Efficient Processes: The Foundation of Successful Organizations in the 21st Century

In the 21st century, efficient processes are one of the main components of successful organizations. As enormous amounts of process-related data are stored everywhere, the possibility to analyze and improve processes gave rise to the field called process mining, aimed at discovering useful insights from processed data. This field was initially focused on conventional business processes like ordering or producing goods, but with the emergence of the Internet of Things, event data now comes in many different types and flavors.

The Emergence of Event Data

Event data is literally infinite in its variety, as a lot of things around us are occurring data about events that happen over time. This has opened up new possibilities for analysis and improvement of processes. In this course, students will learn about the different components of event data and how to create pre-process and analyze them.

Components of Event Data

Event data consists of three basic components: what, why, and who. When a patient enters an emergency department, it becomes an instance of the emergency process. Similarly, when a train leaves the terminal in the morning, it's an instance of the railway operating processes. The process instance, also known as a case, is why events happen because a patient needs to be treated or because a train needs to bring passengers from point A to B.

Events and Activities

When an event is recorded, something has happened. This is what we call an activity. An x-ray scan or a treatment with a certain medicine are examples of activities in a hospital context. Securing a railway track for an approaching train can be an activity in a railway environment. The hook component of event data shows us who is responsible for certain events, such as a doctor, nurse, or train driver.

Resources and the Hook Component

The hook component of event data also includes machines or information systems that execute events. We will refer to these resources collectively as resources. When an action is recorded in an activity, it's essential to identify the specific resource responsible for the action.

Analyzing Event Data: An Iterative Process

Analyzing event data is an iterative process of three steps: extraction, processing, and analysis. The first step is data extraction, where we extract the raw data from one or more information systems and transform them into event logs.

Pre-processing the Data

The second step is pre-processing the data. This involves aggregating the data by removing unnecessary details and focusing on specific parts of the process. We can also enrich the data by adding calculated variables to make it more meaningful.

Analyzing the Data: Three Perspectives

Once we have pre-processed the data, we can analyze it using three different perspectives:

1. The organizational perspective focuses on the actors of the process, such as the roles of different doctors and nurses in an emergency department and how they work together.

2. The control flow perspective focuses on the flow and structured workflow of the process, what is the journey of a patient through the emergency rooms.

3. The performance perspective focuses on time and efficiency, how long does it take before a patient can leave the emergency department or in which area at what time of day or trains are most delayed.

Combining Different Perspectives

We can also combine different perspectives to investigate whether there are links between actors and performance issues. Additionally, we can include additional data attributes such as the cost of activities or types of customers to make our analysis more comprehensive.

Let's Have a Look at Some Examples

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execute events we will refer to them collectively as resources and if antis does a recorded action of an activity the what occurring for an in the why by specific resource the who analyzing event data is an iterative process of three steps extraction processing and analysis first is data extraction extracting the raw data from one or more information systems and transforming them to event logs second is pre-processing the data here we aggregate the data by removing to date detailed information we subset the data allowing us to focus on specific parts of the process but we can also enrich the data by adding calculated variables eventually in the third stage we will analyze the data three perspectives can be distinguished firstly the organizational perspective focuses on the actors of the process for instance which are the roles of different doctors and nurses in our emergency department and how do they work together secondly the control flow perspective focuses on the flow and structured nosov the process what is the journey of a patient through the emergency rooms and finally the performance perspective focuses on time and efficiency how long does it take before a patient can leave the emergency department or in which area at what time of day or trains most delayed furthermore we can also combine different perspectives for example investigate whether there are links between actors and performance issues an additional data attributes which are available such as the cost of activities or types of customers can also be included let's have a look at some exampleshello and welcome to the first lesson of this course on process analytics my name is hector sulla and i will be your instructor efficient processes are one of the main components of successful organizations in the 21st century as enormous amounts of process related data are stored everywhere the possibility to analyze and improve process gave rise to the field called process mining aimed at discovering useful insights from processed data while it all started with conventional business processes like ordering or producing goods event data now adays comes in many different types and flavors with the emergence of the Internet of Things a lot of things around us are occurring data about events that happen over time as a result the types of event data that you can analyze is literally infinite in this course you will learn about the different components of event data and how to create pre-process and analyze them event data consists of three basic components the Y the worth and who events happen because of a certain object a process instance when a patient enters an emergency department it becomes an instance of the emergency process when a train leaves the terminal in the morning it's an instance of the railway operating processes the process instance also called the cases is why events happen because a patient needs to be treated or because a train needs to bring passengers from point A to B when an event is recorded something has happened what has happened is what we call the activities activities are the steps of a process an x-ray scan or a treatment with a certain medicine or board activities in a hospital context securing a railway track for an approaching train can be an activity in a railway environment finally the hook component of event data shows us who is responsible for certain events a doctor or a nurse or train driver of single house operator it don't always have to be real persons also machines or information systems can execute events we will refer to them collectively as resources and if antis does a recorded action of an activity the what occurring for an in the why by specific resource the who analyzing event data is an iterative process of three steps extraction processing and analysis first is data extraction extracting the raw data from one or more information systems and transforming them to event logs second is pre-processing the data here we aggregate the data by removing to date detailed information we subset the data allowing us to focus on specific parts of the process but we can also enrich the data by adding calculated variables eventually in the third stage we will analyze the data three perspectives can be distinguished firstly the organizational perspective focuses on the actors of the process for instance which are the roles of different doctors and nurses in our emergency department and how do they work together secondly the control flow perspective focuses on the flow and structured nosov the process what is the journey of a patient through the emergency rooms and finally the performance perspective focuses on time and efficiency how long does it take before a patient can leave the emergency department or in which area at what time of day or trains most delayed furthermore we can also combine different perspectives for example investigate whether there are links between actors and performance issues an additional data attributes which are available such as the cost of activities or types of customers can also be included let's have a look at some examples\n"