What is Process Mining? Unlock Hidden Business Insights

Companies today generate enormous amounts of data every day. Every task completed, transaction processed, or request handled leaves a trace – tiny records that, when pieced together, form the bigger picture of how a business truly runs. Yet despite this abundance of information, many organizations still struggle to put it to good use when it comes to refining operations or cutting inefficiencies. 

This is where process mining makes a difference. It’s a method designed to bridge the gap, by taking data from information systems and using it to map, analyse, and enhance actual business processes. Instead of relying on assumptions or theoretical models, process mining uses real data to highlight what’s working, what isn’t, and what could be improved. 

In this blog post, we break down the core concepts, steps, and benefits of process mining – along with how it fits into a broader strategy for process excellence. Whether you’re new to the topic or looking to explore its practical applications, this guide offers a clear starting point. 

Process mining is a data-driven approach that helps organizations to better understand how their processes truly run. It works by extracting event logs from information systems like ERP or CRM tools, and uses this data to visualize actual process flows. 

Unlike traditional process analyses, often relying on assumptions or interviews, process mining gives an objective view rooted in real system data. This allows organizations to more accurately identify inefficiencies, streamline operations and make better informed decisions about how to improve performance moving forward. 

Process mining turns fragmented data into a comprehensive view of your operations, one that can be analysed, measured, and improved. Here are the core elements that make this possible:

Event logs are digital records that show when and how steps in a process take place. The systems you use generate these logs automatically during day-to-day operations and serve as raw input for process mining.

This is the act of creating a process model based on the real-world data extracted from the event log. It allows teams to uncover the actual flow of work, often revealing hidden inefficiencies and deviations from the expected behavior.

Conformance checking compares the discovered process model to an expected or ideal process to identify deviations. This is particularly useful for ensuring compliance with internal standards, policies, or regulatory requirements.

Performance analysis highlights bottlenecks, repetitive steps, and other inefficiencies that can be optimized for smoother, faster operations. By tracking key process metrics continuously, businesses can detect performance issues and make adjustments to enhance productivity.

Once insights are gathered, organizations can use these to redesign and refine their processes. This could encompass anything from simplifying workflows, reducing delays, or introducing automation to cut costs and improve process speed.

Hint: For a closer look at the process mining terminology, check out our cheatsheet on process mining fundamentals.

Core Capabilities of Process Mining

Every action in your systems, from completing a task, moving a file, or logging a request, leaves behind data. These digital traces (residing in your ERP or CRM systems) form the basis for process mining.

This collected data is transformed into a visual model that shows how work actually happens. It reveals the real process flow, including its variations and gaps between how things should run and how they do. 

Process flow visualization based on recorded event data

Once the process is mapped, areas for its improvement (such as delays, unnecessary steps, repetitions, etc.) become much clearer. This is then what serves as a guideline for teams in terms of what to fix and where to streamline.

With ongoing monitoring, process mining supports continuous improvement of processes. As changes occur, you can track their impact and stay aligned with your performance goals.

Dashboards with key process metrics and performance trends

Process mining shows where processes waste time or resources. With the help of these, businesses can reduce cycle times and improve resource utilization, which directly results in reduced operational costs.

Clear process insights help teams spot compliance issues before they grow and address risks early. Process mining can ensure that operations stay aligned with required standards by offering visibility and transparency into process flows. With everything tracked step by step, it is easier to follow regulations and prepare for audits without scrambling. In addition, you can reduce the likelihood of costly penalties your company might face.

Instead of relying on intuition or outdated data, businesses can use real-time data and accurate information. They can see the full picture, focus on problem areas, reduce uncertainty and make changes with confidence.

Customers notice when processes work well. Clean, efficient processes help teams respond quickly and deliver more reliably by reducing the risk of errors. With the help of process mining, the agility of your organization can also be enhanced. When customer needs or market conditions change, it is easier to adjust without causing delays.

One of the greatest things about process mining is that you get to watch your business processes work, rather than having to go off assumptions. This level of transparency is so important because it gives businesses a clear picture of what is actually going on. Whether it’s a point where things get stuck, a place where mistakes just keep happening, or just areas that are just not performing the way you expect them to, it helps you catch issues early.

Many tasks still rely on repetitive manual work. Process mining points out where automation can help, cutting down on human errors, speeding things up, and giving teams more time to focus on what matters. 

Many businesses struggle with cross-functional communication and collaboration. Process mining offers a shared, data-driven view of processes. When everyone can see how a process flows, it is easier to coordinate. Teams understand each other’s roles, how their work affects others and can solve problems without stepping on each other’s toes.

Hint: Thinking about applying these benefits to your business processes? Check out the Process Mining module we offer for ADONIS

Process intelligence builds on process mining by adding more depth. Instead of just showing what’s happening in a process, it helps explain why. With process intelligence, we can utilize technologies like machine learning and AI to extract more insights from the data. Through it, we not only visualize processes but also predict future trends, identify hidden patterns, and give intelligent recommendations to improve performance. It is an intelligent assistant that helps you better manage your business processes.

Process mining shows how work flows through systems. Process intelligence takes that further and analyses why certain patterns emerge. For example, if a task is repeatedly delayed, process intelligence can tell you whether it’s due to resource shortages, system slowdowns, or other causes. These insights help teams move from reaction to prevention.

Process mining shows you the data, but process intelligence helps you make sense of it. Combining the two means: 

• Deeper understanding: Go beyond visualizations to get context behind performance. 

• Predictive insights: Spot potential issues before they escalate. 

• Smarter decisions: Use AI-driven suggestions to adapt faster and improve continuously. 

Together, they form a strong foundation for scaling process optimization with confidence. 

While data mining looks for general patterns in large datasets, process mining focuses on understanding how specific processes unfold over time. Data mining might tell you what customers are likely to buy. Process mining tells you how the order-to-cash process flows, and where it stalls. 

In short: 

• Data mining is a broader concept that uncovers general trends. 

• Process mining is a specialized type of data mining that reveals flows and bottlenecks in operations. 

Task mining zooms in on how people work, capturing every click, action, or keystroke in software tools. It helps break down individual tasks and uncover inefficiencies at the user level. 

Process mining, on the other hand, takes a bit of a broader view, mapping entire processes across systems. Together, they provide both micro and macro perspectives, showing you not only how tasks are performed but also how they fit into the larger process. 

Business Process Management (BPM) focuses on designing and improving process models. But those models are often based on assumptions. Process mining brings real-world data into the picture, validating how processes actually run. 

Used together: 

• BPM designs and optimizes the business processes. 

• Process mining checks if reality matches the design, and helps monitor performance over time. 

This combination makes process improvement more accurate and reliable because decisions are backed by data, not just assumptions.

Robotic Process Automation (RPA) automates repetitive tasks. But without visibility into the bigger process, automation might target the wrong steps or miss key inefficiencies.

This is where process mining helps, by identifying the best automation opportunities upfront. And once RPA is in place, process mining continues to monitor performance, track outcomes, and ensure long-term value. 

Process mining is not a silver bullet. Some expect it to instantly resolve all inefficiencies, but its value actually depends on the quality of your data and how you interpret the results. It doesn’t replace strategy or expertise, it supports them with evidence. It’s also often confused with automation, but in fact, process mining is about understanding and improving workflows, not automating them.

One of the biggest hurdles is the data quality. Event logs must be complete, consistent, and accurate. If key actions are not recorded, or data across systems doesn’t align, the insights as a result wont be reliable either. In many companies, process data resides in several systems, making integration a complex and time-consuming step. 

Start with solid data management. Clearly define how data should be collected, stored, and shared across systems. Invest in proper integrations with APIs or data warehouses to bring everything together. 

Work with your teams to ensure that the data reflects how processes happen in reality, not just how they’re supposed to. And keep refining your setup over time. Like the processes themselves, your process mining approach should evolve as your business does. 

Process mining provides a clear, data-backed view of how a company’s processes truly run. By moving beyond assumptions and drawing on real system data, it helps organizations uncover inefficiencies, cut costs, and make smarter decisions. 

It doesn’t replace business strategy, it reinforces it. With continuous monitoring and detailed insights, process mining enables teams to act with confidence, making improvements that are grounded in reality. 

And in the highly digitalized landscape most businesses operate in today, that level of visibility isn’t just useful – it’s essential.