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TDWI Upside - Where Data Means Business

Process Mining Versus Process Discovery: An In-Depth Comparison

Every business is full of processes that might not be fully understood. How can process mining and process discovery help you optimize your existing processes?

Gone are the days when manual methods such as shadowing an employee were used to gather information about enterprise processes. These methods were limited to subjective knowledge, time-consuming, and compromised by human biases.

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Today, a digital revolution has swept the world, and two technologies are uncovering business processes: process mining and process discovery.

Process mining enables enterprises to analyze back-end application log files, and AI-based process discovery is empowering them to observe and record millions of human interactions and derive real-time inputs.

Many enterprises have come to us with one question: What is the difference between process mining and process discovery?

What Is Process Mining?

In simple terms, process mining is the extraction of knowledge from the event commits and application logs to gain insight into business processes. It gives an enterprise insight into its business processes so it can enhance productivity, profits, and customer satisfaction.

The term process mining has been borrowed from data mining where data is “mined” to get insights, predict outcomes, or find solutions. Similarly, process mining “mines” the event logs created from ongoing processes and identifies anomalies, trends, patterns, and even obstacles or challenges. Once an enterprise discovers these insights (typically with process mining software that evaluates the activity name, use-case identifiers, time stamps, event identifiers, and costs), optimizing processes and outcomes becomes easier.

What Is Process Discovery?

Process discovery is the actual discovery of how any process in your enterprise is executed. Using newer technologies such as computer vision, machine intelligence, and deep learning, process discovery gives birth to an enterprise’s digital twin: the invisible enterprise.

The invisible enterprise is the digital representation of the enterprise created by the amalgamation of all the nuances and drifts that diverge from the actual business processes. It’s called invisible because this enterprise is generally excluded from traditional forms of human-led process mapping.

With process discovery, you can extract the process footprint through user interaction with the systems. You can collect both the visible and the invisible processes through this advanced approach. It lets you clearly perceive the invisible parts of your enterprise and make them visible.

Process discovery software gathers the minutest of digital traces left by people in your enterprise. It observes and collects those nuances that could easily be missed during the traditional method of shadowing an employee while he/she does a task in order to map the process.

Instead, process discovery works in a more advanced way. An AI tool captures each human-machine interaction to discover how tasks are done. It uses neural nets and deep learning to recognize the tasks, applications, and human interactions that can be used to create the metamodel for future work.

What Makes Process Discovery Different From Process Mining

First, process discovery software captures even the slightest nuances and discrepancies of human-machine interaction, but process mining overlooks these.

But is that all?

Both process mining and process discovery have several unique factors and capabilities that make them more than just a predecessor and successor in technology.

What’s Unique About Process Mining

Log-based process mining uses discrete points on the committed states of data. It works only on the data collected from the application workflow. An enterprise’s process model acts as an ideal; process mining can help create the process model and analyze any deviation from it, the bottlenecks, and how the process can be optimized to reach the ideal model. It also tells when, where, and how the process deviated from the ideal process model.

For Further Reading:

How Process Mining Can Aid Your Digital Transformation

The Upside of 2020: Trends to Monitor

How to Get More from Your Data in 2020

Process mining use cases include:

  • Discovery: The ideal process model is created after analyzing the event logs and there was no previous process model
  • Conformance: Also called deviation analysis, in this use case, you already have a process model, which acts as a benchmark to compare the collected event logs
  • Performance: You already have a process model with performance indicators in this use case, which helps improve the process’s outcome

Regardless of the origin, process mining works by connecting all the actions stored by the collection of the digital footprint. It creates actionable flow graphs that can be visualized and analyzed to get precise insights.

What’s Unique About Process Discovery

The AI-based approach of process discovery takes a more fluid, continuous, and near-real-time approach to unveil all ad hoc human-digital interactions. Process discovery software unleashes a process digital twin (the invisible enterprise discussed above) that lets you analyze your process “as-is.”

Although process mining requires you to create an ideal process model, process discovery automatically creates a business process representation after collecting and analyzing the digital footprints left by users on the systems and applications. This model comprises all the nuances and variations as well as the frequency in which these variations occur and when.

Unlike process mining, process discovery does not require any logs, databases, or API access, and there is no integration to your system required. It disregards any noise from the interaction and presents the process as it is working. In addition:

  • It does not need any integration to your system application.
  • It incorporates computer vision and machine intelligence to reveal the dark and invisible processes powering your digital enterprise
  • It continuously monitors the processes to ensure real-time analysis and rapid retraining ideas in case of changes in the process
  • Instead of a process model, it creates a metamodel for the future of work to enable a smooth digital transformation

Deploying These Digital Tools into Your Enterprise

Knowledge of process mining and process discovery is not sufficient if digital transformation or automation is your goal. As an enterprise, it is imperative that you know how to effectively deploy each for mapping your processes.

Deploying Process Mining Software

Process mining tools need to be integrated with the back-end transaction system and/or the data file uploads. They are largely deployed by enterprises in the following cases:

  • When they want to optimize their process
  • When they want to conform the existing process with certain specifications
  • When they want to create harmony between distinct processes
  • When they want to get future predictions regarding their processes

Many industries have succeeded with process mining. It helped them discover bottlenecks and build better and more efficient processes. Examples of companies that succeeded with process mining include Vodafone, Walmart, and KPMG. By analyzing the data present within their IT systems, many industries can leverage process mining.

Deploying AI-Based Process Discovery Tools

Contrary to process mining, process discovery software does not need to be integrated into your enterprise systems. Even the deployment is limited to a small, lightweight probe on user desktops. It can be used in different cases such as:

  • When you need to identify RPA opportunities for automation
  • When you are aiming for a digital transformation
  • When you want to unveil previously unknown processes for in-depth process mapping
  • When you do not want to integrate anything but want to know your process

Contact centers, human resource departments, insurance providers, and the finance and accounting industries are among the top industries that can leverage process discovery. These industries often have extensive real-time data that assists them in a true digital transformation.

A Final Word

Both process mining and process discovery play a crucial role in assisting a true digital transformation for any enterprise. One unearths the way your system applications work; the other focuses more on the human aspect and how your employees interact with the systems to unleash the dark and invisible processes.

Depending on your processes and your needs, you can choose between process mining and process discovery.

If you are looking to build your ideal business process model that can make your existing processes more efficient, process mining is your best choice. It can help in business process management as well as conformance to the ideal process model.

If automating your processes or undertaking RPA initiatives is your goal, process discovery can help you know which processes have hidden tribal knowledge and can be optimized for digital transformation.

Today, you can even find AI-powered cognitive process mining and discovery software that can help you leverage the benefits of both process mining and process discovery. It can help you uncover and untangle your enterprise’s business processes and realize the future of your work.

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