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What is Process Mining in 2024?

Updated on Jan 4
11 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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With the increasing complexity of operations and the advances in technology, process mining becomes a critical solution. Process mining can help gain process intelligence and identify bottlenecks for process improvement. 

Process mining trends estimate that the process mining market will grow by 40-50% in 2022. However, business leaders and analysts should understand what process mining is before choosing a process mining tool. 

In this article, we will explain process mining inside out, such as what is process mining, how it works, what are the benefits it offers, how to choose a vendor and many other questions that you must know.

What is process mining?

Companies tend to think of their processes as simple workflows. But in reality, processes are complicated due to iterations, deviations and multiple interactions among process units.

Process mining discovers and monitors the current condition of actual processes rather than the ideal ones. It does that through: 

  • Leveraging real-time data and incident records to identify bottlenecks,
  • Reducing unnecessary steps within the workflows, 
  • And presenting factual insights. 

 As a result, companies can better focus their efforts on improvement.

Process mining definition

Process mining, also known as business process mining, is a technology and analytical discipline to gain process intelligence and develop a data-driven understanding of a company’s processes.

So what does process mining stand for? The name “process mining” comes from the field of data mining which is an outdated term for data science. The technology behind process mining operates similar to data mining.

Process mining vs. data mining

Data mining leverages different algorithms or methodologies to explore a given dataset. Similarly, process mining analyzes event logs and process-related data to “mine” processes.

The full understanding of processes includes:

  • Identifying process trends, patterns, and deviations
  • Detailed visualization of actual processes
  • Defining automation opportunities
  • Discovering new ways to increase process efficiency

How does process mining work?

Process mining tools discover actual process models out of the raw event logs. By extracting event logs from each case, and combining them, these tools show companies how their processes perform in reality. 

Understand how process mining works through the following steps:  

  1. The process mining tools integrate with various business systems of record (e.g. ERP and CRM systems) to collect the event logs from the company’s system.
  2. These tools extract the activity sequence for each case from the event logs.
    • In this step, variations between cases will become apparent. These variations occur because of manual changes or errors in the process.
  3. After deriving the activity sequence of each case, process mining tools start to “merge” these sequences.
    • As variations occur, the actual process will be more complicated than the planned one. This output also enables the company to understand where its process has deviated.

The video below visually describes what process mining is and how it works:

What are the main capabilities of process mining?

The main capabilities of process mining architecture are fourfold:

1. Process Discovery

  • Identifying as-is processes: End users usually do not have the full picture to know  how business processes progress. Process mining can visualize as-is processes (see Figure 1) and help users understand their situation by leveraging event logs and real-time data.
    • For example, HR departments of businesses can discover the intricacies of recruitment processes to identify the speed of the hiring process to deduce  why candidates drop out.
  • Identifying process deviations: Process discovery enables businesses to discover how actual processes deviate from the ideal  ones. With process mining tools, users can find unseen process deviations and their frequency. 
Figure 1: Example of an as-is processes visualization from Appromore process mining free trial

Process understanding is critical for process improvement and automation. For example, process understanding can help simplify automation projects. 57%, of executives believe that complexity of initial RPA projects leads to failures. 1

If you are interested, feel free to read our in-depth guides about process discovery and automated process discovery tools.

2. Conformance Analysis 

  • Analyzing the share of non-conforming  cases: Businesses prefer standardized processes, but achieving a high conformance level is challenging. Process mining allows companies to discover different process variations and their deviation levels  (see Figure 2).
    • For instance, in logistics, delivery processes vary largely due to several reasons. By leveraging process mining in logistics, firms can detect these variations and improve the delivery time. 
Figure 2: Example of a conformance analysis from Celonis process mining free trial.
  • Identifying their root causes: Process mining can detect why some processes differ from the desired ones.
    • For example, businesses can identify the homogenous errors by identifying the root causes. They then can classify the errors, look into why they happen, and try to resolve them effectively.

Explore more on conformance analysis and automated root-cause analysis capability.

3. Process Validation

Process mining leverages real-time data to provide insights for its users. It enables companies to validate their process findings and confirm their current situation. 

Process mining is also commonly used for auditing and compliance processes. For example, a consulting company could cover all their processes and transactions for auditing while reducing time spent by 50%.2

4. Process Improvement

  • Performance monitoring: Process mining provides a user-friendly interface to track KPI measures for users and help them identify the best/worst performing processes.
    • For example, one vendor shares that their process mining solution instantly offers more than 1000 standard KPI measures to reduce the time for building reports.

Why is process mining important?

Ideal process model without process mining for procure to pay process.
Figure 3: Ideal Procure to Pay Process

Business processes can be complex and tend to have many exceptions. For example, Figure 3 and 4 show the difference between an ideal, sequential procure to pay (P2P) process versus an actual one discovered by process mining, which is intertwined and complicated. 

Figure 4: Real P2P Process example from IBM Process Mining free trial

Business leaders and analysts need to have explicit knowledge of their processes when they aim to identify and prevent bottlenecks or find areas of automation. If not, they may take inaccurate and fruitless automation measures. 

For instance, a manufacturer deployed process mining into procure-to-pay operations to discover and optimize their processes. The insights process mining provided enabled the firm to identify new automation opportunities. Based on these insights, the firm could:

  • Automat the purchase order to invoicing process by 75%
  • Detect deviations and reduce maverick buying
  • Save $60,000 on reworking costs.

Why use process mining?

It is broadly applicable to your processes, your competitors are using it and it has substantial benefits.

Broad application areas: The chart below shows the distribution of process mining case studies across use cases. 

Figure 5: Process Mining Use Cases

The pie chart shows the proportion of process mining case studies and examples for industry and business function processes. Automotive industry 3%, insurance and healthcare 9% and banking industry occupies 21% of the all case studies. For the business functions, case studies are distributed as 6% for accounts payable, 12% for customer service & Audit, and 29% for IT service management.
The number of case studies distributed across process mining use cases.

Learn all process mining use cases in specific from our comprehensive articles:

Check out our comprehensive and constantly updated process mining case studies list to learn more.

Popularity: Interest in process mining has been increasing significantly

Read on for the benefits of process mining,

The benefits of process mining

Figure 6: Process Mining Benefits

Among process mining benefits, identifying process bottlenecks ranked as the number one result for 50% of the case studies we collected. This rank has been followed by reducing lead time and identifying non-compliant processes by 18%, enabling process automation by 7% and resolving inter-process conflicts as 5%. The other types of process mining benefits were observed in case studies take 2% in total.
The number of case studies distributed according to the benefits process mining provided.

Reduced Costs

Process mining allows users to identify areas that require automation or any other change. Automating processes increases efficiency while reducing costs. 

Improved Customer Experience

By identifying bottlenecks, discovering areas of improvement, and optimizing different processes, the total process time reduces. This situation enables faster delivery for customers and improves their experience with businesses. As a result, customer satisfaction increases, impacting revenues and customer loyalty.

Compliance Benefits

While auditing is a time-consuming process, fast analysis with process mining tools can shorten it. Besides, these tools can detect non-compliant processes and notify companies about such issues in real time. In a process mining case study, EY reduced its end-customer process analysis in less than a week by leveraging process mining.

Read and learn more on process mining benefits.

What are the steps in process mining?

Process mining projects are achieved in 5 steps, which are:

  1. Planning
  2. Data preparation 
  3. Process discovery and visualization
  4. Process analysis
  5. Progress assessment 

Learn how each step works by checking out our article on process mining steps

Process mining tools

The process mining market has various process mining tools with different capabilities. Some tools leverage AI and pattern recognition to improve their platforms. 

Some vendors offer process mining tools as open source or on free trials. The table below shows the list of these vendors in alphabetical order for each category. 

VendorsFree trial vs. Open source
IBM Process MiningFree trial
Aris Software AGFree trial
CelonisFree trial
LanaFree trial
Minit MicrosoftFree trial
UIPath ProcessGoldFree trial
QPR Process AnalyzerFree trial
ApromoreOpen source
DiscoOpen source
Process Street Open source
PromOpen source

To learn more, check out the open source process mining tools article.

Types of process mining 

There are two ways to look at different types of process mining:

  • Main capabilities of process mining such as, process discovery, conformance checks and process enhancement can be considered types of process mining
  • Technologies that complement process mining can be considered under types of process mining. These technologies are DTOs and task mining.
    • DTO, a digital twin of an organization, generates a virtual model of a given process or service including critical information, such as process performance and resource allocation. Read more on the digital twin of an organization and its applications.

Task mining vs. process mining

The difference between task mining and process mining is that:

  • Task mining records the users’ interactions in apps that employees use and documents, such as excel files.
  • Process mining extracts, analyzes, and models event logs data registered in IT systems.

It is recommended that analysts should leverage both tools so that they can include all task-level details while investigating the entire process flow. 

Leading vendors offer these two solutions in one package to enable a profound understanding of business operations and improve them. 

ُThere are mainly six major differences between the two tools. Learn about them by reading our task mining vs process mining article. 

Process intelligence vs process mining

Some vendors refer to process-related tools, such as process management software or process mining, as process intelligence software. Process intelligence highlights that process technology tools leverage machine learning & computer vision and integrate features like task mining and a DTO.  

Many process mining tools benefit from ML algorithms and context awareness to automatically collect and discover data and identify the root causes behind inefficiencies and deviations. ML also enables building predictive capabilities, generating a DTO or process simulation and offering task mining.

Process mining and IBM

IBM provides the latest additions to process mining functionality (i.e. Task Mining, DTO, automation) with support from one of the largest global services organizations.

For example, IBM process mining users can: 

  • Identify and recommend the RPA candidates,
  • Evaluate the performance of a new automation with a DTO
  • Record user activities with Task Mining,
  • And, automatically create RPA scripts based on the records with Automatic Bot Generation Capability.

Start your  free trial to check out IBM process mining. 

The future of process mining

Below are some of the insights we have collected about the future of process mining from different resources:

The process mining market continues to grow

Gartner estimates that the process mining market will grow around 40% to 50% and reach more than $1 billion in 2022.3You can gain more insights on market size by reading our process mining statistics article, which includes updates for the last four years. 

The interest in process mining is increasing

Figure 7: Process Mining Trends on Google Search

Interest in process mining is in increase since 2014 till today.
An increase in interest for process mining since 2014 is observed.

Source: Google Trends

The trends graph shows that the popularity of process mining has increased, especially over the last three years. We observe that the interest is approximately tripled in this period. As we consider the recent growth and investments in the process mining market, we believe this interest will continue to grow and help process mining become a more popular solution.

Process enhancement will keep gaining popularity

When process mining solutions emerged, businesses were using them for process discovery. However, process mining tools offer more capabilities than just process discovery today. Thus, other process mining capabilities like process conformance and enhancement will be widely used.

According to Gartner’s survey,

  • 38% of their respondents used process mining to discover processes in 2020. It is estimated that this share should have decreased to 34% by 2022. It is not easy to verify without running the same survey, but anecdotally, there is more interest in other process-mining capabilities
  • Process mining for process conformance has been increasing since 2017. Nevertheless, it slightly lost popularity, declining from 28% to 24% by 2022. 
  • Process enhancement was expected to increase from 44% to 42% during 2020-2022.

You can read our process mining trends for this year for more insights.

AI’s impact on process mining will be greater

Process mining tools used to leverage past data to offer business insights in the past. Today, process mining vendors provide real-time monitoring of processes with real-time data. Thanks to advances in AI technology, process mining tools can instantly classify events in real-time and provide insights and analyses. Thus, more process mining software integrates with task mining so that users can capture user interaction data.

In the future, we expect this to improve even further. For example, process mining can automatically predict future events, such as potential delays in delivery, and recommend improving processes. Some vendors have already launched process mining solutions that leverage machine learning to provide predictive analytics (e.g. IBM).

You can read our in-depth article for more detailed information on how machine learning is applied to predictive and prescriptive process mining.

Discovering automation opportunities and digital transformation will become more common use cases

The most typical process mining use case is business process improvement today. However, Gartner shares a decreasing trend for this use case. With the increasing popularity of RPA and digital transformation, supporting such initiatives with process mining is increasingly valuable. 

According to Gartner, discovering process automation opportunities and digital transformation have increasing trends and will become more popular use cases in the future.

Process mining will be integrated into other technology solutions

As process automation becomes a typical use case, new process mining solutions directly integrated into other technologies like RPA or IoT can emerge soon. 

Some RPA companies acquire process mining tools and vendors, while others develop process discovery bots for automated process discovery solutions.

To explore more on process mining integrations, you can read our list of process discovery tools offered by RPA and analytics companies.

How does process mining support automation and RPA?

RPA teams and any automation initiative team need to have an in-depth understanding of the process they are automating. This could do this by observing employees’ work patterns and conducting interviews. But it’s time-confusing and error-prone because people’s memories are quite erroneous.

Process mining software provides a detailed overview of which process steps are necessary for different output and guide automation efforts in a data-driven way.

After implementing process automation, process mining tools can monitor bot performance to measure process improvement and ensure that the automated process works as intended. RPA vendors indicate that process mining can increase process automation value by 40% while decreasing deployment time by 50%.

The Figure 8 shows 4 use cases of process mining to facilitate RPA deployment. For more on these steps, read our 4-step guide to process mining and RPA. To learn the differences between the two read our RPA vs. process mining article.

Figure 8: 4-Steps Guide to RPA with Process Mining

Process mining can facilitate RPA application by: bringing visibility and insights into the existing processes, identifying the best processes to automate, monitoring and optimizing RPA implementations during the project and catching up with the changes by following up.

Choosing a process mining vendor

Process mining vendors highlight various features to stand out in the market, complicating business leaders’ selection of tools that suit their business best. 

While choosing your vendor, you should consider a tool that is capable at:

  • Monitoring event logs to uncover actual business processes
  • Conducting compliance tests to detect anomalies and discrepancies
  • Providing organizational insights into which employees are deviating from the standard procedures
  • Integration with existing business software and IT infrastructure

To decide on a process mining vendor, follow our six steps guide on choosing a process mining tool.

We also prepared a vendor schema, illustrating which vendors claim to offer these capabilities on their product pages. The schema excludes vendors that have less than 30 employees. The vendors marked with a green square are the ones whose free trials we’ve tested.

Process mining vendors offer different capabilities that complicate business leaders' decision-making such as DTO, task mining and automation. Here we provide a schema to show which vendor provides which additional capabilities. We marked IBM, Celonis and Apromore since we tested these tools' free trials. In the schema, IBM found as a tool that provides all of these features.
Process mining vend schema

Which are the leading process mining vendors?

When we rank process mining software using objective, transparent criteria, we identify the top five vendors, which are:

  1. IBM
  2. Celonis
  3. QPR software
  4. Software AG
  5. UiPath

Read more on these vendors in our vendor analysis and compare these software to each other through our benchmarks:

Also, we prepared a checklist in Google sheets with recommended weights per criteria so you can have a transparent methodology to assess different vendors. Feel free to get it on your business email:

Get Process Mining Vendor Selection Guide

Process mining FAQs

Here we listed 6 commonly asked questions about process mining:

What is process mining?

Process mining refers to the techniques of discovering, analyzing and visualizing processes in an organization. It combines data science, process management and business intelligence fields to provide insights about process performance and improvement opportunities. 

How does process mining work?

Companies manage their processes through IT systems, such as ERPs, creating rich event log data. Process mining leverages this data and runs ML algorithms to model the data and discover patterns in it. 

Which are the 3 main attributes for process mining?

The three main attributes of process mining, also known as types of process mining or core capabilities, are process discovery, conformance check and process enhancement.

Process discovery helps users to generate a model, conformance check enables users to compare the discovered model against a rule or ideal model and enhancement ensures process improvement. 

Who can benefit from process mining?

The processes with a certain level of digital maturity are the ones that can benefit from process mining since the tool leverages digital prints left on the IT systems. 

Process mining cannot capture the process step where an employee prints a file, fills it manually and passes it to the relevant department. 

What are the benefits of process mining?

The process mining benefits include: 

  • Identifying bottlenecks and errors to improve 
  • Discovering best candidates to automate 
  • Standardizing and harmonizing across the organization 
  • Reducing lead time and additional costs 
  • Improving customer experience 
  • Boosting productivity 
  • Ensuring compliance 

What is the future of process mining?

The latest process mining trends indicate that:

  • The interest in process mining will continue to grow 
  • Process mining will be commonly used in automation initiatives
  • More vendors will invest in developing AI-driven process mining software and integrating their product with other tools such as IoT or RPA. 

Figure 9: Process Mining FAQs Cheatsheet

Process Mining Cheatsheet illustrating some process mining FAQs answered in the article, such as, what process mining is, its benefits, how it supports RPA and automation and the things to consider while choosing a vendor.

Further reading

To dive into process mining, check out our articles where we explain +30 applications of process mining and relevant case studies:

If you still have questions about process mining software, we would like to help:

Find the Right Vendors

Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
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Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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