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What Is Process Mining (and Why Isn’t It Enough)?

June 25, 2024

In this article, we’ll explore what process mining is, how it works, and what it can and can’t do for businesses looking to optimize their processes.

Jump to:

What is process mining?
How does process mining work?
Six benefits of process mining
Process mining limitations
Process mining use cases
How does process mining work within process intelligence?

What is process mining?

Process mining is a data-driven technique used to understand, track, and improve processes by analyzing data from information systems.

Applications such as CRM and ERP systems, as well as other systems of record, automatically create event logs that record every action taken. The data in these logs can be collected, or “mined,” to create an audit trail of the processes the applications are involved in. This works even when multiple applications are used in a single process.

Process mining technology follows these audit trails to build a process model showing the details of the end-to-end process, as well as any variations. Business users can analyze these models to find out if the processes are functioning as they should and, if not, investigate the root causes of deviations from the optimal path.

Why use process mining?

Businesses live by their processes, which are a prescribed set of actions employees take to get things done. When processes run well, the business runs well. When processes run poorly, the business faces various risks, from loss of revenue and customer dissatisfaction, to compliance violations.

Most businesses have a general idea of how their processes should run but lack insight into the day-to-day details of execution. Without this data, a business cannot make impactful changes. Process mining allows businesses to:

• Visualize and understand their processes in detail.
• Identify bottlenecks and inefficiencies, highlighting opportunities for improvement.
• Analyze process performance metrics in real-time to support resource allocation efforts.
• Improve compliance with internal and regulatory policies.

How does process mining work?

Before process mining , businesses had to do manual data reviews and interview stakeholders to find out how their processes were performing. This was often a slow, tedious process with a high margin of error. Process mining uses automation to depict real-world process performance accurately, faster, and more precisely than manual methods. There are four key steps to process mining:

Step 1: Data preparation

The first stage involves collecting event logs from various information systems, such as ERP and CRM systems, or workflow management tools. The data is cleaned to make sure it’s consistent, complete and doesn’t contain duplicates.

Step 2: Discovery and analysis

During discovery, algorithms will automatically generate visual process models based on the real sequence of actions seen in the event logs. This will include timestamps for each step and any variations found in process flows.

Process analysis involves comparing the discovered process models with your optimal process models or business rules, to check whether the observed interactions match expected behavior. This step will highlight any missing activities, bottlenecks, or delays.

Step 3: Enhancement

Enhancement involves refining and optimizing the discovered process models using the insights you’ve gained, and other data sources. This may involve reallocating resources, redesigning processes or automating tasks to improve efficiency.

Step 4: Monitoring and alerting

The final stage is to establish monitoring mechanisms to track process performance and the impact of optimization efforts. By iterating through the process mining cycle and monitoring changes, businesses will see continuous improvements in operational efficiency, quality, and compliance.

What are the benefits of process mining?

Process mining offers significant benefits to enterprises across all industries, including:

1. Increased transparency: Process mining gives you accurate and objective insights on how your operational processes are running. This helps you to identify bottlenecks, inefficiencies, and deviations.
2. More data-driven decision-making: With access to detailed insights and performance metrics, businesses can confidently make decisions to optimize performance, backed by data. These decisions may include prioritizing improvement initiatives or reallocating resources.
3. Increased efficiency: By identifying opportunities to simplify and automate processes, businesses can streamline workflows and reduce process lead times.
4. Reduced costs: Improving business efficiency and resource allocation is one way process mining reduces costs. Process mining also works with your existing systems, so you don’t need to invest in new applications to expand your operational capabilities.
5. Enhanced customer experience: Identifying opportunities to simplify customer journeys and speed up internal processes will help you to improve your customers’ experience.
6. Continuous improvement. Process mining isn’t an isolated activity. It encourages continuous process improvement by enabling you to simulate proposed changes and providing feedback.

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Where does process mining fall short?

Process mining offers enormous advantages over manual approaches to process analysis, but it has its limitations. For example:

• Traditional process mining identifies process-related issues, stopping short of providing granular answers concerning the root causes of those issues.
• Process mining works well in simpler scenarios, yet lacks the sophistication to evaluate complex processes with a large number of valid variations.
• Process mining can only analyze past performance, lacking the ability to monitor processes on an ongoing basis and to alert users to deviations.
• Some traditional process mining tools may be limited in the types of data sources they can connect to, which can limit the value they can provide.

All of these limitations of basic process mining solutions can be overcome with a comprehensive, modern, next-generation solution: enter process intelligence.. Process intelligence solutions help businesses uncover and analyze root causes of problematic bottlenecks, can visualize any process, including messy and ad-hoc ones, and enable businesses topredict AND simulate potential process improvement measures.. The most effective process mining solutions include process intelligence capabilities.

Process mining use cases

Process mining is a technique that can be leveraged in any industry to gather insights into process execution and inefficiencies. Here are five use cases that demonstrate its versatility:

1. Financial services
   Process mining can help financial services organizations to streamline their interorganizational processes, from loan processing to account auditing. By analyzing process flows, handling times and error rates, financial institutions will identify ways to improve their operational efficiency. This results in reduced costs and a better customer experience.
2. Healthcare
   Organizations in the healthcare sector can use process mining to improve patient care pathways. Scrutinizing treatment protocols, processing times and resource allocation will help providers to spot process inefficiencies, from admission to discharge. Addressing these issues will reduce wait times, enhancing the overall quality of patient care.
3. Education
   Process mining offers more than just insights into process inefficiencies. It also enables users to check engagement and interaction with critical systems. Educators can analyze the effectiveness of course curriculum by tracking student behaviors, such as how long they spend viewing class materials.
4. Manufacturing
   Effective resourcing, production processes and equipment utilization are crucial in the manufacturing industry. Process mining helps enterprises analyze important workflows to enhance overall equipment effectiveness (OEE) and reduce downtime. Analyzing individual product performance will also ensure accurate resource allocation (including workers, machines or storage space) to meet demand.
5. Supply chain
   Process mining is crucial for optimizing supply chain operations, from procurement to delivery. Enterprises can use it to identify bottlenecks, and improve inventory management. With task mining , a distinct form of process mining, supply chain organizations can delve into users’ interactions with their management systems, pinpointing recurring tasks and opportunities for automation.

How does process mining work within process intelligence?

In the current competitive business environment, streamlining and optimizing business processes is crucial for an organization’s success and long-term advantage. Many businesses have realized by now that a lack of effective process analysis tools can potentially result in the introduction of significant risks. But even when process-related information exists—organizations often lack the understanding of how to put it into action. This is because not all process analysis tools on the market are the same. Many legacy or traditional process mining and task mining tools alone fall short of their promise to solve process problems outside of rigid applications and functions such as finance and procurement.

The limitations in the first generation of process mining and task mining offerings have led to the emergence of more comprehensive “process intelligence” platforms, designed to fully meet the demands of today’s enterprises and go beyond surface-level issues. Modern process intelligence solutions today comprise the following five key pillars: process discovery, process analysis, process monitoring, process prediction, and process simulation.

Process intelligence works with all processes, whether they’re simple, unpredictable, manual or automated. Process intelligence technology monitors each process instance as a new step occurs. It alerts the user, or can take automated action when it identifies a process behavior of interest.

Process intelligence supports robotic process automation, or RPA , initiatives by identifying good automation candidates, and then monitoring and reporting on the process bots participate in. In doing this, process improvement is able to deliver:

• Greater productivity
• Reduced risk of costly compliance violations
• More streamlined efficiencies, leading to happier customers, happier employees, and a greater competitive edge

Optimize your business processes with our end-to-end process mining platform

ABBYY Timeline is our market-leading process mining platform , built to power data-driven process improvement.

It will help you visualize your end-to-end process to identify inefficiencies, bottlenecks, and outliers. With insight into your existing processes, you can make informed optimization decisions. Timeline also has a process simulation feature, enabling you to test potential processes changes, and assess their business impact. Our low-code process mining platform comes with over 25 pre-built tools, so you can start analyzing your processes within minutes.

Ready to transform your business processes?

Request trial

Tamas Mahr

Head of ABBYY Timeline Development, Administrative Director of ABBYY Hungary

Connect with Tamas on LinkedIn .

Editorial note: This blog post was originally published on July 21, 2021. On March 30, 2023, we updated this blog to reflect the new capabilities of ABBYY Timeline 6.0 including new UI, deeper analytics, and an advancement of ABBYY’s process intelligence capabilities, process simulation. On June 25, 2024, we updated this blog with insights on four key steps of process mining and benefits why to use process mining, we provided clarified explanations of how process mining works with process intelligence and how to optimize business processes, and answered three frequently asked questions all around process mining, RPA, and AI.

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Frequently asked questions

What are the 3 types of process mining?

The three main types of process mining are:

1. Discovery
   Process discovery involves automatically generating process models from event logs. It aims to reveal the actual process flows recorded in the logs, without prior knowledge of the process.
2. Conformance
   Conformance checking verifies whether the proposed process model is consistent with its real-world execution. This process mining method compares a process description with an existing process model using event log data, to identify deviations.
3. Enhancement
   The aim of process enhancement (also referred to as an extension process or performance mining), is to optimize the quality and completeness of existing process models. For example, this might involve using data from a conformance check, along with other domain expertise, to remove a bottleneck.

How is AI used in process mining?

AI plays a critical role in process mining by automating several aspects of process discovery and optimization. For example:

1. Discovery
   At the initial process discovery stage, AI algorithms infer process models from event logs, reducing the need for manual modeling.
2. Conformance
   Anomaly detection, powered by AI, identifies process deviations or irregularities in the discovered models. This data shows enterprises’ opportunities to streamline processes.
3. Enhancement
   AI technologies automate repetitive tasks found at the conformance checking stage. Predictive analytics models then leverage AI to forecast future process behavior and outcomes, based on proposed changes. This allows users to proactively decide next steps.

What’s the difference between process mining and RPA?

Both process mining and robotic process automation (RPA) aim to optimize business processes. However, they serve distinct purposes, and are used at different stages of the process improvement lifecycle.

Process mining is a technique used to analyze event data, with the goal of understanding and optimizing processes. It uses algorithms to holistically visualize end-to-end processes, providing insights on execution and performance. In contrast, RPA automates repetitive tasks by deploying software robots to perform predefined processes, boosting efficiency and reducing manual effort.

In summary, process mining focuses on understanding how processes unfold, while RPA automates rule-based, repeatable tasks.

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