COURSE OBJECTIVE:
The objective is to really understand what Process Mining is, what it represents, what you can do with it and what you cannot do with it. The main focus is on practice. With the gained basic knowledge, one should be able to make independent (follow-up) choices about the use of Process Mining.
Selection of the questions to be answered during the Level 1 training:
• What is process mining?
• What can you do with it?
• What can you use process mining for?
• What can you use process mining for?
• When does it work and when doesn't it work?
• When to use it?
• Why use process mining instead of other (process)analytical data technologies?
• When and how to integrate process mining in a natural way into the organisation?
• What are the risks?
• What are the preconditions for its successful use?
• How to successfully deploy Process Mining, how do you go about it?
• Who is using it within the organisation?
• What does a business case for Process Mining look like?
• The role of Process Mining software, how to deal with it and what are the alternatives?
• Which knowledge/experience is required?
TARGET AUDIENCE:
Those who are (almost) unfamiliar with Process Mining and are serious about using it in their profession and want to have a solid foundation in Process Mining before making decisions about its use. The participant expects that the knowledge offered will be based on practical experience. It should by definition not be a 'tool-training', the software approach must be independent and objective.
COURSE PREREQUISITES:
Higher education work and thinking level. No specific technical background required.
COURSE CONTENT:
Process Mining conceptual:
• First encounter with Process Mining (demo)
• History of Process Mining
• Scientific and practical approaches
• Process Mining as part of Data-Analysis
• Developments and the future of Process Mining
• Application areas
Data for the purpose of Process Mining
• Event logs
• Extraction and data preparation
• Data quality
• Data and purpose of the analysis
• (Near) Real-time or ad-hoc data
• Data model
• Data knowledge and skills
Applying Process Mining yourself
• Approach using the Pizza Case exercise
• Finding data in tables
• Reading and improving the self-created data set in Power BI
• Descriptive analysis
• Exploring the process and dataset
• Explanatory analysis
• Predictive analysis
Developments that are important for increasing attention to process improvement
• Increased digitalisation
• Multichannel communication
• Speed
• Transparency
• Legislation and regulation
• Robotic Process Automation (RPA)
• Deployment of Artificial Intelligence (AI)
Fulfilling the prerequisites for success
• Criteria for success
• Case selection
• (management) culture
• Data maturity
• Method of organisation and application (domain, broad, at role level)
• Training, competences
Technology choices
• Software tools
• Software selection
• The business case
Data analyses and storytelling
• Data model (incl PM tooling restrictions)
• Maturity analyses
• The role of probability and statistics
• Different data analysis techniques in combination with process mining
• Story telling with data
• Inductive and deductive reasoning
• Storytelling with data
Generic application areas:
• Type of processes
• Systems and data sources
• Process Management & Improvement
• Finance and Control
Organising and working with Process Mining
• Pilot
• Create value
• Specific in-depth or broad scaling
• Knowledge and skills
• Embedding in the organisation
FOLLOW ON COURSES:
Process Mining level 2: Basic Process Analysis
Process Mining level 3: Advanced Process Analysis