Process Mining: How to Analyze Real Processes and Find Optimization Opportunities

Even a perfectly designed procedure inevitably collides with operational reality. A contract approval workflow might be defined to take two days, yet in practice the cycle stretches into weeks. IT support tickets can bounce between first- and second-line teams for days, dragging down overall efficiency and frustrating users.

When a company tries to pin down the cause of delays like these, the traditional approach falls to business analysts: interviews and manual process mapping, often in BPMN notation. The catch is that this method captures how employees think they work — or how they're supposed to work — not how the process actually plays out.

Getting an objective picture means grounding the analysis in facts, and that's exactly what process analytics, or Process Mining, delivers. This article looks at how the technology reveals what's really happening inside a company using nothing but digital data, and how modern Process Mining tools turn those insights into measurable savings in time and resources.

What Is Process Mining

Any modern corporate IT system logs user actions: who created a ticket, when its status changed, who approved a document, and when the cycle closed. A Process Mining platform pulls these scattered digital traces together and uses mathematical algorithms to build a clear, visual map of the business process.

The result isn't an idealized process diagram — it's a factual model of execution, deviations, redundant approval loops, and all the steps that get sent back for rework.

How Process Mining Works: From Event Logs to a Digital Model

System data is the foundation of the analysis. For the algorithms to work correctly, three parameters need to be extracted from the IT applications — the event log:

1. Case ID: a unique identifier for the object whose lifecycle is being tracked (for example, an incident number in the service desk: INC000123).
2. Activity: the specific action taken (for example, status changed, assignee set, approval completed).
3. Timestamp: the exact time the server recorded the event.

Working from this data, a Process Mining solution reconstructs the timeline, stitching scattered events into a single end-to-end path for a customer or a document.

Deeper analysis draws on additional attributes: department, assignee role, contract value, or ticket category. This makes it possible to test specific hypotheses — for example, whether requests from the finance department really do take longer to process than requests from sales.

What Problems Process Mining Solves

Adopting process analytics is aimed at solving concrete business problems and lifting operational efficiency:

• finding bottlenecks: visualizing the process clearly shows the stages where work piles up and critical delays happen;
• compliance checking: the system automatically compares the actual process flow against the approved target model, revealing how often employees skip mandatory controls or break the required sequence of statuses;
• root cause analysis: the tooling helps identify what's driving missed deadlines. Process Mining can surface correlations — for example, that delays consistently show up at a particular branch or with a specific request category;
• assessing automation potential: before rolling out RPA robots or low-code automation, Process Mining shows exactly which parts of a process are worth automating for the strongest return on investment.

Process Mining vs. Traditional Analysis: What's the Difference

Process optimization has historically leaned on interviews, time-and-motion studies, and manual data collection. Here's why a technology-driven approach delivers more accuracy.

Where Process Mining Is Used: ESM and Beyond

The technology applies to any area where processes leave a digital trail, but it delivers the most value in end-to-end corporate processes — particularly within the ESM (Enterprise Service Management) concept.

• IT support (ITSM): a detailed look at incident management — tracking down the causes of chronic SLA breaches and spotting cases where tickets get bounced excessively between support lines;
• HR: evaluating recruitment (from application to offer) and onboarding, and pinpointing the stages where candidate processing stalls;
• procurement (P2P — Procure-to-Pay): finding the causes of drawn-out contract approvals and catching purchases that bypassed standard approval procedures;
• finance (O2C — Order-to-Cash): analyzing the cycle from order creation through payment receipt, and streamlining accounts-receivable management.

Why Process Mining Needs a Unified Platform

The biggest obstacle to adopting a classic, external Process Mining tool is data collection and normalization. When an end-to-end process is spread across an ERP system, an email client, a task-management tool, and legacy portals, analysts can spend months extracting, cleaning, and reconciling logs into a single format. The garbage-in, garbage-out principle is unforgiving here.

That's why a clear trend has emerged in the enterprise sector: Process Mining delivers the most value when the analysis tools are built into the same platform that runs the business processes. If an organization uses a single low-code platform — SimpleOne, for example — to automate IT, HR, and adjacent services, the data is already structured, stored in a shared data model, and protected by a common access-control model.

That removes the need to build complex extraction pipelines (ETL). Analytics can run directly on top of clean internal transactions.

Process Mining Office for SimpleOne: A Practical Tool for Analyzing Service Processes

Addressing the market's demand for built-in analytics, SimpleOne's technology partner, Media-Tel Group, developed a dedicated solution — Process Mining Office (PMO). It's available through the official SimpleOne Partner Marketplace.

This isn't an external analytics system that requires complex integration. Process Mining Office for SimpleOne (PMO4S1) is an intelligent tool that deploys directly inside the SimpleOne platform — on-premises or in the cloud — and starts working with historical data right away. It helps teams analyze, optimize, and automate service processes across ITSM, ESM, and HR, using data on tickets, their routing, and their resolution to raise service quality.

Instead of static reports and fixed metrics, you get a dynamic visualization of real workflows — bottlenecks surface on their own, efficiency becomes measurable, and improvement opportunities stand out.

PMO Functional Modules: What's Available to Analysts

Process Mining Office for SimpleOne (PMO4S1) covers the following business needs:

• process control: the system automatically reconstructs the actual process map, showing exactly where tickets get stuck, how much real time gets spent handing tasks between teams, and where "ping-pong" reassignment loops crop up;
• fewer SLA breaches: the module doesn't just flag missed deadlines after the fact — it predicts SLA risk ahead of time and identifies ways to speed up work on critical incidents based on historical patterns;
• finding automation opportunities: ease the load on first- and second-line staff by spotting routine, repetitive steps worth automating with SimpleOne's low-code tools;
• in-depth change analytics: the Process Modeling tool lets you load a reference process and compare performance before and after improvements, so you can measure the real — not assumed — impact of a change and identify what's actually working;
• smarter prioritization: the system reduces the human-error factor in ticket prioritization by analyzing real data on how tickets are handled and routed.

The real value of PMO lies in turning Process Mining from a one-off consulting or audit exercise into an everyday operational tool for CSI managers and process owners.

When It's Time to Adopt Process Mining

Dedicated analytics tools don't pay off at small data volumes — say, 50 tickets a day handled by a small team. Adopting a Process Mining platform makes sense once:

• transaction volume is high (thousands of transactions per month);
• a process is formally documented, but the metrics show it running slowly;
• there's a large volume of manual work and plans for large-scale automation — and you need to know where to start for maximum impact;
• SLA targets are missed repeatedly without an obvious, clear-cut cause.

How to Roll Out Process Mining in Your Company

Implementation calls for a structured approach to data and clear goal-setting.

1. Define the business goal. Analysis for its own sake delivers nothing. You need a specific target — for example, cutting standard contract approval time from 14 days to 7.
2. Choose a system and prepare the data. On a platform-based setup like SimpleOne, this step is minimal, since the logs are already structured. With data scattered across separate systems, you'll need to extract, normalize, and clean the event logs first.
3. Build the actual process map (Discovery). Construct the graph of the real process. The actual picture — full of workarounds and rework loops — is often an eye-opener for management.
4. Run a Root Cause Analysis. Investigate what's driving the deviations: missing instructions, routing errors in the system, or integration issues.
5. Roll out changes and set up monitoring. Optimize the process — update the procedure, configure low-code automation — then keep monitoring through PMO to confirm the changes stick.

Conclusion: Process Mining as a Path to Digital Maturity

Effective process optimization depends on access to objective data. Process Mining gives management the tools to assess how operations really run.

Moving from one-off consulting snapshots to built-in analytics modules — like Process Mining Office for SimpleOne — makes this kind of analysis part of everyday management. IT and business service owners can not only spot where time is being lost, but fix it quickly, backed by reliable data from their own ITSM/ESM platform. In today's corporate landscape, the ability to spot and eliminate process inefficiency quickly is a genuine competitive edge.