What is Process Mining?
Process Mining is an AI-powered analytical technique that uses event log data from business systems to automatically discover, visualise, and analyse how business processes actually operate. It reveals the difference between how processes are designed to work and how they work in reality, identifying bottlenecks, inefficiencies, and compliance violations.
What is Process Mining?
Process Mining is a data-driven technique that extracts knowledge from event logs recorded in information systems to discover, monitor, and improve real-world business processes. Unlike traditional process mapping, which relies on interviews and workshops to document how people think processes work, process mining shows how processes actually work based on factual data.
Every time an employee creates a purchase order, approves an invoice, updates a customer record, or completes a service request, the business system records a timestamped event. Process mining collects these events, reconstructs the actual process flows, and visualises them, revealing the true path of every transaction through your organisation.
How Process Mining Works
Data Extraction
Process mining starts by extracting event logs from your business systems. An event log typically contains three elements:
- Case ID: A unique identifier for each process instance (e.g., order number, ticket ID, invoice number)
- Activity: What happened (e.g., "Create Purchase Order," "Approve," "Ship")
- Timestamp: When it happened
Most enterprise systems including SAP, Oracle, Salesforce, and ServiceNow generate these event logs automatically.
Process Discovery
Algorithms automatically reconstruct the actual process flow from the event log data. The result is a visual process map showing every path transactions take through your organisation, including the mainstream path and all the variations, loops, and exceptions.
Conformance Checking
Process mining compares the discovered process against the intended process design to identify deviations. This reveals:
- Skipped steps: Activities that should happen but are bypassed
- Rework loops: Activities that are repeated unnecessarily
- Unauthorised paths: Process flows that violate compliance rules
- Bottlenecks: Steps where transactions queue up and processing slows
Performance Analysis
By analysing timestamps, process mining quantifies the time spent on each activity and between activities. This reveals where delays occur, how long each process variant takes, and where resources are over or underutilised.
Root Cause Analysis
Advanced process mining uses AI to identify the factors that cause process deviations, delays, or failures. It can answer questions like: Why do 30 percent of purchase orders require rework? Which customer type generates the most support escalations? What factors predict a late delivery?
Process Mining Use Cases
- Order-to-cash: Analysing the full cycle from order receipt to payment collection, identifying bottlenecks that delay revenue recognition
- Procure-to-pay: Discovering inefficiencies in purchasing, from requisition to invoice payment, and identifying maverick spending
- IT service management: Analysing incident resolution processes to reduce resolution times and improve service levels
- Customer service: Mapping the actual customer journey across touchpoints to identify friction points and improvement opportunities
- Compliance and audit: Automatically monitoring processes for regulatory compliance and generating audit evidence
Process Mining in Southeast Asia
Process mining is gaining traction across ASEAN as businesses pursue operational excellence:
- Digital transformation readiness: As Southeast Asian businesses digitise their operations, process mining provides a data-driven foundation for identifying what to automate and optimise next
- Regulatory compliance: Increasing regulatory scrutiny across ASEAN financial markets makes process mining valuable for demonstrating compliance and identifying violations proactively
- Shared services centres: Many multinational companies operate shared services centres in Malaysia, the Philippines, and Thailand. Process mining helps these centres identify and eliminate inefficiencies across standardised processes
- ERP implementations: As more ASEAN businesses implement or upgrade ERP systems, process mining helps validate that the new systems deliver the expected process improvements
Leading Process Mining Platforms
The process mining market offers several established platforms:
- Celonis: The market leader, offering comprehensive process mining, execution management, and AI-powered recommendations
- Microsoft Power Automate Process Mining: Integrated into the Microsoft ecosystem, accessible for businesses already using Microsoft tools
- SAP Signavio: Deep integration with SAP systems for businesses running SAP ERP
- UiPath Process Mining: Integrated with UiPath's RPA platform, enabling a seamless path from process discovery to automation
Most businesses have a significant gap between how they think processes work and how they actually work. This gap represents hidden costs, unnecessary delays, compliance risks, and frustrated customers. Process mining closes this gap by providing objective, data-driven visibility into operational reality.
For CEOs, process mining answers critical questions that traditional approaches cannot: Where is our money being wasted? Why do some transactions take days while others take hours? Are we complying with regulations consistently? Where should we invest in automation for the highest return? These insights drive better resource allocation, faster improvements, and more confident decision-making.
For CTOs, process mining is a strategic enabler for digital transformation. It provides the analytical foundation for deciding what to automate, which systems to integrate, and where to focus improvement efforts. Rather than guessing which processes to improve, process mining uses data to prioritise initiatives by their potential business impact. In ASEAN markets where businesses are investing heavily in digital transformation, process mining ensures those investments are directed where they will deliver the greatest value.
- Start with a process that is well-documented in your systems and has high business impact, such as order-to-cash or procure-to-pay. These processes generate rich event logs and offer clear improvement opportunities.
- Ensure your business systems capture sufficient event log data. Process mining requires at least a case identifier, activity name, and timestamp for each event.
- Involve process owners early. Process mining often reveals uncomfortable truths about how processes actually operate. Engage stakeholders who can act on the findings.
- Combine process mining with automation. Use process mining insights to identify the best candidates for RPA or workflow automation, then use ongoing process mining to measure the impact.
- Plan for continuous monitoring, not just one-time analysis. The greatest value comes from ongoing process monitoring that detects issues and drift in real time.
- Budget for action, not just analysis. Process mining insights are valuable only if the organisation acts on them. Allocate resources for implementing the improvements that mining reveals.
Frequently Asked Questions
How is process mining different from traditional business process mapping?
Traditional process mapping relies on interviews, workshops, and manual observation to document how processes are supposed to work. Process mining uses actual data from your IT systems to show how processes really work. The difference is often significant: process mining frequently reveals process variants, bottlenecks, and compliance violations that were unknown to the organisation. It is objective, comprehensive, and based on every transaction rather than a sample.
What systems do we need to have in place for process mining?
Process mining requires systems that generate digital event logs with at minimum a case ID, activity description, and timestamp. Most modern business systems including ERP (SAP, Oracle, Microsoft Dynamics), CRM (Salesforce, HubSpot), ticketing systems (ServiceNow, Jira), and even spreadsheet-based workflows can provide this data. The key requirement is that activities are recorded digitally rather than performed entirely manually without system interaction.
More Questions
An initial process mining analysis can deliver actionable insights within 2 to 6 weeks, depending on data availability and process complexity. The first phase involves data extraction and preparation (1-2 weeks), process discovery and analysis (1-2 weeks), and findings presentation (1 week). Ongoing process monitoring can be established within 1 to 2 months. The fastest results come from processes with clean, readily accessible event log data.
Need help implementing Process Mining?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how process mining fits into your AI roadmap.