security of sensitive data. If confidential data is exposed during the analysis, it can lead to legal and ethical issues.

Example: In a healthcare process, if patient data is exposed during the analysis, it can lead to legal and ethical issues.

Solution: To address this challenge, it’s important to establish data privacy policies and ensure that the data being analyzed is anonymized and encrypted. This can be done by using secure data transfer protocols, implementing access controls, and using data masking techniques.

Process Complexity

Process mining can also be challenging when dealing with complex processes that involve multiple systems and stakeholders.

If the process is too complex, it can be difficult to identify the root causes of problems and implement effective solutions.

Example: In a customer service process that involves multiple systems and stakeholders, it can be difficult to identify the root causes of customer complaints and implement effective solutions.

Solution: To address this challenge, it’s important to break down the process into smaller, more manageable components and use process modeling tools to visualize the process. This can help identify areas for improvement and implement effective solutions.

Process Change

Finally, process mining can be challenging when dealing with processes that are constantly changing. If the process changes frequently, it can be difficult to maintain accurate process models and keep up with the analysis.

Example: In an IT support process that is constantly changing due to new technologies and software updates, it can be difficult to maintain accurate process models and keep up with the analysis.

Solution: To address this challenge, it’s important to establish a process for regularly updating process models and analyzing the impact of process changes.

This can be done by using version control systems for process models, conducting regular process audits, and using change management tools to track process changes.

Learn Process Mining

Process mining is a complex field that requires a deep understanding of data science and process analytics.

However, there are many resources available to help you get started with process mining. In this section, we will highlight some of the best online resources to learn process mining.

Online Courses to Learn Process Mining

1. Coursera – Process Mining: Data Science in Action

This course, offered by Eindhoven University of Technology, covers the three main types of process mining: discovery, conformance, and enhancement. It provides an in-depth understanding of process mining techniques and their applications.

The first type of process mining is discovery. You’ll be given an example of an algorithm taking an event log to produce a process model  explaining the behavior recorded in the log. The course then shows you other ways of discovering a process model from event data.

The second type of process mining is conformance. You’ll learn how to perform conformance checking to determine if reality, as recorded in the log, conforms to the model and vice versa.

Lastly, you’ll find how process mining can be applied on running processes, also called enhancement. The idea is to extend or improve an existing process model using information about the actual process recorded in some event log.

How You Will Learn

The course spans 6 weeks, with each week taking 3 to 6 hours to complete. Every module consists of a series of short lecture videos of 8–15 minutes each, with handouts for each video lecture available to be downloaded.

Regarding assessments, there will be weekly quizzes to test your understanding of the lecture videos, and a final exam. There’s also an optional peer assignment in which you apply the tools and techniques on real data and make a short report.

Review: Coursera – Process Mining: Data Science in Action

This is a comprehensive course that covers all the essential topics in process mining. It is suitable for beginners and provides a good foundation for further learning.

I personally liked the quizzes a lot and can recommend the course to anyone interested in a solid understanding of process mining

2. Udemy – Process Mining with Celonis

This course, offered by Udemy, covers the basics of process mining, including process discovery, conformance checking, and process enhancement.

You are introduced to the discipline of process mining using a healthy blend of theoretical knowledge and hands-on practice.

The theoretical parts of this course are drawn from leading academic publications. The hands-on practice parts will be conducted in the leading process mining software application Celonis

Review: Udemy – Process Mining with Celonis

This is a good course for beginners who want to learn the basics of process mining. It provides practical experience with process mining tools and techniques.

Tips: Watch the lectures and complete the exercises to gain a good understanding of the topics covered. Use the process mining tools provided to gain practical experience.

3. FutureLearn – Process Mining in Healthcare Description

This course, offered by Eindhoven University of Technology, focuses on the application of process mining in healthcare. It covers topics such as patient pathways, clinical guidelines, and quality improvement.

Review: This is an excellent course for anyone interested in process mining in healthcare. It provides a good overview of the techniques and their applications in this field.

4. Open HPI – A Step-by-Step Introduction to Process Mining

5. Data camp – Business Process Analytics in R

Online Resources from Providers to Learn Process Mining

1. Celonis Academy

2. Minit Academy

3. UiPath Academy

Future of Process Mining

As organizations continue to adopt process mining to optimize their operations, the future of this technology looks promising. Here are some trends that are expected to shape the future of process mining:

Role of AI and Machine Learning in Process Mining

Artificial intelligence (AI) and machine learning (ML) are expected to play a big role in the future of process mining.

Process mining algorithms can be enhanced with AI and ML to improve the accuracy of predictions and recommendations.

For example, AI-powered process mining can help identify patterns and anomalies that may be missed by traditional process mining tools. Additionally, machine learning can help automate the process of identifying and prioritizing process improvement opportunities.

Digital Transformation and Process Mining

Digital transformation is another trend that is expected to impact the future of process mining.

As organizations continue to digitize their operations, process mining will become a critical tool for identifying inefficiencies and bottlenecks in digital processes.

Process mining can help organizations optimize their digital processes by providing insights into how they are performing and where improvements can be made.

In addition, process mining can help organizations monitor the impact of digital transformation initiatives on their operations.

By analyzing the performance of digital processes before and after a transformation, organizations can determine whether the transformation has been successful and identify areas where further improvements can be made.

Overall, the future of process mining looks bright. As organizations continue to adopt this technology, we can expect to see more advanced process mining tools that incorporate AI and ML, as well as increased integration with digital transformation initiatives.

By leveraging the power of process mining, organizations can optimize their operations and stay ahead of the competition.

Conclusion: Process Mining

In summary, process mining is a data analytics technique that allows you to discover, monitor, and improve your business processes. By analyzing data from various sources, such as process logs, you can gain insights into how your processes are being executed and identify bottlenecks and inefficiencies. This can help you optimize your processes, reduce costs, and improve your overall business performance.

Process mining can provide many benefits to your organization, including:

• Increased efficiency: By identifying bottlenecks and inefficiencies in your processes, you can optimize them to improve efficiency and reduce costs.
• Improved compliance: Process mining can help you ensure that your processes are compliant with regulations and standards.
• Better decision-making: By providing insights into how your processes are being executed, process mining can help you make better decisions about how to improve them.
• Enhanced customer experience: By optimizing your processes, you can provide a better customer experience, which can lead to increased customer satisfaction and loyalty.

It’s important to note that process mining is not a one-time activity. You should regularly monitor your processes and analyze data to identify areas for improvement. By doing so, you can continue to optimize your processes and improve your business performance over time.

Overall, process mining is a powerful tool that can help you gain insights into your business processes and optimize them for better performance. If you’re looking to improve your business efficiency, compliance, decision-making, or customer experience, process mining is definitely worth considering.

Frequently Asked Questions

What are some common use cases for process mining?

Process mining is used in a variety of industries to identify inefficiencies, bottlenecks, and areas for improvement. Some common use cases include analyzing supply chain processes, optimizing customer service workflows, and improving healthcare operations.

What types of techniques are used in process mining?

Process mining uses a combination of data mining, machine learning, and process management techniques to analyze data from event logs and create process models. Some common techniques include process discovery, conformance checking, and process enhancement.

Can you provide an example of process mining in action?

One example of process mining in action is using event log data from a call center to identify areas for improvement in the customer service process. By analyzing the data, process mining can identify common issues such as long wait times or repetitive tasks, and suggest changes to streamline the process and improve customer satisfaction.

How does process mining differ from traditional data mining?

While traditional data mining focuses on finding patterns and insights in large datasets, process mining specifically analyzes event logs to create process models and identify inefficiencies in workflows. Process mining is also more focused on continuous improvement and optimization of processes, rather than just finding insights.

What benefits does process mining offer?

Process mining offers several benefits, including improved efficiency and productivity, better customer service, reduced costs, and increased transparency and compliance. By identifying inefficiencies and bottlenecks, process mining can help organizations optimize their processes and improve overall performance.

What tools are commonly used for process mining?

There are several software tools available for process mining, including Pega, Microsoft Power Automate, IBM, and QPR. These tools use a combination of data analytics, machine learning, and process management techniques to analyze event log data and create process models.

Process mining is a collection of methods that combine data science with business process management (BPM) to assist in the study of operational processes using event logs. What this means is that process mining aims to convert event data into insights and actions. 

Process mining applies algorithms to business processes, cases and events logged by enterprise applications (for example, Salesforce, SAP, etc.) to understand actual work done by employees, uncover hidden bottlenecks and identify where automation will lead to the most significant process improvements.

In general, you can think of process mining as a way to try to answer the questions:

• How are our processes actually working
• Why are they not working as they should
• How can we make them better

Let’s see some of the benefits with process mining

One of the key benefits of process mining is the ability to show, based on facts (data), how the processes are actually performed. The actual process is almost always more complex and has several different variants than what people/companies estimate.

In other words, people typically underestimate how complicated their processes really are.

The image below illustrates how people assume that processes are performed versus how they are actually performed. With the help of process mining, companies can uncover the right-hand side, actual process reality.

By revealing performance issues, bottlenecks, and tasks that can benefit from automation, organizations can significantly reduce their operating costs.

Process mining helps companies identify root causes and gain valuable insights into where they should focus their efforts to get the highest impact

Data-driven decision making is the method of using data to make informed and verified decisions. Process mining is a tool that helps people to overcome biases and make the best decisions that are aligned with business strategies.

Therefore, by using data-driven decision making, companies get a completely objective view of the process flow and see the process as it is, not as how someone perceives it.

Business process mining enables a data-driven understanding of how the different processes throughout the organization work and gets everyone on the same level of understanding that can lead to standardizing processes and streamline business outcomes.

According to Celonis, these are the basic steps involved in process mining

Today, business processes often run across several different systems, with different types of data, with varying types of users, and may belong to various departments. As you notice, it gets very complex.

Luckily, most process mining tools provide a few different methods for obtaining this data from the underlying systems. It could, for example, in the more straightforward case, be just to upload the event log from a system (most likely in .csv format).

Similarly, for real-time data ingestion, some process mining platforms, for example Celonis, offers real-time data ingestion to sync process data continuously

Now that we have got all the necessary data in the process mining software, we can start to discover how the process looks and all its different variants. You could say that this is where the actual process mining starts, where we get to dig into the process and find out how we are actually doing

Time to analyze the process! The process mining platforms help us find various bottlenecks, what creates deviations, number of process variants and the cause for variants. In other words, help us understand the root causes of process inefficiencies and quantify their impact on the KPI:s

Process benchmarking includes comparing and analyzing the business processes with the processes considered to be best practices. The comparison could be between different countries, or perhaps the throughput time of a purchase order from one supplier versus another.

The purpose is then to share these best practices between regions, units and teams.

Companies can use process mining to validate if their actual processes conform and comply with the given specifications and identify non-conforming and non-compliant cases and reasons for the deviations.

This is when the gap between the process “as defined” and the process “as is” becomes apparent. For example, know when steps are being ignored, performed in the wrong order, or taking longer than anticipated at a particular stage of the process

The purpose of both Business Intelligence (BI) and Process Mining is to assist companies, more specifically business managers, in making better, more informed, and evidence-based decisions based on data.

However, the main difference between business intelligence and process mining could be described, although quite generally speaking and the line between them is not as distinct, is the focus on root cause analysis. Business intelligence can tell you something went wrong, and process mining can tell you why it went wrong.

In other words, business intelligence uncovers what went wrong, and Process Mining can reveal how / why it went wrong.

Image source: Datarundown

An example I like to use, and again quite generally speaking, is to imagine a company with several production plants that use business intelligence to monitor numerous KPI and visualize their ongoing operations. 

With the help of business intelligence, the company revealed that production plant A1 is their best performing plant and that production plant B2 and E3 are the worst-performing ones. Then with the help of process mining, the company can dig deeper and try to find out why production plant A1 is the best and B2, and E3 are under-performing. 

According to Gartner and g2 these are the five most used process mining softwares and platforms

To see the complete Best Process mining software list and read all of the user reviews, follow the link to G2

If you are curios to learn more about process mining and related topics, then check out all of our posts related to process mining