Process Mining is not a recent approach and can provides great outcomes if the recipe ingredients are all here and if the cooker respects all the methodology steps and activity in the right order. However Process Mining Projects can fail and there several here several concrete reasons why such initiatives can fail.

1. Because of Poor Data Quality: Process mining relies on accurate and complete data to generate insights about business processes. If the data is incomplete or inaccurate, the process mining results may be unreliable, leading to incorrect or incomplete insights. As a pure data-driven approach the Process Mining project destiny is bounded to the quality of the data.
2. Lack of Executive Buy-in: Process mining initiatives require support from senior executives to succeed. Without executive buy-in, it may be difficult to secure the necessary resources and funding for the initiative, and it may not be seen as a priority within the organization.
3. Inappropriate Solution: Some Process Mining solutions are really complex (most of them in fact) to set up & use. So they need to have specifics skills (and training – see below-) to be used properly. This complexity does not match with the expected usage as the Process Analyst does not have these skills (and does not have time to be trained on it). The Process Mining solution choice is most often based on the widest criteria (ie. the solution which bring the most features). At the opposite (see the pareto law) the usability and the simplicity of the solution is too often under-estimated and cand lead to a project fail.
4. Inadequate Training: Process mining tools can be complex (especially the oldest and biggest solutions), and require specialized knowledge and training to use effectively. Sometimes it’s also required to have deep technical knowledge which is a non sense as these tools should be designed for Process Analyst. So, without proper training, thses Process Analysts may not know how to use the tools properly, or may misinterpret the results, leading to incorrect or incomplete insights.
5. Limited Scope: Process mining initiatives may fail if they are too narrowly focused, or if they don’t address the most critical business processes within the organization. It’s important to identify the key processes that will have the most impact on business performance, and focus process mining efforts on those areas. This is typically the role of ExYPro stage 2 methodology.
6. Resistance to Change: Process mining initiatives may fail if there is resistance to change within the organization. Process improvements may require changes to the way people work, which can be met with resistance from employees who are comfortable with the status quo.
7. Lack of Continuous Improvement: Process mining initiatives are not a one-time event, but rather an ongoing process of continuous improvement. Without a commitment to continuous improvement, process mining initiatives may not deliver the desired results.

So to avoid all of these traps it’s really key to consider a Process Mining initiative as a real project and not only under the solution prism. By combining the right Process Mining solution with the right methodology the project must not fail and will drive to great business outcomes.

This is the purpose of the ExYPro Methodology: ensuring all the informations and resources are well gathered and used in the right time and providing the best guidance to lead to the expected outcome.