5
You think you know how work gets done at your organization. But if you’re not performing process analysis, do you really?
Process mining takes recorded events across systems like ERPs and CRMs and reconstructs the workflows that teams perform. Process mining can reveal bottlenecks, inefficiencies, and automation opportunities that improve business outcomes.
Here, learn the process mining definition, understand how businesses utilize process mining today, and see where process mining falls short. Find out how Scribe Workflow AI is changing the game by combining process mining with other powerful data-driven analysis tools to help businesses maximize efficiency.
What is process mining?
Process mining is an algorithm-based information gathering technique that logs data on how work happens. Software tracks business processes, identifying trends and patterns.
Process mining bridges business operations and IT by turning event data into process maps. Put simply, the algorithm creates a flow-chart style visual of current operational processes.
Part of the field of process intelligence, process mining helps teams gain information (that is, intelligence) on how tasks progress. Then, they can analyze this data for inefficiencies and implement improvements.
How process mining works
Process mining comprises three stages: data extraction, process reconstruction, and analysis and optimization. Here’s more on how each works.
Data extraction
In this first step, the algorithm collects raw, real-time event log data from the systems it scans, which often include Enterprise Resource Planning (ERP) software or Customer Relationship Management (CRM) systems. For each process the algorithm reads, it collects case IDs, activities, and timestamps.
Process reconstruction
In the reconstruction phase, the software generates a visual map of the end-to-end process studied. The process mining tool links the case IDs, activities, and timestamps gathered into an easily legible flow chart.
Analysis and optimization
In this step, the software analyzes the process model for deviations, a.k.a. inefficient ways of working that don’t take the most direct route to success. The tool pinpoints bottlenecks that cause work to slow down. With this knowledge in hand, teams can optimize process inefficiencies and streamline workflows.
3 primary process mining techniques
The three main types of process mining are process discovery, conformance checking, and enhancement. While different, all of these process analysis methods work toward a single goal: boosting efficiency.
- Process discovery: In this model, process mining software gathers event log data and turns it into a visual map.
- Conformance checking: This methodology compares an existing process model to actual ones, highlighting where real-world data deviates from intended processes.
- Enhancement: This model focuses on process optimization, improving upon the current workflow—removing bottlenecks and redundancies.
Benefits of process mining
Process mining helps your operation run more efficiently, which may sound like reason enough to perform it. But there are several other convincing reasons, too. Here are five others.
- Increased transparency: Without a process mining solution in place, you don’t really know how work gets done. Only when you map current processes can you spot the hidden bottlenecks and other inefficiencies dragging on your teams’ productivity. With the transparent data process mining provides, you can pivot toward much more functional work.
- Faster identification of inefficiencies: Without process mining, inefficient tasks can slip under the radar. And they often waste resources, time, money, effort, and tech. The longer processes stagnate, the bigger the business losses. But when you use process mining, you can quickly identify bottlenecks and launch improvement initiatives.
- Measurable process performance: Mining gives you data on how actual process models play out, so you never have to guess. With hard facts in hand, you can compare “as-is” process flows to intended or ideal ones. And as you implement improvements, you have a data-based point of reference to check against when assessing the success of new processes.
- Better compliance and audit trails: Businesses in every sector—from financial services to healthcare to human resources—must meet certain industry standards. But as processes erode, so does compliance with those best practices. When you perform process mining, you catch deviations from the norm and keep your organization compliant. In turn, you confidently avoid risks, like anti-compliance fines or failed audits.
- Improved decision-making for automation and digital transformation: Automation and other AI-driven tools can revolutionize operational efficiency—but only when companies are ready to adopt them. You must already have functional workflows to enhance to get the most out of your digital transformation. Constantly reviewing and improving your processes readies your organization for tech-driven interventions.
Limitations of process mining
Process mining is a first step organizations can take to understand how teams perform their daily work, but is has limitations that obscure what is actually going on under the hood:
- Requires event logs: process mining often only works if logs contain data like case IDs, timestamps, and clear activity names. Many of the applications that are involved in teams’ day to day work don’t have detailed logs that a process mining system can ingest. Process mining only shows what systems can record, not what actually happens.
- Struggles with unstructured work: processes that rely on judgment calls, back-and-forth conversations, or creative work often don’t map clearly to event logs and are harder to model by process mining tools.
- Acts on siloed workflows: process mining is useful to dissect individual workflows, but it often struggles when there are many teams involved in doing work. Process mining cannot show you how work is moving between teams.
How Scribe maximizes process mining with Workflow AI
Scribe’s Workflow AI levels up process mining, combining it with task mining and workflow mining, giving you a high-level and detailed view of how work happens at your company—the clearest picture yet. With Scribe’s Workflow AI, you:
- Gain visibility: Process intelligence takes a three-pronged approach to data capture: process mining, task mining, and workflow mining. Process mining tracks system record information (i.e., from your CRM), task mining monitors individual interactions (think: keystrokes and other micro-steps), and workflow mining analyzes inter-platform, cross-functional workflows (how work actually moves between people and tools). Workflow AI gives you a comprehensive view of how work gets done—top to bottom.
- Prioritize with data: Workflow AI pinpoints the highest priority areas for improvement so that you can quickly intervene to fix these high-impact processes.
- Drive actionable change: Scribe leverages artificial intelligence to not just capture and map processes but also recommend improvements. And the tool works iteratively—continually “checking its work” and suggesting new and better paths.
Workflow AI use cases
- IT systems or operations: IT projects often contain complex, cross-functional workflows. Information siloing, slow handoffs, tech glitches, and incomplete sources of truth can all stall work—spurring a chain reaction of productivity loss. With Workflow AI, you’ll spot bottlenecks in real time and be able to intervene with fixes before development goes off course.
- Help desks: AI-driven data mining allows help desks to surface time-consuming process breakdowns, like request reassignments, fractured conversation threads, and rework. When you pinpoint and rectify these issues, you speed up resolution times, helping end users get the answers they need faster. In turn, your organization boosts customer satisfaction.
- Employee onboarding: Onboarding is a new employee’s first impression of working at your company, so you want the experience to be great. Workflow AI finds inefficiencies in your onboarding processes, recommending potential automations. For example, AI can trigger-send documents for signatures and perform new hire data entry, upping your time-to-productivity rate.
How to get started with process mining
Ready to improve efficiency at your organization? Here’s how to begin using process mining to improve business outcomes.
- Identify key business processes to analyze: Decide which high-impact processes to analyze. Generally, these are processes that have a significant effect on business value and consumer-facing products. Don’t know where to start? Get Scribe. Scribe’s Workflow AI pinpoints the most important processes to prioritize.
- Gather event or workflow data: Run the process mining software, collecting data on current flows. Go a step further with Scribe’s Workflow AI, gathering task, process, and workflow data for a holistic view of work at your organization.
- Map the process and analyze key metrics: Have the process mining or workflow analysis software map the current flows against projected ones or compare task-based data points with your ideal KPIs.
- Identify root causes of inefficiencies: The software will help you visualize bottlenecks and deviations. Then, it’s time for some critical thinking on how to improve. Leverage data—instead of gut feelings—with Scribe’s Workflow AI. This tool suggests more streamlined workflows for you to implement, taking the guesswork out of process improvement.
- Prioritize improvements and measure impact: Scribe’s Workflow AI guides you toward high-priority process improvements, so that you don’t have to rely on instinct alone. When you rely on this type of data-driven decision-making, you tackle the organization’s most wasteful inefficiencies first, saving big on time and other resources. But the work is never done. Scribe continuously monitors new workflows, measuring positive impact and suggesting further process improvements as needed.
Turn process insights into action with Scribe
Combine your good business instincts with the power of machine learning to achieve the most streamlined workflows yet. Process, task, and workflow mining give you the enhanced visibility you need to make significant improvements.
With Scribe, organizations don’t need complex data pipelines to start uncovering insights. Integrate Workflow AI with your existing tools and rapidly discover when processes go off course. Then, gain data-driven insights on how to get them back on track.
FAQs
How is process mining different from task mining?
Process mining uses process data and maps how systems record process events across core platforms, while task mining reads user-level interactions, like clicks, keystrokes, and time spent using apps.
How is process mining different from workflow mining?
Process mining shows how systems record process events (ERP, CRM, logs), while workflow mining uncovers how work actually moves between people and across tools.
Do you need ERP or CRM data to start process mining?
Not necessarily. While ERPs and CRMs are excellent data sources as they contain detailed event logs with timestamps and event names, Scribe is able to mine processes in browser-based applications. You don’t need systems with detailed event logs to begin using Scribe to see how work is done.
Can AI automate process mining?
Yes. AI can track systems data, suggest improvements, and monitor the success of changes in workflows, helping organizations be more efficient.
What kinds of businesses benefit most from process mining?
All businesses can benefit from process mining. But some of the most successful process mining use cases are in businesses with complex workflows. Examples include customer experience (i.e., help desks), information systems (i.e., software development teams), payroll, HR, fulfillment, supply chain, and procurement.
