Process Mining vs. Process Modeling vs. Process Mapping: What’s the Difference?
6 min read
Process mining, process modeling and process mapping are distinct, but related, methods of visualizing and analyzing business processes.
Every business is, ultimately, a collection of business processes. Processes power the creation of new products, facilitate the delivery of services, enforce company policies, maintain compliance and ensure the organization is, at all times, moving toward its overarching goals.
Each business process is a complex set of interconnected and interdependent activities that work in tandem to drive a particular business outcome. Employee performance reviews, marketing activities, content creation, software development and sales are all common types of business processes. To ensure these processes are working as intended, businesses need a way to easily define, analyze, adjust and oversee each workflow.
Organizations have developed methods to transform these abstract workflows into concrete, comprehensive pictures that illustrate the inner workings of each process. These methods include process mining, process modeling and process mapping. While each technique helps an enterprise manage, optimize, and automate processes, they do so in slightly different ways.
What is process mining?
Process mining is a form of business process management that helps organizations discover, assess and improve workflows. In process mining, an algorithm is applied to the event log of an IT system, such as a customer relationship management (CRM) or accounting software. The algorithm uncovers trends in the event log data and uses those trends to construct a process model, which visualizes the workflows occurring within these systems. Process mining algorithms can also be used to enhance existing process models, compare process models to actual workflow instances and simulate process changes.
The key benefit of process mining is the data it provides. It helps enterprises use event log data to guide resource allocation, workflow optimization, automation initiatives and other critical business decisions.
What is process modeling?
A process model is a visual representation of a workflow, produced by applying data-mining algorithms to event log data. Process models offer quantitative, objective illustrations of business processes. These models contain a wealth of workflow data, including events, a log of who owns or initiates those events, paths taken within the workflow, timelines of each step, success rates and more. Process modeling can be understood as a subcomponent of process mining — specifically, the stage at which the algorithm uses event log data to generate a workflow model.
The key benefit of process modeling is that it paints a picture of processes as they exist, rather than how the enterprise may think they exist. By leveraging event log data, process-mining algorithms create quantitative models that offer previously unobtainable levels of transparency into organizational workflows.
What is process mapping?
Like process modeling, process mapping refers to the creation of a visual representation of a business process. However, while a process model is data-driven and quantitative, a process map is subjective and qualitative. Process mapping typically begins with a business analyst or strategist holding workshops and interviews with the people involved in a target process. The analyst or strategist then uses the information they’ve collected to create a process map, either by hand or with a process-mapping software.
The key benefit of process mapping is that it visualizes workflows in a more human-focused way. Instead of capturing objective metrics, process maps primarily describe how various people and teams within a business are involved in a given process.
Note: Both process models and process maps represent processes visually, but they are not interchangeable. Instead, they highlight different aspects of the same workflows. Process models are more quantitative, while process maps are more qualitative.
When should organizations use process mining vs. process modeling vs. process mapping?
Consider an enterprise looking for increased clarity into its organization-wide procurement process. When would it be appropriate to use process mining, process modeling, or process mapping?
- Process modeling: If the organization wants to understand precisely what is happening at each step of the procurement process, it would use process modeling. By applying a data-mining algorithm to procurement-process event log data, the organization would generate a comprehensive process model, offering an objective perspective of the entire workflow.
- Process mining: If the organization has already modeled its procurement process and wants to identify specific opportunities for improvement and optimization, process mining would be the best choice. Process mining can compare actual workflows to existing models, highlighting opportunities to streamline processes and improve model accuracy. If the organization wants to test potential improvements before implementing them, it could also use process mining to generate a hypothetical model grounded in accurate event log data.
- Process mapping: If the enterprise wants to clarify which departments own which parts of the procurement process, a process map would be the right choice because it relies on qualitative data and employee experiences. A process map would also help illuminate how the procurement process cuts across departments.
For the most comprehensive view of the procurement process, the enterprise would use a combination of all three — process modeling to capture quantitative data, process mapping to capture qualitative data and process mining to pinpoint opportunities for improvement:
Pros and cons of process mining, process modeling and process mapping
Process mining makes a science out of business process management — but certain prerequisites must be met before it can be applied:
- Pro: Process mining extracts the data from IT system event logs and renders it practical and usable for enterprise teams.
- Pro: Process-mining algorithms can make existing process models more accurate and generate hypothetical models of what would happen if a process were changed.
- Pro: Process mining provides a data-driven view of actually existing workflows and their outcomes, supplying the enterprise with more objective business intelligence to guide resource allocation, automation initiatives, workflow optimization and other key business decisions.
- Con: Organizations need to use specialized tools to deploy process mining because the method relies on advanced data-mining algorithms. That said, employees don’t necessarily need data science backgrounds to conduct process mining, as most process-mining tools automate the application of algorithms and the generation of models.
Process modeling is useful for furnishing enterprises with more objective views of the workflows that power their operations. However, there are some types of data these models cannot capture:
- Pro: Process models offer objectively accurate representations of processes, removing human error and digging beyond assumptions to uncover what workflows look like in practice.
- Pro: Process models visually depict quantitative process data like time, success rates, error rates and objectively measurable outcomes, allowing for a more informed analysis of business processes and business logic. Without a process model, teams are limited to discussing workflows in qualitative terms that do not necessarily reflect reality.
- Pro: Process models make disseminating and discussing processes easier by transforming abstract workflows into concrete images.
- Con: Process models cannot capture qualitative data about how employees experience workflows in the real world; they can only reflect data recorded in an event log.
Process mapping is a fast and flexible way to generate broad overviews of processes, but maps can sometimes be inaccurate because they rely on qualitative reports from employees:
- Pro: Process maps can capture qualitative data about how workflows manifest in real-world employee activities and interactions.
- Pro: Process maps don’t require much in the way of specialized tools, and they can be produced relatively quickly and easily.
- Con: Because process maps are based on employee workshops and interviews, they are less objective than process models and may contain flawed, incomplete or inaccurate information.
Process mining, process modeling and process mapping with IBM
Business automation can’t be achieved without a clear understanding of business processes. In other words, an enterprise must know precisely what happens in a workflow before it can effectively automate any of the steps. Process mining, modeling and mapping techniques deliver quantitative and qualitative data that grants a business unparalleled transparency into workflows. With comprehensive views of their existing processes, enterprises can undertake large-scale automation efforts with ease.
IBM Cloud Pak® for Business Automation has built-in business process capabilities to help organizations gain an accurate understanding of their as-is processes. By applying process mapping, modeling and mining to core business workflows, enterprises can digitize and pinpoint inefficiencies or hotspots in operations.
IBM Cloud Pak for Business Automation is a modular set of integrated software, built for any hybrid cloud, that quickly solves your toughest operational challenges. It includes the broadest set of AI-powered Automation capabilities in the market — content, capture, decisions, workflows and tasks — with a flexible model that lets you start small and scale up as your needs evolve.
IBM also offers IBM Blueworks Live, a cloud-based business process-mapping solution designed to help organizations discover business processes and then document them in a collaborative fashion across multiple stakeholder groups. Teams can work together through an intuitive and easily accessible web interface to document and analyze processes to help make them more efficient. No download required.