Process mining is a data-driven technique that analyzes event logs to understand, monitor, and improve business processes. This technology extracts information from event logs and audit trails in an organization's information systems, such as CRM or ERP systems. The data is used to create a process model that shows how a process is actually executed, including any variations.
Business as usual won’t cut it in today’s competitive landscape, especially when most organizations are sitting on untapped data.
Enter process mining — a powerful technology that unlocks insights into your company's actual workflows by analyzing data from IT systems. Whether you’re looking to improve operational efficiency or drive digital transformation, business process mining can be a game-changer.
But what exactly is it, and how did it evolve into the essential tool it is today?
This blog will explore the history of process mining, break down process mining vs process discovery, and look ahead to the future of process mining technology. By the end, you’ll have a clear understanding of how process mining can optimize your business operations and why it's critical for companies in industries like retail, manufacturing, and government organizations.
Simply put, process mining is a data-driven method for analyzing and improving business processes.
It uses event logs generated by enterprise systems (like SAP or Oracle) to create a visual map of how processes actually unfold within an organization. Unlike traditional process management tools that focus on pre-set process models, process mining discovery looks at real-time data to uncover the truth about how work is done.
For businesses, this means gaining a clearer view of process mining conformance (how actual processes align with planned ones), and identifying inefficiencies or opportunities for optimization. Dealing with a claims process or an IT workflow? Process mining enhancement helps companies streamline operations, reduce costs, and increase customer satisfaction
Imagine you’re trying to improve a product, but you don’t have a clear view of how it’s made. You know the steps, but the details are blurry. Process mining is like installing a clear lens that lets you see each step in crisp detail—how work flows, where it’s getting stuck, and where it’s performing well. It doesn’t guess or assume; it shows you exactly what’s happening so you can make smarter, data-backed decisions on how to improve.
Though it may seem like a recent innovation, process mining’s roots go back several decades. It began as a way for businesses to better understand their processes through data, but as the digital landscape grew more complex, so did the need for a clearer, more automated solution.
To understand where process mining is today, it’s essential to look at its history.
Process mining began in the early 2000s when Wil Van der Aalst, often referred to as the godfather of process mining, began formalizing the concept. Van der Aalst’s pioneering work helped lay the foundation for modern business process mining practices.
In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the Process Mining Manifesto, calling for the development of a new field of study focused on the extraction and analysis of process-related data.
The manifesto called for a new approach to process analysis—one that could pull actionable insights from the vast, often untapped, sea of operational data. It wasn’t just about automating processes; it was about understanding them at a granular level, uncovering inefficiencies, and making business operations more transparent and agile.
Since then, process mining has transformed from a niche concept into a core element of business process management (BPM). It’s evolved from simply identifying problems to providing organizations with the insights they need to take decisive action, moving them toward smarter, more efficient workflows.
But the story doesn’t end there.
Today, process mining is poised to enter its next chapter—one where the line between data analysis and intelligent automation begins to blur. Rather than merely uncovering inefficiencies, the next frontier is about actively enhancing operations. And that’s where the difference between process discovery and process mining becomes crucial. While they’re interconnected, they serve distinct purposes on the journey to operational mastery.
Process Mining vs Process Discovery
You may have heard of process discovery and wondered how it compares to process mining. While they are closely related, the distinction between the two is crucial to understanding how businesses use them to drive improvements.
Process mining is the umbrella technique—think of it as the whole toolkit—for analyzing event logs to uncover and optimize business processes. It includes several core components: discovery, conformance, and enhancement. Essentially, process mining takes a deep dive into your business data to reveal what’s really happening behind the scenes. It’s about using raw data to continuously refine and perfect your processes.
Process discovery, however, is the starting point within this process. It’s like putting on a pair of magnifying glasses to get a clear, detailed view of your workflows. It’s the process of creating an initial, visual map of how work actually happens within your organization, based on event data. Process discovery takes all the scattered data and assembles it into a coherent map, showing exactly how tasks and information flow through your systems. This allows businesses to see the true state of their operations, free from assumptions or guesswork.
In other words, process discovery is the foundation. Once the process is discovered, it’s time to take it further with conformance checking—to make sure that actual workflows align with business goals—and enhancement, where improvements are made to optimize the processes for better performance.
Together, process discovery and process mining create a dynamic, data-driven approach to operational efficiency.
Process mining technology has evolved significantly since its inception. In its early stages, process mining was primarily about analyzing event logs from traditional IT systems, providing organizations with valuable insights into past workflows. While this approach offered visibility, it was limited to static data and couldn’t capture the dynamic nature of how work truly unfolds in real time.
As technology advanced, the concept of process intelligence emerged—an evolution that takes process mining to the next level. Modern process discovery platforms, like Skan AI’s process intelligence platform, utilize AI and computer vision to track and observe workflows as they happen. This approach allows businesses to visualize how people interact with digital systems, capturing both the digital and human elements of work without the need for complex backend integrations. With this real-time visibility, organizations can now optimize their processes on the fly, rather than relying on historical data alone.
This shift from static, retrospective analysis to real-time, dynamic process tracking has redefined the possibilities for operational improvement. Industries like manufacturing, retail, and government now have the tools to continuously refine their processes, leading to more agile, efficient, and responsive operations at scale.
The next phase in this evolution is the integration of agentic AI—intelligent systems that not only discover inefficiencies but also take action to resolve them autonomously. Imagine an AI that can identify process bottlenecks, simulate the impact of different optimizations, and automatically implement those changes. With agentic AI, process mining becomes more than just a tool for discovery; it evolves into a fully autonomous system that can optimize workflows in real time, adapting to shifting conditions and continuously improving efficiency.
By combining the power of real-time data, AI-driven discovery, and autonomous decision-making, process mining technology is entering an era of continuous, self-sustaining process optimization. The future of process intelligence is not just about understanding what’s happening—it’s about actively driving improvements, with AI leading the way.
Process mining has been redefining how businesses operate by uncovering inefficiencies and providing actionable data to drive smarter decisions. Across industries, organizations are using process mining to optimize workflows, reduce costs, and improve outcomes.
Here’s a deeper look at how process mining is already making an impact in various sectors:
In manufacturing, process mining is used to optimize production and supply chain operations. For instance, a company producing automotive parts might uncover delays due to inconsistent inventory deliveries or underutilized machines. By identifying these inefficiencies, process mining helps teams streamline workflows—adjusting schedules, reallocating resources, or improving supplier coordination—to reduce downtime, increase throughput, and lower operational costs.
Insurance companies apply process mining to enhance claims management and underwriting. By mapping out the end-to-end claims process, insurers can pinpoint delays caused by redundant approvals, manual handoffs, or missing information. Process mining then helps automate steps, improve data flow, and accelerate decision-making, ultimately speeding up claims resolution and improving customer experience.
Hospitals and healthcare providers use process mining to improve patient care workflows. For example, by analyzing emergency room operations, process mining can uncover bottlenecks in patient triage or lab testing that extend waiting times. With these insights, healthcare organizations can optimize resource allocation, streamline procedures, and reduce wait times, ultimately delivering faster and more efficient care.
In government organizations, process mining helps improve compliance and optimize public service operations. A common application is in managing the complex invoicing processes for public works projects, where multiple contractors and agencies are involved. By visualizing the entire workflow, process mining can identify inefficiencies, ensuring timely payments, accurate reporting, and adherence to regulations, all while reducing administrative overhead.
In e-commerce, process mining is used to understand and optimize the customer journey. For example, an online retailer might analyze purchase patterns and uncover issues like slow checkout processes or payment failures that cause cart abandonment. With these insights, businesses can refine their checkout systems, reduce friction, and boost conversion rates.
Financial institutions use process mining to improve operational efficiency and compliance. For example, in the loan approval process, process mining can identify delays caused by incomplete documentation or manual data entry. By automating key steps or improving data validation, financial organizations can speed up approvals, reduce errors, and enhance customer satisfaction.
IT teams use process mining to streamline service delivery and incident management. For instance, by analyzing service desk operations, process mining can reveal bottlenecks in ticket resolution or issues with resource allocation. By using these insights, IT departments can improve response times, optimize team workflows, and deliver a better overall customer experience.
In software development, process mining helps teams document, analyze, and optimize their workflows. For example, process mining might reveal inefficiencies in sprint planning or code review cycles, causing delays in project timelines. By identifying these gaps, development teams can refine their processes, minimize rework, and accelerate delivery.
Business Process Management (BPM) focuses on optimizing, automating, and continuously improving an organization’s workflows to drive efficiency and achieve strategic goals. Process mining plays a critical role within BPM by offering a data-driven approach to understanding, analyzing, and enhancing business processes.
Where traditional BPM tools may rely on assumptions or static process models, process mining injects real-time, factual data into the equation. This allows businesses to accurately map the flow of work, spot inefficiencies, and measure conformance—ensuring processes are not only executed as planned but optimized for better performance.
As part of the BPM lifecycle, process mining supports discovery, conformance, and enhancement efforts. Discovery lays the groundwork by visualizing current workflows, while conformance checks ensure that actual processes align with business goals. Finally, enhancement leverages insights to refine workflows and continuously improve operations.
Looking ahead, Process Intelligence will take center stage in driving the next wave of BPM innovation. With platforms like Skan AI, businesses are not only able to uncover inefficiencies but gain actionable insights into how to optimize and automate workflows at scale. Our platform's advanced analytics enable continuous monitoring of processes, empowering organizations to make data-backed decisions and achieve long-term performance improvements. As organizations continue to embrace the full potential of process intelligence, they can expect to see more agile, adaptive operations that proactively evolve in response to changing demands and opportunities.
As digital transformation continues to reshape industries, the demand for process mining technology is only expected to grow. In fact, the process mining market is predicted to see significant expansion as more companies adopt AI-driven process discovery tools to improve their workflows.
Organizations across the board are realizing the power of process mining to drive efficiency. Whether you're in manufacturing, retail, or government, process mining has the potential to streamline operations, enhance productivity, and increase overall business performance.
The integration of agentic AI in the coming years will reshape the market. With AI systems that can autonomously take action, monitor processes, and suggest real-time changes, businesses will be able to optimize at a level of precision and speed that is impossible to achieve manually.
Businesses will not only uncover hidden inefficiencies but also leverage autonomous AI systems to continually optimize workflows, enabling rapid adaptation and sustained competitive advantage. By understanding the evolution of process mining and process intelligence, businesses can unlock new opportunities for growth, improve decision-making, and ultimately thrive in the digital age.
Ready to take your operations to the next level?
Download the Process Intelligence Playbook to explore how process intelligence can drive smarter decisions and enhance your business processes.
Integration of AI-powered Process Intelligence stands at the forefront of the transformation to revolutionize how healthcare payer organizations...
Skan's process intelligence platform uses machine learning and computer vision to provide a complete view of digital processes. Skan helps...
Get the inside intelligence on Process mining and its next evolution, process discovery. Discover how it gives an organization the tools it needs to...
Unlock your transformation potential. Subscribe for expert tips and industry news.