Business intelligence and process mining both use data, but answer different questions. This page explains how they differ, how they complement each other, and when to use each to understand and improve process performance.
Process mining and business intelligence are often compared because both use data to support analysis and decision-making. However, they focus on different questions and provide different types of insight.
If you are working within process discovery, this distinction matters. Business intelligence helps you understand what is happening through metrics and trends, while process mining helps you understand how processes actually run across systems and cases. For this reason, process mining is also frequently compared to task mining and process intelligence, which address related but distinct aspects of work and execution.
This page explains how business intelligence and process mining differ, how they complement each other in practice, and when each approach is most appropriate for understanding and improving processes.
Business intelligence focuses on analyzing and reporting data to understand business performance. It aggregates data from multiple sources and presents it through reports, dashboards, and metrics that help teams monitor results and trends.
BI is primarily used to answer questions about outcomes. It helps organizations see how key indicators change over time, compare performance across regions or products, and track progress against targets. Typical BI outputs include KPIs, scorecards, and trend analyses.
Because BI works with aggregated and summarized data, it is well suited for monitoring and decision support at a management level.
However, it does not explain how results are produced at the level of individual cases or process flows. When performance issues appear in BI reports, additional analysis is often needed to understand the underlying causes.
Process mining focuses on understanding how processes actually run across systems. It uses time-stamped event data to reconstruct end-to-end process flows and show real execution behavior at the level of individual cases.
Instead of aggregating data into metrics, process mining preserves sequence and context. This makes it possible to see how activities are ordered, where cases wait, how often paths vary, and where execution deviates from expectations. These insights are difficult to obtain through traditional reporting or dashboards.
Process mining is used when the goal is to understand process behavior, not just outcomes. It helps explain why performance looks the way it does in reports, identify structural causes of delays or rework, and validate whether process changes are having the intended effect.
Because it analyzes execution data at scale, process mining provides a factual basis for process analysis, improvement, and ongoing monitoring—especially in complex, system-driven processes.
Process mining and business intelligence both rely on data, but they serve different analytical purposes.
Business intelligence provides descriptive insight. It summarizes data into metrics and trends that show what has happened, such as revenue performance, order volumes, or average processing times.
Process mining provides behavioral insight. It shows how processes actually run by revealing sequences, variants, and deviations in execution. Instead of summarizing outcomes, it explains the paths that lead to those outcomes.
This distinction matters because metrics alone do not explain behavior. Process mining adds the missing execution context behind BI results.
Business intelligence typically works with aggregated data. Information is grouped by dimensions such as time, region, or product to support comparison and reporting.
Process mining works with case-based event data. Each event is linked to a specific case, preserving order and timing. This allows process mining to reconstruct complete process flows rather than isolated data points.
As a result, BI is well suited for monitoring performance, while process mining is better suited for understanding cause-and-effect relationships within processes.
Business intelligence answers questions such as:
Process mining answers questions such as:
These questions are complementary. BI highlights where performance issues exist, while process mining explains why they occur.
Business intelligence operates at the business outcome level. It focuses on results and indicators that support strategic and operational decision-making.
Process mining operates at the operational execution level. It analyzes how work moves through systems and steps, revealing structural patterns that influence outcomes.
This difference is important when moving from reporting to improvement. Improving outcomes often requires understanding execution, not just measuring results.
Business intelligence excels at monitoring, benchmarking, and communicating performance. Its limitation is that it cannot explain process behavior or root causes on its own.
Process mining excels at revealing flow, variation, and execution issues. Its limitation is that it does not replace BI reporting or strategic performance management.
Used together, these approaches provide both visibility and explanation—metrics to detect issues and process insight to resolve them.
Process mining and business intelligence are most effective when used together, each addressing different stages of analysis. Rather than choosing one over the other, organizations often combine them to move from monitoring to understanding and improvement.
Business intelligence is typically used as the starting point. Dashboards and reports highlight performance issues, such as increasing cycle times, missed targets, or growing backlogs. These signals indicate that something is wrong, but not why.
Process mining is then used to explain what BI cannot. By analyzing execution data, it reveals how processes flow in practice, which variants drive poor performance, and where delays or rework occur. This makes it possible to connect performance metrics to concrete process behavior.
In practice, insights often flow back into BI. Findings from process mining can inform new KPIs, refine existing metrics, or help teams interpret trends more accurately. This creates a feedback loop where BI monitors outcomes and process mining explains and validates the underlying behavior.
Together, BI and process mining support a more complete analysis cycle: detect issues, understand causes, and measure the impact of changes over time.
Choosing between business intelligence and process mining depends on the type of question you are trying to answer and how much detail you need about process execution.
Business intelligence is the right choice when your goal is to monitor performance and track outcomes. If you want to understand trends, compare results across time or regions, or report on KPIs, BI provides the aggregated view needed for decision-making.
Process mining is more suitable when performance issues are already visible but not well understood. When BI shows delays, inefficiencies, or inconsistencies, process mining helps uncover how these outcomes are produced by actual process behavior. It is especially useful when processes span multiple systems or involve many variants.
Using both together makes sense when organizations want to move from reporting to improvement. BI identifies where attention is needed, while process mining explains what is happening inside the process and why. This combination supports targeted improvements and helps validate whether changes lead to measurable results.
In practice, organizations often start with BI for visibility, introduce process mining to gain execution insight, and then use both to support continuous monitoring and improvement as maturity grows.
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