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Simona from Minit
Simona from Minit

The Case Against Process Mapping and Why You Should Do Process Discovery Instead

Process mapping vs. process discovery is akin to perceived reality vs. reality; the former rooted in subjectivity, the latter rooted in verifiable data. Elements of process mapping creep into process discovery, restricting an absolute dichotomy between the two. However, the critical differentiator between mapping and discovery lies in the distinction between fact and fiction. Process discovery is primarily concerned with concrete, verifiable data (event logs, digital footprints), while process mapping relies on subjective first-hand remembered events. 

Let us first review the core definitions of process mapping and process discovery before moving to the case against process mapping in favor of process discovery.

Key Differences Between Process Mappingig and Process Discovery 


Process mapping is the human-side of establishing an ‘as-is-process.’ It’s concerned with measuring and comparing a defined objective against an organization's larger vision to ensure process are aligned with a company’s core competencies, capabilities, and overarching values. While the end game aims for process improvement, a significant element of subjectivity and unintentional validation techniques are laced within manual process mapping. Perceived reality is flawed regardless of intentions. 

Automated process discovery, on the other hand, is exclusively concerned with verifiable data logs, providing an accurate picture of how processes are performed, rather than the idealized model of how they should be performed, or how employees think they are performed. Additionally, process discovery takes the white space between information systems and seemingly unrelated events and builds bridges with data rather than assumptions. Anomalies and outliers are accurately weighted without being unfairly amplified or ignored 

Process Mapping Process Discovery
Manual Automated
Subjective Objective
Remembered events Verifiable event logs
Human validation Data-driven
Limited scalability Full scalability
Process details must be known by employees No employee knowledge of process details needed
Slow, drawn-out Fast, continuous
Too much or too little detail Exact reality
White space between IT systems and processes unknown White space between IT systems and processes bridged
Outliers ignored Outliers appropriately weighted


Process Discovery vs Process Mapping — Why Discovery is Best

The case for process discovery is strong, particularly when midsize and enterprise level companies are looking to initiate Business Process Improvements (BPI). From knowledge gaps bridged to socio-cultural subjectivity eliminated, here are the top reasons to choose process discovery over process mapping.

1. You don’t know what you don’t know (white space)

White space is the unseen area between various systems, departments, and functions at the edges of a process. One of the most significant challenges in manual process mapping is effectively extracting information from employees involved in a process. Piecing together “remembered activities” to create a process map will inevitably be riddled with knowledge gaps, employee validation, and human subjectivity. In other words, you don’t know what you don’t know, and “unseen” events will not be included in the map.

Process discovery captures all the nuances of a process, including statistical information, process exceptions, unusual transactions, deviations, potential process risks, bottlenecks, and variants. Process discovery bridges the gaps between the individual process steps across multiple ERP and IT systems. Automated process discovery delivers a detailed process map rich with data and flexible for interactive analysis.

2. Map unstructured data from process unknown to employees

Concerning white space and knowledge gaps, some processes, and steps in a process are entirely unknown to employees and, therefore, must rely on digital event logs to create a picture of a process. Consider a global supply chain operating on Just-In-Time manufacturing principles. This fast-moving, lean approach to production relies on multiple actions triggered by a single event. Building on verifiable, time-stamped data logs from hundreds to hundreds of thousands of tiny events is something humans simply cannot map. Process discovery systems, like Minit, take this unstructured data and automatically create a process model to enable in-depth analysis. Minit technology is able to take various data inputs from scratch and deliver pattern recognition. This enables companies to discover processes without prior process knowledge or specifying an existing model.

3. Eliminate socio-cultural behavior from analysis

Humans make decisions based on emotions. It’s a fact wired into our neurological pathways. This doesn’t mean we are illogical, far from it. It means we gather information, process this information from our lens of reality, and then use the frontal lobe of our brain to make a decision — the area responsible for emotional expression, problem-solving, memory, language, and judgment.

Without intent for sabotage, humans will deliver a subjective view of reality, as well as use unintentional validation techniques. It’s not lying; it’s how we communicate as humans. Automated process discovery reads between the lines of data logs, not words, eliminating the presence of socio-cultural behavior from a process analysis. Additionally, what manual process mapping may express as statistical noise, process discovery can appropriately highlight as inefficiencies in business processes.

4. Focus on accuracy and speed

Have you ever seen a whiteboard overloaded with sticky notes — diamonds, arrows, mini-sticky notes upon larger sticky notes? An array of orange, pink, blue and green activities? This is process mapping. Process mapping can, and should, be accompanied by technology like Microsoft Visio and purpose-built process mapping tools, but keep in mind that these tools help you take sticky notes off a white board and build sticky notes on a computer screen. Your big win here is that a humid day won’t wipe progress off the board. This approach is still relying on human inputs, not data inputs, bringing accuracy level to a low.

In terms of speed, calculate the total human hours needed for individual staff interviews, facilitated discovery workshops, analysis of existing documentation and direct work observation. Then compare this to process mining software that plugs in, transforms unstructured data into meaningful maps, and delivers a flexible, in-depth process analysis based on hard data. This is an essential part of understanding the real cost of going through a BPI transformation.

5. Unlimited scalability

Last but not least, a massive benefit of automated process discovery over manual process mapping is scalability. Once systems are connected and process mining software established, a process can be endlessly reanalyzed with little to no additional effort. As subtle changes in the process are made during the discovery period, technology will capture this immediately and include it in data mapping. Businesses need to optimize business processes continuously, and scalability is a big part of making this financially feasible.

See Process Discovery Technology in Action

Minit software analyzes concrete data from various systems to discover how business processes flow in reality. Our platform automatically recreates process maps from traces of user actions (electronic footprints) left within applications. It provides an accurate picture of how users — employees, suppliers, customers, etc. — are performing their duties or actions rather than the idealized model of what they are supposed to be doing.

We’d love to show you the power of Minit and how it can help deliver an effective BPI transformation at your organization. Get in touch with our team to learn more and request a trial of Minit.


This entry was published on Aug 06, 2018 / Simona from Minit. Posted in Business Process Management (BPM) , Business Process Modeling Notation (BPMN) , Data Analysis & Modeling, Analytical and Problem Solving Skills. Bookmark the Permalink or E-mail it to a friend.
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