What is Process Intelligence & 5 Reasons Why It Matters

Hazal Şimşek

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The graph shows process intelligence and process mining google search trends, proving how two terms are related.

Process management, or process improvement, helps companies enhance performance and boost operational excellence. As a result, more decision-makers are adopting process management tools for optimization. Various vendors offer tools under different names, such as process intelligence software and process mining. These name differences could confuse analysts, experts, and leaders, making process improvements harder.

Explore what process intelligence is, discussing if it’s different to process mining, and providing you with five reasons as to why you should implement process intelligence.

What is process intelligence?

Process intelligence is the practice of collecting and analyzing the process data to identify bottlenecks and improve operational efficiency. Process intelligence allows business analysts to discover each step within a workflow, the personnel in charge, the duration of the entire process, the average wait time, and the issues with the process.

Process intelligence tools include:

Process intelligence tools employ process mining algorithms to collect event data in a process from systems and applications, such as ERP and CRM, and to analyze the data and visualize the process. These tools help monitor and discover processes by exploring historical or real-time process data. To learn more on each tool, read our process improvement framework article.

Check out alternatives to process intelligence like process analysis tools and process improvement techniques.

Process Mining vs. Process Intelligence

Some vendors name their process management capabilities as process intelligence software. By calling their product process intelligence, vendors highlight that their software leverages ML & computer vision to disentangle complex processes and additional capabilities. Vendors also refer to process intelligence as an upgraded process mining which integrates task mining and a DTO.

However, many process mining vendors also benefit from machine learning process mining (e.g process mining algorithms and context awareness) to automate their data collection, process discovery, and root cause analysis while providing additional capabilities such as predictive process mining. Thus, these vendors integrate their platform with a DTO and task mining capability, as well.

Check out comprehensive and constantly updated list of process mining case studies to find out more on process mining real-life examples.

Why is process intelligence important?

Process intelligence help analysts understand process execution, assess process performance, detect bottlenecks, and discover automation opportunities. Based on these insights, analysts can optimize their processes, eventually improving customer experience and reducing cost.

Here is a list of benefits you can gain by deploying process intelligence:

1. Process Intelligence enables data-driven decision making

The digital and physical systems (e.g SAP, IoT or sensors) used in daily operations generate an outstanding amount of data.

Process intelligence tools allow you to leverage this data to discover patterns and insights for data-driven decision making. You can collect, aggregate and clean the process data to centralize your business operations.

2. Process Intelligence helps establish best practices

Business leaders and analysts often have an idea of how their processes are executed, but it is usually a quite limited understanding of their operations, one that lacks context and knowledge of the process.

You can utilize process intelligence to understand how your processes operate and compare each employee or team to manage the operations to determine the optimal method. Then, you can establish best practices for the rest of your business processes. For example, you can generate process definition documents based on your optimal process execution to serve as an ideal model for the latter.

3. Process Intelligence identifies and eliminates inefficiencies

Processes often contain deviations, bottlenecks, errors (such as tasks that are disintegrated with the entire process), and dysfunctional processes which hinder overall productivity.

You can capture, analyze, identify, and visualize your processes with process intelligence tools. As a result, you can pinpoint the bottlenecks in a workflow and deviations from the idealized processes by checking the models, digging into the root causes behind the issues, and start improving them. This will help eliminate inefficiencies in the model.

4. Process Intelligence allow you to optimize your costs

You can allocate more money by optimizing your budget, which is feasible with process intelligence. Once you establish your ideal processes and eliminate the bottlenecks and deviations, you will notice that your operational costs will start to go down. This will provide you with an optimized budget to allocate to other aspects of your business.

5. It enables progressing faster and efficiently

Process improvement is considered a long and iterative initiative because it requires communicating with different teams, collecting data and cleaning it, mapping and analyzing processes based on the gathered data. Therefore, business analysts might hesitate to undertake a process improvement project.

Process intelligence tools can help analysts shorten the time and efforts in such projects by automating the process improvement. This will allow business analysts to progress faster by obtaining results and implementing the changes. Thus, automation decreases human intervention and potential errors, saving time and preventing iterations.

Power BI vs Process mining

Power BI is a common business intelligence tool which enables organizations to explore their external and internal data to optimize strategies, improve customer experience and save costs. Process mining can serve as an alternative to business intelligence tools since it extracts and analyzes real-time process data to identify performance issues, root-causes, noncompliant cases, and automation opportunities.

We recommend combining these two tools to be able to process a higher volume of data and create interpretable visuals to obtain more insights and improve data-driven decision-making. Here is how:

Enhance Power BI infrastructure with Process Mining (PM): Power BI struggles with complex datasets, often resulting in static models due to failed refreshes or limited real-time data extraction. Process mining resolves this by collecting and analyzing large datasets from IT systems, enabling the discovery of dynamic process models, performance metrics, and interactive graphs. Integrating Power BI with process mining improves process analysis while reducing the time spent on data cleaning.
Enrich process mining visuals with Power BI: Process mining provides process models and workflow maps, but these can be complex for users to interpret. Power BI enhances these visuals by offering intuitive, customizable charts like stacked bars and slicers, enabling real-time tracking, budget revisions, predictive insights, and better decision-making. Analysts can schedule and share reports to make process insights more accessible and actionable.
Generate data-driven strategies: While Power BI highlights poorly performing KPIs, it does not provide root causes or solutions. Process mining identifies bottlenecks and root causes, such as delays in manual approval processes, allowing BI analysts to implement strategies based on actionable insights without relying on guesswork or interviews.
Improve efficiencies for a streamlined workflow: Power BI offers high-level KPI tracking but lacks compliance checks or detailed root cause analysis. Process mining complements this by identifying non-compliant activities and performance issues through conformance checks. Together, they streamline workflows by combining broad KPI monitoring with detailed process analysis.
Implement RPA and workflow automation: Power BI alone cannot identify root causes of inefficiencies, missing automation opportunities. Process mining uncovers manual tasks, calculates ROI, identifies RPA deviations, and predicts failure points, enabling successful automation projects.
- For example, in a process mining case study, Piraeus Bank solved the problems in its automation project and shortened its loan application process from 35 minutes to 5 minutes by employing process mining.