What is Process Mining? All You Need to Know

If there are two phrases we are hearing much too often today, they are “digital transformation” and “automation.”

This drastic change was overdue, of course, but the pandemic catapulted it, eliminating any and all excuses for procrastination. If a business has to survive this uncertainty, it has to make digital transformation its key focus, the core of which is automation.

Here’s where the problem comes – all businesses know what to do. But how should they start?

Say hello to process mining.

What is Process Mining?

And why is Forrester driving this change?

Process mining is a set of approaches that discovers real processes through the use of event logs, monitors them, and work towards improving them. It is an analytical approach through which knowledge is extracted from the organization’s systems.

Through this process, organizations can detect errors and bottlenecks based on the facts instead of relying on conjectures. Overall, it covers the following:

• Checking the conformance of business processes
• Automated business process discovery
• Analyzing performance

Reports and research suggest that by 2023, the market size of process mining will inflate to $1,421.7 million!

Are Process Mining and Data Mining the same?

Data mining has been a fad phrase for quite a while now. So, it is easy to confuse process mining and data mining. In fact, many organizations fail to understand the difference. This is mainly because they are both a part of business intelligence and have quite a few similarities.

The differences, however, are significant.

• Data mining is a process of finding relationships in data sets in order to gain insights and improve accordingly. Big data is analyzed and connections are established. This, in turn, helps unearth information that can be put to use in various ways. This technique is very popular in research.
• Process mining, as aforementioned, is all about analysis. It analyses the data in a system and finds ways to improve processes.

How is Process Mining related to Business Process Management?

Process mining is definitely a technique in business intelligence. It is closely related to business process management (BPM) in that it combines data mining with BPM. So, existing data is taken, visualized, and improved.

Process mining combines analysis, control, and improvement of processes.

Is Process Mining a component of BPM software?

Once we hear that process mining and BPM are related, this question is bound to arise. You may be confused by the answer but please allow us to explain.

In short, process mining is not a component of BPM software. But why? Well, that’s because the applications of each are quite different. BPM has been around since the 1970s while process mining is relatively new. BPM speaks about designing and managing operational procedures while the latter works towards optimization and redesigning of operations through existing data.

Also read: Business Process Management – The What, Why, and How

What are the Types of Process Mining?

There are 3 types of process mining and we shall explore them in detail now:

1. Discovery

In this type of process mining, event logs come to the forefront. There’s no prior information, however. A process model is established based on the event logs and algorithms.

2. Conformance

Here, real processes and predefined processes are compared to identify similarities and differences. This helps find out deviations and fix them accordingly.

3. Enhancement

The data of real processes are considered to improve process models. Enhancement process mining aims to improve existing processes by identifying bottlenecks, inefficiencies, or areas for optimization. It analyzes event logs to uncover opportunities for process improvement.

4. Extension

Extension process mining involves enriching existing process models with additional information, such as performance metrics, resource data, or cost information. It helps in gaining a more comprehensive understanding of the process and its associated factors.

What are the steps involved?

There are 2 major steps –

1. Selecting the process and prioritizing: In this step, objectives for improvement are identified and established based on business value. Here, organizations will also understand how processes create value.
2. Capturing process information: Now, the process information is captured and improved to form a process model.

These two steps are further broken down into multiple stages.

Santiago Aguirre did a wonderful job of explaining the methodology of process mining in detail.

What are the Benefits of Process Mining?

When we already have BPM, which is doing an excellent job helping organizations transform digitally, do we really need process mining? How does it benefit organizations? The answer will both surprise and please you.

Some of the most significant benefits of process mining are as follows:

1. It helps save time because, with process mining, you automate data collection. Data collection is usually the most tedious part of optimization.
2. It provides fact-based proof of bottlenecks, which are otherwise very difficult to locate.
3. It allows organizations to see the big picture through real processes and data, giving them the opportunity to find the causes of errors, deviations, and variations.
4. It helps monitor KPIs in real-time.
5. It provides data preparation and optimization support.
6. It standardizes most business processes.
7. Once processes are optimized, operational efficiency will improve.
8. It helps gain perspective in various ways – not just from the process vantage point.

Applications of Data Mining

Use cases

Let’s explore a few use cases to understand the importance of process mining:

• Robotic process automation: Organizations can find out automation opportunities and variations in the processes to enhance their projects.
• Digital transformation: How to optimize the work process and what to transform digitally – process mining helps answer these questions.
• Auditing: It helps ensure that all compliances are met and only the agreed processes are rolled out.
• ERP development: Process mining can be used to provide complete support for version updates, consolidation, etc.

Industries

How do you apply process mining to industries? We have a few examples to help you understand:

1. Manufacturing

One of the most crucial aspects of manufacturing is timely delivery. When a business has multiple factories, delivery cannot rely on one aspect. Many differences come into the picture. Process mining will help understand the process in every single region, right down to costs, KPIs of the people involved, and the duration. This fact-based analysis will automatically help optimize the process.

2. Services

Service companies offer higher efficiency and lower costs to their clients, through operational excellence. Process mining finds process inefficiencies and helps optimize the operations.

3. Retail

Retail business operations are very extensive. They involve logistics, order management, forecasting, supply chain, customer service, etc. Process mining provides visibility to the entire life cycle and the processes involved in each step. It helps unearth bottlenecks through facts so that they are taken care of immediately.

4. Banking

Rules and regulations are the driving force in the financial sector. Event data helps with process visualization in each step. If there is non-conformance to any rule, process mining will help find that out along with deviations to the said rule.

Summary

Process mining may be relatively new but it is here to stay and optimize business processes. The insights this approach provides through analytics are crucial for growth and efficiency.

Want to know how you can apply process mining to your business? Get in touch with us or request a demo now!

Frequently Asked Questions (FAQs)