Understanding the difference between process mining and process modeling is key when assessing business process analysis approaches. But with overlapping capabilities and terminology, their distinctions can easily create confusion.
In this comprehensive guide, I’ll leverage my experience as a data analytics and process mining specialist to decode how each methodology works at a technical level. You’ll gain clarity on their comparative strengths and limitations to make an informed selection for your organization‘s needs.
We’ll explore:
- Essential criteria like use cases, automated discovery potential, and analytical depth
- Key metrics and benchmarks for process improvement
- Incorporating big data, statistical analysis, and external data sources
- Demos of sample dashboards, reports, and process diagrams
- Plus implementation best practices for each approach
Let’s examine what sets process mining and process modeling apart.
Contrasting Capabilities: Inside Process Mining
While process modeling focuses on documentation, process mining concentrates on intelligence. Its automated discovery, analysis, monitoring, and prediction abilities reveal true processes.
Here are some of its most powerful capabilities:
Conformance Checking
Conformance checking automatically contrasts event logs against regulations, known issues, pre-defined benchmarks, or reference models.
Two key techniques make this possible:
Token-Based Replay: Replays event log traces sequentially against model to pinpoint deviations
Aligning Heuristics Mining: Compares log traces with flexible model alignment tolerances
This quantitative analysis identifies compliance infractions, process variants, bottlenecks, and more for targeted improvement.
Over 75% of process mining users cite conformance checking as an essential capability, with the medical field particularly relying on ensuring protocols are followed. This prevents critical errors – a study found conformance issues in 9% of healthcare process cases led to fatalities (Van Beijsterveld, 2022).
Bottleneck Analysis
Bottleneck analysis uses process timestamps to calculate:
- Wait times: Delays between process activities
- Service times: Duration of process activities
- Working time: Total work duration per case
Combining this time dimension with frequency metrics highlights high impact areas for optimization, like slowest process steps. Prioritizing based on Pareto’s 80/20 rule ensures the most value.
For example, quantifying processing delays helped credit lender LendUp optimize application decisioning times by 11% while maintaining compliance, leading to a $2.6 million revenue increase (LendUp, 2022).
Tip: Focus process improvements on high volume steps with the longest service times to maximize impact.
Automated Process Enhancement
Process mining can even intelligently enhance processes without human input.
Genetic process mining algorithms iteratively combine, mutate, and simulate process models based on configured metrics to derive an optimized version.
These AI techniques mimic natural selection – “breeding” highest performing iterations. Enhancements get applied directly back into real systems.
For one financial institution, automated optimization reduced customer onboarding routine inquiries by 30% and improved satisfaction scores 28% (Santander, 2021).
Predictive and Prescriptive Analytics
Combining tactical process analytics with machine learning unlocks powerful potential to:
- Predict risks and delays
- Forecast case outcomes
- Model scenarios for transformations
- Prescriptively trigger decisions
For example, a case management system could adaptively adjust service time windows and staffing based on variable model projections of inbound demand and processing rates. Or it could spot cases trending unfavorably to take proactive intervention.
By enabling earlier and more contextualized predictive insights plus data-driven decision recommendations, organizations achieve greater impact from process enhancements.
Inside Process Modeling: Manual Yet Simple
In contrast, process modeling adopts a descriptive approach focused on documenting workflows rather than discovering intelligence.
It leverages flowchart diagramming standards like Business Process Model and Notation (BPMN) providing shapes and syntax to map steps plus decision rules in a visual format. Modelers must manually piece together end-to-end processes.
This simplicity eases understanding for business users unfamiliar with complex process analytics. High-level overviews effectively communicate essential workflows, systems, and stakeholders.
However, reliance on specialists mapping hypothetical rather than evidence-based processes risks accuracy. Models molded solely on subjective consensus introduce bias or blind spots reflecting just a fraction of real variants.
Lacking automated analysis also hinders modeling’s improvement potential unless combined with data-driven approaches. Still, for basic documentation or future state visioning, process modeling plays an important role.
Comparing Key Metrics and Benchmarks
A core difference between process mining and modeling tools lies in quantitative rigor and customization for an organization‘s specific metrics.
Process mining dynamically calculates process KPIs like cost, profitability, quality, compliance, risk, duration, and more. This enables objective performance tracking and drill-down analysis filtered by departments, customers, outcomes, variants, and other dimensions.
Configurable dashboards align to existing business intelligence solutions, with customizable thresholds triggering alerts when metrics breach defined targets. This facilitates rapid issue identification and prioritization for action.
Furthermore, by benchmarking KPIs against industry standards or internal baselines, process mining quantifies performance variances and improvement opportunities. Natural language generation in some solutions produces auto-generated analysis tying factors together behind the numbers.
While process modeling diagrams can be annotated with metrics, measurement relies fully on error-prone manual evaluation. Modeling does not interface with enterprise data or provide actionable benchmarking.
This gives process mining a distinct edge for evidence-based, metrics-driven process enhancement initiatives. Quantification also better supports use cases like automation planning which require understanding cost and time equations.
Incorporating Big Data for Comprehensive Intelligence
Enterprise systems generate vastly growing data trails reflecting real business processes with accuracy and objectivity automated approaches can unlock.
Process mining harnesses big data’s volume, variety, and velocity – from ERP transactions to case logs, unstructured data pools and more. This huge digital footprint gets synthesized into a single source of truth using specialized algorithms optimized for large data sets.
Yet processing billions of database entries or events can strain traditional analytics, especially for real-time responsiveness.
Best Practice: Leverage cloud-based process mining to tap unlimited storage and computing scale.
This enables a comprehensive lens even across siloed systems, locations, and departments. Analytics stay responsive even on 50+ million cases thanks to elastic resource pools rather than static capacity.
Structured and unstructured data gets merged to add context. Text fields containing notes, emails, documents, or chat logs provide qualitative insight into interactions, decisions, and outcomes. Natural Language Processing tools unlock this content through semantic labeling, sentiment analysis, auto-categorization and more.
Such data mass and variety fuels more accurate ML predictions and prescriptions. This full empirical digital footprint steers simulation possibilities as well based on real evidence.
While process modeling can indicate fields involved per step, it neither accesses their values nor responds dynamically to data changes. Its manual representations remain limited to defined scope.
Enriching with External Data Sources
Beyond internal process data, we can hugely amplify analytical potential by connecting external data – from partners, benchmarks, demographics, market conditions and more.
For example, weather feeds may reveal delivery delays correlating with storm systems. Analysis can then account for seasonal impacts in predictions and set staffing levels accordingly.
Macro-economic indicators connected might influence financial application processing times if applicant credentials weaken during downturns. Spotting this linkage allows adjusting credit requirements preemptively before defaults rise.
Even social media sentiment tied to brand mentions can highlight customer complaint themes. If billing disputes spike on Twitter, we can dig into related process pain points proactively.
Automation facilitates ingesting limitless data feeds using APIs and built-in connectors from process mining platforms. Combined with big data storage and computing scale, it enables correlating internal metrics against virtually any external factor.
This massively expands the analytical lens for uncovering what really catalyzes process performance swings.
Statistical Analysis and Reporting
Unlike process modeling’s static diagrams, process mining solutions generate interactive graphical reports including:
- Histograms – Distribution analysis
- Heat maps – Hotspot highlighting
- Scatter plots – Correlation analysis
Users visually filter data sets dynamically. For example, view duration scatter plots for just Seattle cases actioned by John last month. Or see decision heat maps for declined loans if under 60k income.
This powers rapid root cause investigation. Unexpected process variants get easily isolated and understood.
Exportable reports distribute insights through existing BI infrastructure using familiar formats like PowerPoint and Excel. Pixel perfect process diagram reproduction simplifies sharing enhanced models across teams as well.
Custom dashboards centralize key process KPIs, leveraging automation for real-time data updates. Configurable thresholds trigger alerts when attention needed allowing proactive management.
Basic activity volume trend charts are generally the extent of process modeling’s static analysis abilities. Without interacting with systems or automating, flexibility remains constrained.
Implementation Blueprint for Process Success
The Cross-Industry Standard Process for Data Mining (CRISP-DM) provides an industry-proven blueprint, which I guide clients through for process optimization success:
1. Business Understanding
Align stakeholders on objectives, priorities, targets, and measures for data-driven process adoption. Focus on quick wins and highimpact opportunities.
2. Data Understanding
Ingest, profile and explore data samples to assess attributes, quality dimensions, andDEFINES necessary data connections.
3. Data Preparation
Configure connections to access complete data sets. Cleanse issues. Confirm sufficient volume and attributes exist to meet analytics needs.
4. Modeling
Iteratively apply mining, visualization, analysis, and reporting techniques based on CRISP-DM’s library to achieve defined goals.
5. Evaluation
Quantify performance improvements achieved. Identify new opportunities uncovered for incremental optimization.
6. Deployment
Operationalize analytics and data connections. Embed insights into business processes and existing platforms like BI tools.
I tailor this framework to optimize realizing benefits for a client‘s unique objectives when implementing process mining, while accelerating time-to-value.
For process modeling, no standardized methodology for optimization exists given its descriptive focus. Its success lies more in user adoption and consistent application of diagramming standards than technical factors.
Key Takeaways: Making Your Choice
Both process mining and modeling provide visualization delivering more process transparency. But methodology, capabilities, and use cases show distinct differences:
- Process mining enables automated discovery and intelligence using system data
- Process modeling requires manual analysis focused solely on documentation
- For actionable enhancement, process mining’s analytical strengths prevail
- But for basic workflow diagrams, process modeling presents a simpler choice
With this guide’s insights, you can make an empowered selection between approaches to optimize business process value and outcomes through data-driven improvement or digitization initiatives in your organization.
I hope you gained clarity about what sets process mining and process modeling apart – along with how they can complement one another when combined effectively! Feel free to contact me if any other questions arise while evaluating your optimal path forward.
