Process Discovery vs. Process Mining

Process Discovery and Process Mining are powerful tools that provide continuous monitoring and analysis of processes across various industries. They offer numerous benefits and can help organizations achieve business success and maximize their return on investments (ROI).

Here are some key benefits of Process Discovery and Process Mining:

Process Improvement: These tools aid in the improvement of processes by providing insights and data-driven recommendations. They enable organizations to identify inefficiencies, bottlenecks, and areas for optimization before implementing automation solutions.
Prioritizing Automation: Process Discovery and Process Mining help determine the most valuable areas to add automation. By analyzing process data, organizations can identify high-volume, repetitive, and error-prone tasks that are ideal candidates for automation.
Accurate Process Understanding: These tools provide a holistic view of what machines, employees, and organizations are actually doing, helping to uncover the true state of operations. This eliminates assumptions and provides accurate insights for process optimization.
Workflow Management and Productivity: Process Discovery and Process Mining assist in workflow management, improvement, and productivity enhancement. By analyzing process data, organizations can identify bottlenecks, streamline workflows, and optimize resource allocation.
Process Analysis and Tracking: These tools enable detailed analysis and tracking of processes. Organizations can gain insights into process performance, cycle times, deviations, and compliance issues. This information helps drive informed decision-making and continuous process improvement.

While Process Discovery and Process Mining share similarities as standalone solutions, they complement each other when used together. By combining their capabilities, organizations can address a wide range of common business challenges effectively.

It's important to have a clear understanding of what a process is before comparing Process Discovery and Process Mining. This foundation allows organizations to make informed decisions about which tool or combination of tools best suits their specific needs and objectives.

What is a process?

A process refers to a sequence of interconnected tasks that collectively convert inputs into desired outputs. These tasks can be performed by individuals, automated systems, or a combination of both, utilizing various resources.

A process comprises four key elements:

Steps and Decisions: Represented by a flowchart, this element outlines the specific tasks and decisions involved in completing the work. It provides a visual depiction of the sequence of actions required to achieve the desired outcome.
Variability of Processing Time and Flow: This element pertains to the pattern and consistency of the time it takes to complete each task within the process. It considers factors such as the efficiency of individuals or systems involved and potential variations in task durations.
Timing and Interdependence: Refers to the timing of when employees engage in the process, including work and break periods. It also addresses the interdependence of tasks within the process, where the completion of one task may depend on the output of another.
Assignment of Resources:Involves determining the allocation of resources, such as personnel, equipment, or materials, to specific tasks or stages of the process. It ensures that the necessary resources are available at the right place and time to facilitate smooth process execution.

Understanding these elements helps organizations analyze, optimize, and streamline their processes to enhance efficiency, productivity, and overall performance. Tools like Process Discovery and Process Mining can assist in gaining deeper insights into these elements and identifying opportunities for improvement and automation.

Process Mining and Process Discovery differences:

Process Mining		Process Discovery with Nintex Process Discovery
Objective	The high-level goal of process visibility and optimization	The high-level goal of process visibility and optimization driven by the need to streamline automation
Source	Event logs from enterprise software systems	Real-time user behavior via the employee desktop
Systems	Vertical support: mainly for applications that are creating collectible log files	Support for all types of systems and technologies
Setup	Requires integration to each application separately	Minimal setup time with zero integration
Output	Process modeling (on levels), conformance, intelligence, and documentation	Set of repetitive actions for RPA investigations, CSV file, BPMN, and a framework for RPA
Granularity	Transaction level and user action: task level	User action level
Time to Value	Several weeks or even months	1-2 weeks

Process Mining and Process Discovery are two distinct approaches used to analyze and optimize business processes, each with its own focus and benefits.

Process Mining relies on event logs and unique identifiers to gather data and gain insights into business activities. It primarily utilizes the digital footprints of IT systems to analyze and optimize end-to-end business processes. By analyzing these event logs, Process Mining can identify bottlenecks, inefficiencies, and opportunities for improvement.

On the other hand, Nintex Process Discovery operates by running Discovery Robots on user machines in real-time, recording their actions (keyboard and mouse activity) within approved applications. The recorded data is then sent to a server for analysis using algorithms based on front-end features and metadata. The primary focus of Process Discovery is to identify automation opportunities.

Both Process Mining and Process Discovery have their strengths and are suited for different types of processes. Process Mining is well-suited for complex processes where analyzing event logs from IT systems provides valuable insights. However, for simpler processes, Process Mining may be excessive and time-consuming. In such cases, Process Discovery can be more helpful as it provides real-time analysis and focuses on automation opportunities.

The combination of Process Mining and Process Discovery can be powerful. Once data is gathered from Process Mining and a specific area requires deeper analysis, Process Discovery can be employed. For example, if an organization wants to understand the performance of a specific department or application, Discovery Robots can be deployed to gather insights. By zooming into specific areas, such as the accounts payable team in the finance department, organizations can gain valuable insights into process inefficiencies, employee performance, and potential process improvements based on the gathered data.

How can organizations link Process Discovery data to Process Mining?

To link Process Discovery data to Process Mining, organizations can follow these steps:

After the Discovery Robots collect data from various applications, the data is analyzed in the Process Discovery Server and mapped in Nintex Process Discovery.
From the available interactive map views, such as the Linear Graph and Unified Graph, organizations have several output options:
- Automation file: This option allows users to download visible variants as automation workflow files that can be imported into the Studio for further development. See Downloading an Automation for more details.
- Discovered process report: Users can download selected processes and variants in an editable MS Word format, providing a detailed report on the discovered processes. See Downloading a Discovered Process Report for more details.
- Event Log: Selected processes and variants can be downloaded as an event log in CSV format, which can be used with a Process Mining tool. See Event Log for more details.
- BPMN diagram: Users can download selected processes and variants as a Business Process Model and Notation (BPMN) diagram. This diagram provides a graphical representation of the processes and can be added to process modeling, process mapping, or a process mining solution.
When the Nintex Process Discovery Server analyzes the data, it creates an action event log in CSV format. This CSV file can be incorporated into the organization's Process Mining solution. By using the CSV file output, users can zoom into specific parts of a process in the Process Mining tool and gain visibility into deeper-level activities.
Additionally, organizations can utilize BI dashboards like Appsight to bridge the gap between Process Mining and Process Discovery. These dashboards provide a high-level view of metadata, allowing users to identify trends, patterns, and insights. Users can focus on specific areas of the business analyzed through Process Mining and complement it with the processes identified through Process Discovery.

By combining the capabilities of Nintex Process Discovery and Process Mining, organizations can gain a comprehensive understanding of their business processes, optimize automation opportunities, and uncover valuable insights for process improvement.