Process Mining is no longer just a niche research field; it’s a strategic tool for organisations looking to gain real-time visibility, enhance compliance, and unlock efficiency across complex workflows.
Process mining is more than a business intelligence tool; it’s the future of process improvement. By analysing event logs from business systems, process mining enables teams to discover, monitor, and improve real operational processes, how things are actually done, rather than how they are supposed to work.
At a recent Celonis Process Intelligence Day in Zurich, a colleague (I missed his name) said something along the lines of:
Prove things you already know – make it visible
However, simply adopting a process mining tool doesn’t guarantee results. Success depends on how well the organisation can integrate technology, expertise, and change management. This post outlines the Process mining Success Factors you need to get right.
Contents
- 1 What Are Critical Success Factors?
- 2 Success Factor: Stakeholder Support and Involvement
- 3 Success Factor: Data Availability
- 4 Success Factor: Technical Expertise
- 5 Success Factor: Structured Process Mining Approach
- 6 Success Factor: Operating Model & Organisational Structure
- 7 Success Factor: Tool Capabilities
- 8 Success is Holistic
What Are Critical Success Factors?
Critical success factors are the few essential areas where strong performance ensures favourable outcomes. In the context of process mining, they go beyond tools and dashboards. They define the organisational conditions that turn analysis into action.
Success Factor: Stakeholder Support and Involvement
Process mining success hinges on early and continuous engagement from key stakeholders, including executive sponsors, process owners, domain experts, and frontline users.
Why it matters:
- Focus: Stakeholders help define the right questions, ensuring analysis is business-relevant.
- Validation: Domain experts provide context to interpret findings accurately.
- Adoption: Sponsors and managers turn insights into action and secure buy-in across the organisation.
Key roles to involve:
- Executive Sponsor – Provides legitimacy and resources
- Process Owner – Ensures relevance and drives implementation
- Domain Expert – Interprets data and refines hypotheses
- IT/Data Owner – Supports access and quality of event logs
💡 Pro Tip:
Involve stakeholders in storytelling. After your analysis, bring stakeholders into the communication loop. Let them help shape the narrative behind the data. This not only builds credibility but accelerates internal buy-in.
Success Factor: Data Availability
Process mining is only as strong as the data behind it. This means having access to complete, well-structured event logs from core systems, along with relevant process documentation, such as business rules and compliance requirements.
Why it matters:
- Foundation: Without event data (timestamps, case IDs, activities), no process model can be built.
- Context: Supporting documentation adds meaning and helps interpret results accurately.
- Feasibility: Incomplete or siloed data often stalls projects before they start.
Key enablers:
- IT & System Admins – Extract and structure event logs
- Process Owners – Identify where the relevant data lives
- Business Analysts – Link data fields to process logic
Poor data access is one of the most common blockers in early-stage initiatives.
💡 Pro Tip:
Don’t wait for “perfect” data, start with what’s available and refine iteratively. Early wins build credibility and create momentum for better data access later.
Success Factor: Technical Expertise
Process mining demands a blend of data wrangling, analysis, scripting, and process modelling skills. It’s not just about using tools. Expertise must be domain-specific to extract the insights that drive change.
Why it matters:
- Execution: Technical experts extract and transform raw data into structured event logs.
- Insight: They design and interpret process models that reveal bottlenecks and inefficiencies.
- Enablement: Strong technical foundations allow the team to troubleshoot quickly and scale efforts efficiently.
Key roles:
- Data Engineers – Handle data extraction, merging, and preparation (SQL, Python, ETL tools)
- Process Analysts – Build models, run diagnostics, and translate findings into recommendations
- Tool Specialists – Maximise the value of your chosen platform
Without the right skills, even the best tools become shelfware.
💡 Pro Tip:
Invest early in upskilling internal teams or bring in expert support for the first projects, and you’ll accelerate value and build internal capability at the same time.
Success Factor: Structured Process Mining Approach
A formal methodology helps teams stay focused, aligned, and efficient across phases.
Why it matters:
- Clarity: Translates broad ambitions into defined, measurable steps
- Consistency: Ensures repeatability across teams, systems, and projects
- Value Realisation: Keeps the effort grounded in business outcomes, not technical curiosity
Common frameworks:
- Discovery → Conformance → Enhancement
- Lifecycle-based models (e.g., PM lifecycle)
- Vendor-specific methods (like Celonis MTP or QPR frameworks)
Ad hoc approaches lead to fragmented insights and stalled adoption.
💡 Pro Tip:
Map your PM approach to business priorities, not just data availability. Focus on prioritised value generation.
Success Factor: Operating Model & Organisational Structure
Even the best analysis is wasted if there’s no pathway to action. Process mining requires an operating model that connects insights to decision-makers and empowers them to take action.
Why it matters:
- Accountability: Clear roles ensure someone owns the insights and drives follow-up
- Cadence: Regular review cycles prevent analysis from gathering dust
- Impact: Empowered teams can implement changes, not just observe problems
Key enablers:
- Process Mining Lead – Owns delivery, sets cadence, links to business priorities
- Embedded Analysts – Translate findings into operational context
- Decision Gatekeepers – Have the authority to act on what the data shows
Without the right structure, PM becomes an expensive diagnostics lab: interesting but ineffective.
💡 Pro Tip:
Establish a recurring process mining rhythm, conduct monthly insight reviews, and hold quarterly impact sprints. Tie findings to action and track what changes as a result.
Success Factor: Tool Capabilities
Not all process mining tools are created equal, and not every feature is worth paying for. Select platforms that align with your business objectives, data landscape, and maturity level.
Check out the Gartner 2025 review of Process mining tech and this helpful overview.
Why it matters:
- Fit-for-purpose: Early-stage teams may only require process discovery, rather than AI-driven automation or advanced conformance checks.
- Scalability: Enterprise rollouts require robust performance on large datasets and strong integration options.
- Usability: Features mean nothing if users can’t access or interpret them.
What to look for:
- Automated process discovery – core capability for all teams
- Benchmarking & conformance checking – useful once maturity increases
- Extensibility – APIs, connectors, and integration with BI or workflow tools
- Scalability – the ability to handle enterprise volumes without lag
Overbuying on features you’re not ready to use is a common and expensive mistake.
💡 Pro Tip:
Start with the 20% of functionality that delivers 80% of value. Expand tool usage as your organisation builds capability and trust in the insights.
Success is Holistic
Success in process mining doesn’t come from buying the right tool. It comes from aligning technology, talent, data, and change leadership.
If you’re just starting, focus on building:
- Technical expertise
- Stakeholder alignment
- Strong data foundations
Treat process mining as a long-term capability. The payoff is better visibility, faster decisions, and operational resilience.
💡 Interested in how process mining could accelerate performance in your organisation? Let’s connect and explore tailored use cases.