A man employing prescriptive analytics looks out of a window at a city during the night.

How Does Process Mining Work? All You Need to Know

Key takeaways

  • Process mining gives a clear view of your business processes by analyzing event logs.
  • It identifies process inefficiencies, enabling data-driven decision making for improvement.
  • Continuous advancements in process mining tech underscore its growing importance for businesses.

Process mining has become a critical tool in understanding how your business operates from a data-centric point of view.

Essentially, it involves the use of specialized algorithms to analyze event logs from your company’s IT systems.

This analysis helps to create a visual model of your processes, including how they actually function versus how they were designed to.

Discovering the discrepancies and bottlenecks in the operational flow enables you to pinpoint areas for improvement, compliance monitoring, and to optimize overall performance.

One of the core strengths of process mining is its transformative impact on business process management.

It provides a factual basis for process improvement initiatives, by offering clear insights into the sequence of events and their timing.

This information is invaluable when you aim to streamline workflows, prevent deviations, enhance productivity, and ultimately, to foster an innovative business environment.

Fundamentals of Process Mining

Process mining transforms the way you understand your operational workflows by analyzing event logs to reveal insights and efficiencies. This powerful approach offers you a clear view of when, where, and how your business processes operate.

Defining Process Mining

Process mining is a technique you can use to get an inside look at what’s happening within your company.

At its core, it’s about analyzing event logs that your IT systems generate during the execution of processes. These logs contain events with timestamps that tell you not just what happened, but precisely when.

Every process step you take leaves digital traces — event log data — in the transactional systems you use. Process mining extracts those digital traces and meaningfully connects them to help you understand your business better.

Key Components of Process Mining

To effectively utilize process mining, you should be familiar with its key components:

  • Event Logs: The data source for process mining, event logs are records of sequential events captured by your information systems. They’re the backbone of the analysis, containing details like timestamps, event types, and other contextual data.
  • Events: Within these logs, an event represents a single step or activity within a larger business process, such as a transaction’s approval or rejection.
  • Timestamps: Events come with timestamps which are crucial for understanding the sequence and duration of processes. They help you pinpoint bottlenecks and understand the flow of activities over time.

By closely examining these elements in your log data, process mining software reconstructs a virtual map of your business processes—often with surprising accuracy.

Process Mining actual process vs desired process

Image source: Appian Documentation for Process Mining

The Process Mining Workflow

Process mining transforms your understanding of internal business workflows by pinpointing inefficiencies and revealing process enhancement opportunities.

1. Data collection and preparation

Your first step in the process mining journey is data collection and preparation. You’ll gather detailed event data from various IT systems used by your organization.

An isometric image of a data storage system designed for process mining

This is not just any data; it’s specific to the events of your business processes—timestamps, user IDs, task names, and other relevant metadata. The accuracy and completeness of this data are crucial, as they form the foundation for reliable analysis.

Here’s how to start:

  • Identify Data Sources: Gather data from various IT systems that track business activities, such as ERP, CRM, or BPM systems.
  • Extract Event Logs: Event logs are the raw transaction data that capture events like starting a task, completing an activity, or updating a status.
  • Prepare and Cleanse Data: Ensure the data is clean and consistent. This might involve removing errors, standardizing formats, and consolidating data from different sources.
  • Ensure Compliance and Privacy: Address any data privacy concerns by anonymizing sensitive information and adhering to relevant regulations.

2. Event log analysis

Once you have the event logs, it’s time for event log analysis. These logs are meticulously examined to understand how processes actually function as opposed to how you think they do.

A data scientist is sitting at a desk with a laptop and graphs on the screen doing event data analysis for process mining

You’ll discover the sequence of events and identify any deviations or unusual patterns that might indicate inefficiencies or bottlenecks.

It’s where we start to uncover the true story of your business processes.

  • Discover Process Models: Process mining algorithms use event logs to automatically generate visual models that show the sequences and paths of your business processes.
  • Identify Variations: Compare the discovered process models to the ideal or expected process flows. This highlights deviations, bottlenecks, and inefficiencies.
  • Analyze Performance: Assess the performance of various process steps by analyzing factors such as time, frequency, and resource utilization.
  • Gain Insights: Use the analysis to identify process improvements, potential areas for automation, and best practices.

3. Process Discovery

During the Process Discovery phase, a visual model of your current process is created. This step gives you a clear picture of the workflow, allowing you to spot areas that differ from the ideal or expected process.

A person is standing in front of a laptop with a lot of icons, exploring process discovery

You’ll see exactly where those bottlenecks or inefficiencies are, which is the first step in strategizing ways to address them.

This image from Celonis is illustrative how the reality often are different from what we expect

Process Mining actual process vs desired process

Process Discovery is where the actual processes are uncovered and visualized.

  • Visualize the Process: Utilize specialized process mining software to create a visual model of the process based on the event logs. This is often presented as a flowchart or a similar diagram.
  • Understand the Workflow: The visual model allows stakeholders to see the sequence of tasks, decision points, and paths that make up the business process.
  • Identify Variants: Recognize different variations of the process that occur in reality, which may differ from the assumed or ‘to-be’ process models.
  • Spot Opportunities for Improvement: Analyze the visual model to identify inefficiencies, redundancies, or bottlenecks that could be streamlined for better performance.

4. Conformance Checking phase

Next is the Conformance Checking phase. Here, you compare the visual model against predefined process models (the way things should be).

A group of people studying process conformance with process mining

It’s like putting your actual workflow to the test to see if it’s up to par. This helps to highlight areas where reality diverges from expectations, signifying possible deviations or non-conformance.

Conformance Checking phase helps to compare them against the intended design or compliance standards.

  • Compare with Ideal Processes: Align the discovered process model with the predefined ‘to-be’ models or compliance requirements to spot discrepancies.
  • Quantify Compliance and Deviations: Measure the degree of compliance and identify the root causes of deviations or non-conformities.
  • Understand the Impact: Assess the impact of deviations on process performance, customer satisfaction, and other critical business outcomes.
  • Initiate Corrective Actions: Use the insights gained from conformance checking to make informed decisions about necessary changes or improvements to processes.

5. Process Enhancement

Finally, Process Enhancement is where the action happens. Based on your discovery and conformance checking, you enhance your process by removing inefficiencies, implementing automation where possible, and rerouting tasks to eliminate bottlenecks.

The goal is to streamline your process for improved performance and efficiency.

A data analyst is sitting at a desk with a laptop doing process enhancements

Process Enhancement is the action-driven phase of process mining where the goal is to optimize and refine the processes that keep your business running.

  • Implement Improvement Measures: Based on the insights from Process Discovery and Conformance Checking, identify and implement changes to enhance process efficiency and compliance.
  • Automate and Optimize: Look for opportunities to automate repetitive tasks and optimize process flows to reduce cycle time and improve overall performance.
  • Monitor Changes: Keep an eye on how the implemented changes affect the process by continuous monitoring, ensuring the enhancement goals are being met.
  • Engage Stakeholders: Involve process participants and decision-makers in the enhancement phase to ensure buy-in and to align improvements with business goals.

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

Technology Behind Process Mining

When exploring how process mining works, you’ll come across some fascinating technology that transforms raw data into insightful process maps.

This technology combines data mining techniques with predictive analytics powered by artificial intelligence (AI), giving you a window into your operations.

Data Handling and Extraction

To get started, process mining requires access to your event logs. These logs are data-rich records of the activities carried out by either people or machines.

Initially, data handling and extraction is crucial; it involves collecting data from various IT systems and ensuring it’s in a usable format. Imagine the raw data as puzzle pieces. Before you can solve it, you need to ensure all pieces are present and oriented correctly.

Efficient process mining relies on extraction tools that can seamlessly interface with systems like ERP, CRM, and BPM. This interaction allows for the retrieval of comprehensive event logs in a structured manner, which might include timestamps, user IDs, and task information.

Process Mining Algorithms

Once the extraction is done, the next step is where the real magic happens with process mining algorithms.

These specialized algorithms scrutinize your data to identify trends and patterns. For example, they can reveal how tasks flow from one step to the next or pinpoint where bottlenecks hinder your process efficiency.

At the heart of these algorithms lies machine learning, a subset of AI, which facilitates the understanding and improvement of processes without being explicitly programmed for specific tasks.

Artificial intelligence enhances these algorithms, allowing them to adapt and improve through use. It’s like teaching your computer to think like a detective, unravelling the complexities of your processes to present clear, actionable insights.

A group of people standing in front of a computer screen discussing offensive and defensive data strategy.

Process Mining Tools and Resources

When you’re looking to improve your business processes, the tools and resources at your disposal can make all the difference.

Your choice in process mining software not only provides insights into your current performance but also guides the strategies for enhancement.

Software Solutions Overview

Process mining tools are vital for businesses aiming to analyze and enhance their operational workflows.

These tools extract data from event logs within your systems, such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) software, to construct a clear model of your processes.

Solutions like Celonis offer powerful analytics to understand not just how processes are supposed to work, but how they actually occur in reality.

Here’s a brief overview of what you can expect:

  • Real-time analysis: Track and monitor your processes as they happen.
  • Discovery: Uncover the actual flow of your processes through visual maps.
  • Conformance: Compare the expected workflow to the real-world data to identify deviations.
  • Enhancement: Receive recommendations on how to improve efficiency and optimize performance.

Case Study: Celonis

Celonis stands out as a leader in the process mining solutions, providing a sophisticated platform that gives you a deep dive into your process efficiencies. With Celonis, you can:

  • Visualize: Get a comprehensive view of your business processes with intuitive heat maps and diagrams.
  • Optimize: Use the insights gathered to streamline operations, reduce bottlenecks, and eliminate inefficiencies.
  • Automate: Implement automation in areas identified for potential improvement to enhance speed and accuracy.

By embracing the capabilities of Celonis, you position yourself to not just analyze but truly transform your business processes for better outcomes.

Example of a dashboard in Celonis

Celonis Process Mining Platform

Future of Process Mining

As you look ahead, consider that the future of process mining is closely intertwined with advancements in technology, particularly in areas such as automation and artificial intelligence. These innovations promise to enhance the efficiency and effectiveness of your business processes.

Trends in Process Automation and AI

Unquestionably, artificial intelligence (AI) is shaping the future of process mining. AI helps in automating complex tasks and making data-driven decisions.

Tools have evolved to not only map processes but also to recommend and automate improvements. This integration of AI into process mining tools is proving to be a powerful combination that can significantly streamline your business operations.

Combining RPA and Process Mining

Robotic Process Automation (RPA) when combined with process mining, transforms your ability to optimize and automate workflows.

RPA can execute repeatable tasks, while process mining provides the insight needed for identifying which tasks are ripe for automation. This symbiosis between RPA and process mining equips you with a method to continuously improve and automate processes across your organization.

Process Mining and Digital Transformation

Digital transformation is the bedrock of modern business strategy, and process mining is a key component.

By providing clear insights into actual process performance, process mining empowers you to make data-informed decisions that align with your digital transformation goals.

Companies are finding that by employing process mining, they’re better poised to tackle the digital challenges of the future and stay ahead of the competition.

A man employing process mining techniques looks out of a window at a city during the night.

What Are The Process Mining Steps?: A Recap

Process mining bridges the gap between traditional process analysis and data-centric insights, providing a clear, objective view of how processes actually run within an organization.

At its core, it’s about analyzing event logs that your IT systems generate during the execution of processes. These logs contain events with timestamps that tell you not just what happened, but precisely when.

Key Takeaways: How Does Mining Processes Work?

  • Data-Driven Insights: Process mining leverages real-time data from event logs to provide an accurate picture of business processes.
  • Visibility and Clarity: It offers unparalleled visibility into the nuances of your processes, revealing bottlenecks, deviations, and opportunities for improvement.
  • Continuous Improvement: The insights gained from process mining facilitate a culture of continuous improvement, where processes are constantly evaluated and refined.
  • Objective Analysis: By relying on data rather than subjective viewpoints, process mining enables a more objective analysis of process performance.
  • Integration and Automation Opportunities: Process mining can identify potential areas for automation and seamless integration between different systems and processes.
A laptop displaying process discovery graphs and a cup of coffee on a wooden table

FAQ: How Process Mining Works?

What are the top process mining tools currently available?

Currently, a variety of process mining tools are utilized across industries, notable ones include QPR ProcessAnalyzer, Celonis, and UiPath. These tools differ in their features and methods of integrating with enterprise systems.

Can you explain the use of process mining software in improving business processes?

Process mining software empowers your business by providing insight into your actual processes. By visualizing and analyzing processes, companies can pinpoint inefficiencies, conformances, and enhance overall performance.

What benefits can organizations expect from implementing process mining?

By adopting process mining, organizations can expect improved employee performance, streamlined workflows, and a comprehensive understanding of process trends, which together foster continuous optimization of business operations.

Could you describe a typical process mining project lifecycle?

A typical process mining project lifecycle begins with data extraction and concludes with monitoring and continual improvement. It involves data collection, process discovery, conformance checking, and enhancement of the processes being analyzed.

What are the key techniques involved in process mining?

Important techniques in process mining include discovering process models from event logs, conformance checking to compare existing processes with intended models, and enhancing process models for increased efficiency and effectiveness.

How do Celonis and UiPath Process Mining differ in their approach to process mining?

Celonis and UiPath Process Mining are both leaders in the field but they differ in their functionalities and user interface. Celonis focuses on full process transparency and a more user-centric design, while UiPath emphasizes integration with its RPA suite for end-to-end process automation.

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Eric J.
Eric J.

Meet Eric, the data "guru" behind Datarundown. When he's not crunching numbers, you can find him running marathons, playing video games, and trying to win the Fantasy Premier League using his predictions model (not going so well).

Eric passionate about helping businesses make sense of their data and turning it into actionable insights. Follow along on Datarundown for all the latest insights and analysis from the data world.