A Strategic Guide for Data-Driven Process Improvement
A comprehensive guide to leveraging data for effective process improvement and business transformation.
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This guide explains how process mining and automation (RPA) relate to each other, where process mining genuinely helps find automation opportunities, and why treating process mining as just an automation scouting tool misses most of its value.
The Automation Hype and the Reality Check
Search for “process mining and RPA” and you will find dozens of vendor pages telling you that process mining is the perfect starting point for your automation journey. The pitch sounds compelling: use process mining to discover your processes, find the repetitive tasks, and hand them off to bots.
It is a neat story. It is also incomplete and a bit misleading.
In reality, most obvious automation candidates are already obvious. The finance team knows they are copying invoice data between systems. The support team knows they are manually routing the same ticket types over and over. You rarely need a full process mining deployment to spot those.
So where does process mining actually help with automation? And what else does it bring to the table? Let’s be honest about that.
A Quick Primer: RPA and Process Mining
Before we go deeper, let’s make sure we are on the same page.
Robotic Process Automation (RPA)
Software bots that mimic human actions in user interfaces: clicking buttons, copying fields, filling forms, moving data between apps.
- What it does: Automates repetitive, rule-based tasks at the UI level
- Input: A defined set of steps and rules
- Strength: Works across systems without deep integration
- Limitation: Cannot handle ambiguity or undefined scenarios
Process Mining
Uses event log data from IT systems to reconstruct how work actually flows. Shows the real process, not the assumed process.
- What it does: Discovers, visualizes, monitors, and analyzes real business processes
- Input: Event logs with case IDs, activities, and timestamps
- Strength: Reveals the full picture, including exceptions, bottlenecks, and workarounds
- Limitation: Requires access to structured event data
For a deeper introduction, see our guide on what is process mining .
Where Process Mining Genuinely Helps with Automation
Let’s be fair: there are real scenarios where process mining adds value to automation efforts.
- Uncovering hidden repetition — Some repetitive patterns span multiple teams or systems. Process mining can reveal that the same data gets entered three times across departments, or that a particular handoff always triggers manual rework. Those are automation candidates you would not find from a single team’s perspective.
- Quantifying the business case — Even when you suspect a task is automatable, process mining gives you the numbers: how often, how long, what it costs per case. That data turns a vague “we should automate this” into a concrete ROI calculation.
- Validating automation after deployment — What happens after the bot goes live? Process mining lets you monitor automated processes continuously. You can see if the bot is performing as expected, whether it introduced new bottlenecks, and how the overall process changed.
- Identifying where NOT to automate — Process mining might show that a task you thought was a good automation candidate has too many exceptions, involves too much judgment, or happens too rarely to justify the investment. Knowing where not to spend your automation budget is just as important as knowing where to spend it.
Think Bigger
If you deploy process mining only to find automation tasks, you are buying a Swiss Army knife to use as a toothpick. Process mining delivers value across the entire lifecycle: discovery, conformance checking, monitoring, and optimization. Automation scouting is just one small part.
Be Honest: You Don’t Always Need Process Mining for Automation
Here is the uncomfortable truth that most vendor blogs won’t tell you: if all you need is a list of tasks to automate, process mining might be overkill.
Process mining is a meaningful investment. It requires data extraction, data preparation, organizational buy-in, and skilled interpretation. Getting value from it takes effort, time, and commitment.
If your automation targets are straightforward, a few workshops with the teams doing the work will give you a perfectly good shortlist. Save the process mining investment for when you are ready to do more.
Don’t use process mining for obvious automations. If the accounts payable team already knows they spend four hours a day copying data from emails into the ERP, just automate that. You do not need event log analysis to confirm what everyone already knows.
Where process mining shines is when the picture is more complex: when there are multiple process variants, unexpected bottlenecks, compliance gaps, or when you want to understand the full end-to-end impact of a change.
Process Mining Does Much More Than Find Automation Targets
This is the key point that gets lost in the “process mining for RPA” narrative. Automation discovery is a minor side benefit of process mining, not its purpose.
- Continuous process monitoring — Keep monitoring after mining. Are cycle times improving? Are deviations increasing? Is that new policy actually being followed? Process mining gives you a living dashboard of how work really flows. See our guide on continuous process monitoring .
- Conformance checking — Compare how your process actually runs against how it should run. This is critical for compliance, auditing, and governance. No amount of RPA bots will fix a process that is fundamentally broken.
- Root cause analysis — Why are 30% of orders getting delayed? Process mining can correlate delays with specific suppliers, regions, teams, or process variants. That insight usually leads to process redesign rather than automation.
- Process improvement and redesign — The biggest wins almost always come from changing the process itself, not from automating a broken one. Process mining shows you where and how to simplify, eliminate steps, reduce handoffs, and remove unnecessary approvals.
- Process simulation — With ProcessMind you can model changes and simulate their impact before implementing them. That is far more powerful than blindly automating the status quo.
Automation should be the last resort, not the first response. Redesign the process first. Then automate what remains.
Busting the Myths
There are several myths floating around about process mining and automation. Let’s address them directly.
Myth 1: “Process mining will output an automation plan”
No, it won’t. Process mining shows you how a process works. It highlights inefficiencies, bottlenecks, and deviations. Interpreting those findings and deciding what to automate, what to redesign, and what to leave alone is a human judgment call. Any vendor promising a magic “automation opportunities” button is oversimplifying.
Myth 2: “Process mining is great for mining RPA processes”
Using process mining to analyze already-automated RPA processes sounds logical but is rarely useful. Bots do exactly what they are programmed to do. There is no mystery to uncover. The bot follows its script. If the script is wrong, you already know because the bot fails. Process mining adds value analyzing the broader process that surrounds the bot, not the bot itself.
Myth 3: “You need process mining before any automation project”
Not always. For simple, well-understood automation targets, go ahead and build the bot. Process mining adds the most value for complex, cross-functional processes where you cannot see the full picture from any single team’s vantage point.
Myth 4: “Automation and process mining need the same data access”
They don’t. RPA works at the UI level and often only needs screen access. Process mining needs structured event log data from backend systems. RPA is surface-level; process mining connects to databases and data warehouses. They require different skills, different access, and often different teams.
Planning for both RPA and process mining simultaneously means coordinating across these boundaries: different mindsets (“What can we script?” vs. “What is actually happening?”), different owners (automation CoE vs. process excellence team), and different infrastructure.
Vendors: All-in-One vs. Best-of-Breed
Some vendors offer both RPA and process mining in one platform. UiPath, for example, started in RPA and added process mining later. Others, like Celonis, come from process mining and partner with automation vendors. There are trade-offs to both approaches.
All-in-One Platforms
Advantages:- Tight integration between mining insights and automation workflows
- Single vendor relationship and contract
- Unified user interface
Disadvantages:
- Automation-first bias: the tool steers you toward automation, even when process redesign would be better
- Vendor lock-in: switching either component means switching everything
- “Good enough” in each area, rarely best-in-class in both
Best-of-Breed Approach
Advantages:- Pick the strongest tool for each job
- Flexibility to swap components as needs evolve
- No bias toward one type of solution
Disadvantages:
- Integration requires more effort
- Multiple vendor relationships to manage
For a detailed comparison, see our UiPath vs. ProcessMind analysis .
For most organizations, we think flexibility matters. Process mining and automation serve different purposes. Locking yourself into a single vendor’s ecosystem because it is convenient in year one can become a constraint in year three when your needs shift. ProcessMind is built to work with any automation stack, so your process intelligence is never tied to a specific RPA vendor.
A Practical Approach: Process Mining + Automation Done Right
If you do want to use process mining to support your automation strategy, here is a practical approach that avoids the common traps.
1. Understand
Mine your processes first. Look for bottlenecks, compliance issues, rework loops, and unnecessary handoffs.
2. Improve First
For every issue, ask: “Can we fix this by changing the process?” Eliminating steps is cheaper than building bots.
3. Then Automate
After improving, automate what remains: the repetitive, rule-based, high-volume tasks. Now you are automating a clean process.
4. Monitor
Track the combined process: human steps, automated steps, handoffs. A continuous feedback loop to catch problems early.
5. Iterate
Processes change, requirements shift, new opportunities emerge. Keep mining, keep monitoring, keep improving.
For more on step 4, see our guide on continuous process monitoring .
Common Pitfall
Automating a broken process just makes it break faster. Always improve first, automate second. The most successful organizations treat automation as one tool in a broader process improvement toolkit, not the goal itself.
ProcessMind: Your Process Intelligence Platform
ProcessMind is a modern, self-service process intelligence platform that combines process mining, process modeling, and simulation. It helps you:
- Discover how your processes actually work with data-driven mining
- Analyze bottlenecks, deviations, and root causes with interactive dashboards
- Design improved processes with BPMN 2.0 modeling tools
- Simulate changes before implementing them to predict impact
- Monitor process performance continuously to sustain improvements
Whether or not automation is on your roadmap, ProcessMind gives you the visibility to make better decisions about how to improve your operations. And when automation does make sense, you will have the data to build a solid business case and the monitoring to verify results.
Ready to see what is really happening in your processes?
Related Resources:
- What is Process Mining? - The fundamentals
- How to Analyze Your Process - From data to insights
- How to Implement Process Optimization - Turning insights into results
- How to Continuously Monitor Your Process - Sustaining improvements
- UiPath vs. ProcessMind - Platform comparison
- Process Mining Challenges and Best Practices - Avoiding common pitfalls
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