What Is Process Mining and How It Turns Event Logs into Better Business Processes

If you ask three managers to describe your purchase-to-pay process, you’ll likely get three completely different answers. Your ERP system, however, may give you a fourth version, the one backed by real event timestamps. Process mining analyzes those digital footprints to reconstruct exactly how work flows through your company, step by step, revealing what is happening on the ground versus what people think is happening.

Today, many companies are putting serious money behind process mining because it works. Valued at $5.45 billion in 2026, the global process mining market is on track to surge to $58.18 billion by 2034, driven by a relentless 34.40% annual growth rate as companies race to turn operational data into automated, friction-free workflows. Most importantly, companies are moving beyond small-scale pilots: 48% of organizations have deployed process mining company-wide, and 80% say it delivers tangible value.

AI is moving process mining to the top of the executive agenda. A 2025 Celonis study of more than 1,600 business leaders found that 89% believe AI needs to understand how a company works before it can deliver any real value. Without good process data, AI and automation are basically working on guesswork.

In this guide, we’ll break down how process mining works and where it pays off across your business operations. You’ll also explore how it supports data-driven decision-making and why the process mining market continues to grow.

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What Is Process Mining and Why Business Process Management Teams Use It Now

Every time an employee creates an order, approves an invoice, or closes a support ticket, your software records a digital breadcrumb. Process mining is a data analysis method that collects those scattered tracks and transforms them into an interactive visual map of real work.

The raw material behind this process is event log data extracted from core platforms like enterprise resource planning systems, customer relationship management platforms, and IT service desks. To build this visual map, process mining requires only three essential data points per action: a case ID, the name of an activity, and an exact timestamp.

Process mining provides you with an objective, fact-based view based on system logs that is simply not possible with traditional interviews and manual workshops. Surveys are based on human memory and bias. Process mining focuses on what your software has captured.

Today, software vendors package these capabilities into broader process intelligence solutions, layering AI and rich business context over the underlying map. This evolution is being described by tech leaders like Celonis as a living “digital twin” of business operations, enabled by modern tools such as object-centric process mining and process graphs.

For business process management (BPM) teams, this move transforms daily operations. You stop debating how existing processes should work and start measuring how they do work. With over 80% of organizations planning to deploy AI across their operations, business leaders increasingly consider process mining important groundwork for AI. A reliable process intelligence platform feeds AI systems the context they need to deliver actual results.

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How Process Mining Works from Event Logs to Process Analysis

Every process mining work cycle follows the same logic: collect what your systems recorded, organize it, and compare it with what you expected. The steps below show how raw entries become a map you can query. Most of the effort sits in the first two steps, where data gets connected and cleaned.

1. Connect: Read-only connectors pull system event logs and related system data from ERP, CRM, and ITSM tools
2. Build the event log: The process mining method converts table rows from enterprise systems into event logs, with one row per activity
3. Discover: Algorithms assemble process flows from the actual data, showing every path each case took
4. Analyze: You filter variants, measure wait times, and compare results with the documented process description
5. Monitor: Dashboards refresh as new event data arrives, so you can track the effect of each change

Here is what three rows of log data look like for an invoice process (illustrative sample):

Gartner treats this pipeline as the baseline for any serious platform. Its 2025 Magic Quadrant lists event log extraction, process model discovery and analysis, comparative process mining, and model enhancement as mandatory features. The result is a process model grounded in a data-driven approach.

Three Core Techniques Behind Process Mining

Both academic experts and industry practitioners group process mining techniques into three distinct families, forming a natural progression that takes you from total visibility to targeted action. Most organizations move through all three phases sequentially. It’s a logical journey: you first need to see the real workflow, then check if it obeys the rules, and only then optimize it for maximum efficiency.

Process discovery

It takes an event log with no prior model and produces a diagram of how cases moved. You see the main path, every rework loop, and the rare variants nobody documented. Discovery often surprises teams by revealing dozens of paths where they expected one.

Let’s say you have a 4-step sales order process. Discovery can find 60 variants, including credit checks that are done three times for the same customer.

Conformance checking

It compares recorded cases against an intended process model, such as a policy requiring two approvals above $10,000. The software flags every case that skipped a step, ran steps out of order, or involved the wrong role. Internal audit teams use this to test every transaction.

The results double as audit trails, since each deviation links back to its original record. You can score each case against an existing process model based on your approval matrix.

Process enhancement

Process enhancement extends an existing process model with extra data, such as durations, costs, or resource names. The map stops being a static picture and starts showing process performance at every step. This is where you find the handoffs that add days and the teams carrying uneven workloads.

Advanced platforms add predictive process monitoring, which uses predictive analytics to flag cases likely to miss deadlines. Teams then tie these findings to key performance indicators and resource allocation decisions.

Types of Process Mining and the Technologies Behind Them

Process mining types differ mainly in how they structure data and where that data comes from. Some tools follow one case at a time, while others track several linked objects. A second split separates system-level logs from desktop activity recorded on user screens.

Traditional and object-centric process mining

Traditional process mining assigns every event to a single case ID, such as one purchase order. That approach works well for simple, linear flows. It struggles when one order splits into three deliveries and two invoices, because forcing those objects into one case distorts the picture.

Object-centric process mining (OCPM) tracks orders, items, deliveries, and invoices together, along with the links between them. Gartner names OCPM among the five directions shaping the platform market. It suits complex processes in supply chain and manufacturing.

Task mining, data mining, and AI in modern tools

Task mining records clicks, keystrokes, and screen activity on employee desktops to show what happens inside a single step. Process mining sees that an invoice waited two days. Task mining shows the clerk copying values between four windows. Together, they give you both the system view and the human view.

Both build on data mining, the broader discipline of finding patterns in large datasets. Modern process mining tools also add generative AI, so you can ask root-cause questions in plain English. AI adoption inside these tools is still early, though.

Process Mining Use Cases Across Business Operations

You can use process mining on any process that leaves a digital trail with case IDs and timestamps. Deloitte’s 2025 survey shows that procurement and accounting remain core applications, while supply chain, IT operations, and customer service are gaining ground. The table maps nine common operational processes.

Across all nine, the goal stays the same: identify inefficiencies fast, then monitor processes after each fix. Ongoing process monitoring confirms that improvements hold as new cases arrive.

Benefits of Process Mining for Process Improvement and Customer Satisfaction

Process mining benefits come from replacing opinion with evidence. Deloitte’s respondents ranked process improvements (61%) and cost savings (59%) as top expectations, followed by transparency, monitoring, and finding automation opportunities. Here is how those expectations translate into results you can measure.

• Find where work piles up: Wait-time analysis shows which handoffs hold cases longest, so you improve processes where delays cost most
• Discover hidden inefficiencies: Rework loops, duplicate entries, and manual workarounds surface automatically
• Reduce operational costs: You optimize processes around the paths that cost less and run faster
• Improve process visibility: Every team works from the same map, built from the same records
• Detect deviations and compliance issues: Policy breaches appear as cases run, months before a year-end audit
• Support data-driven decisions: Process insights replace anecdotes in budget and staffing discussions
• Optimize automation opportunities: Frequency and variant data show which steps to automate first

These gains compound into continuous process improvement. Deloitte also found that 28% of respondents now expect process mining to raise customer satisfaction, which shows the method reaches beyond back-office efficiency. Teams that optimize business processes this way build a habit of continuous improvement and a stronger process improvement practice.

The Difference Between Process Mining and Related Process Improvement Methods

The difference comes down to the data source and the question each method answers. Workshops and interviews capture what people believe happens. BI dashboards count outcomes such as revenue or ticket volume. Process mining reconstructs the sequence of events behind those outcomes, which is why teams often combine several methods.

Put simply, process mining reveals what BPM models assume. Good process analysis pairs both, using the map to test and refine the design.

Key Challenges of Process Mining and How to Handle Them

Process mining delivers its best results long before the first map ever appears on screen, which means most obstacles lie in the preparation. Here are the five places where projects most often lose momentum, along with practical ways to navigate around each one.

Data quality

Your process map can only be as reliable as the event log behind it. Missing timestamps, duplicate records, and inconsistent activity names all distort the picture. A field renamed during a system update can make one approval step look like two separate activities, which inflates variant counts and hides the real flow.

You need to run a data profiling sprint before analysis starts. Agree on one naming standard per activity, and document every transformation so analysts can trace each number back to its source.

System integration

Most processes cross several systems, and each one stores events in its own format. An order may start in a CRM, move through an ERP, and finish in a warehouse tool. Linking those records requires a shared key, such as an order number, that every system carries.

You need to budget engineering time for connectors, API access, and security reviews before you sign a license. If you run Odoo, keep in mind that external API access requires the Custom pricing plan.

Process complexity

Real operations rarely follow one tidy path from start to finish. Discovery can return hundreds of variants, and a map showing every connection at once becomes unreadable for most business users. Complexity grows further when one order splits into several shipments and invoices handled by different departments.

You can start with the variants that cover most of your case volume, then add exceptions in layers. Object-centric models help when several documents move through one flow.

Change management

Process mining exposes how teams work, and that visibility can feel uncomfortable. Employees may read a map of their handoffs as a performance review. Leaders may question findings that contradict long-held assumptions. Visible sponsorship and open communication keep adoption going well past the pilot.

It’s worth sharing findings as team-level insights and involving process owners in every review session.

Turning insights into measurable improvements

A process map delivers value only when someone changes the process. Many programs produce attractive dashboards and then pause, because nobody owns the follow-up work. Each finding needs an owner, a target KPI, a deadline, and a budget for the fix itself.

You should measure a baseline before you change anything, then compare the same metric 60 and 90 days later.

How Process Mining Drives Process Automation and Digital Transformation

Insight only pays off when it leads to action. Once process mining exposes your actual workflow, it highlights the repetitive, rule-based steps that slow people down. Those steps are prime candidates for process automation. And the story doesn’t end when a fix goes live: those same event logs continuously track each automated process to prove it is performing as planned.

Choosing the right technology depends on where the friction lives:

• Robotic process automation (RPA) is excellent for repetitive, screen-based work, even on legacy applications that don’t have clean APIs
• ERP-native workflows automate standard approvals and notifications right in your core systems (e.g., building custom approval rules in platforms like Odoo)
• AI agents step in to handle more complex steps that require context, reasoning, and adaptive decision-making

Match the right tool to the right bottleneck, and you’ll turn raw process data into measurable operational impact.

Glorium Technologies applied this pattern when working with a US industrial parts distributor stocking more than 30,000 SKUs across three regional warehouses. The discovery phase mapped its source-to-pay and order-to-ship flows, where purchase approvals ran through email without enforced spend limits. After Approval Rules went live, sign-offs that once took two to three days were completed within one shift, a 52% shorter cycle. In another logistics project, warehouse and dispatch cycles shrank by about 30%. Each step like this moves your digital transformation from slide decks to measured change.

Process Mining Platforms and What to Look For

The process mining software market is crowded with competition between pure-play specialists, ERP vendors, and automation suites. Gartner assessed 16 vendors in its 2025 Magic Quadrant for the category. The right choice depends on your systems, data volume, and team skills more than on brand.

When comparing process mining tools or complete process mining solutions, consider five points:

• Connectors: Prebuilt extractors for your ERP, CRM, and ITSM systems
• Object-centric support: Native OCPM for flows with multiple related objects
• Action layer: Process intelligence capabilities that trigger alerts, tasks, or automations
• Total cost: Licenses, data engineering, and internal analyst time
• Usability: Business users can work with maps without writing SQL

Common Misconceptions About Process Mining

A few persistent myths keep teams from starting or lead them to stop too early. Each one has a simple correction based on how successful programs run. Deloitte found that 41% of respondents cite a lack of management attention as a barrier, so correcting these myths helps you win sponsorship.

• “It’s BI with a new name”: BI totals outcomes, while process mining traces the sequence that produced them
• “Our data must be perfect first”: Most projects start with imperfect logs and fix gaps, like disconnected order and invoice IDs, along the way
• “It’s a one-time project”: Value grows when the map stays live and tracks each change

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Turn Process Insights into Results with Glorium Technologies

Process mining gives you a factual map of how work moves, where it slows, and which steps deserve automation first. The value grows when you treat that map as a living tool that guides each improvement cycle.

With over 16 years of hands-on software delivery experience, Glorium Technologies knows how to solve inefficiencies. Our team seamlessly turns process mining findings into fully working automation. Whether your workflows require native ERP optimization or intelligent AI-driven execution, we build the modern digital architecture your operations need to scale effortlessly.

Book a free process audit to see where your processes lose time and money.

FAQ

Can process mining work with Odoo or Dynamics 365 Business Central data?

Both systems store timestamped records that can feed an event log. Odoo logs tracked field changes in each record’s chatter, and its external API, available on the Custom plan, supports extraction. Business Central exposes data through its own APIs. Glorium Technologies, a certified Odoo partner and Microsoft partner, builds these data pipelines for clients.

Is process mining worth it for a mid-sized company?

It usually is when you run high transaction volumes through digital systems and lose time on a known process, such as procure-to-pay. Start with one process and one defined KPI. A focused pilot lets you prove value before you commit to a wider rollout or a multi-year license.

How do you protect employee and customer privacy during process mining?

First, collect only the fields you need for your analysis, and pseudonymize user and customer IDs before the data lands on the platform. Access to process maps is role-based; resource names may expose individual performance. Task mining needs extra care because it records desktop activity, so involve your legal and HR teams before any rollout.

How does Glorium Technologies start a process mining engagement?

Glorium Technologies starts with a free process audit. The team maps your current workflows, identifies manual steps, and puts a dollar figure on what that manual work costs you. A paid diagnostic and discovery phase follows, producing as-is and to-be maps, an exception matrix, and an integration plan before any build begins.