Process mining is an “analytical method”. The mere introduction of a process mining tool doesn’t start anything. You will need to plan a series of steps as an “analytical project” and manage their execution.
However, if you have not done any research or analysis projects in the past, it does not seem to be easy to understand the steps of an analysis project. Therefore, I would like to explain the flow of process mining analysis by contrasting the flow of cooking.
First, let’s see the flow of the food. The assumed location is the kitchen of a restaurant. The first activity is “purchasing foodstuff” and the last is serving dished-up food to customers’ tables.
COOKING FLOW
1 Purchase of foodstuffs
purchase a variety of food from all over the world through food wholesalers.
2 Foodstuff
The foods to be cooked are now available. Check to see if there are any insects eating or rotting.
3 Precooking
prepare the food by chopping it with a knife or boiling it in a pot of boiling water to remove the bitterness.
4 Cooking
Cooks food using a variety of cooking utensils.
5 dishing-up and serving
dish up cooked foods and serve the finished dishes to the customers.
Role of Master Chef
Note that the role of the master chef is to oversee the entire cooking process of the restaurant.
Next, let’s explain the steps of the process mining analysis, corresponding to the above cooking steps.
process mining procedure
1 Extraction of data = Purchase of foodstuff
extract data from various systems that record and accumulate event logs that are the target data for analysis, such as ERP represented by SAP, CRM systems such as Salesforce, or proprietary business systems.
As a method of data extraction, it is common to extract data directly from a DB by SQL.
Data extraction is basically done by system engineers or system administrators, and when the database structure is complex, such as ERP, it is necessary to determine where the data to be analyzed is located, for example, with the assistance of SAP experts who have good knowledge about SAP.
2 Data to be analyzed = Foodstuff
The data extracted from the system is collectively referred to as the “event log. This is because the history of operations on the system is recorded on an event-by-event basis with a time stamp.
As a data format, it would be easier to pre-process the data in the post-process if it were provided in CSV format. In some cases, the event log may be provided in JSON format and the pre-processing of the event log in JSON format can be a bit cumbersome.
3 Data preparation = Precooking
The event log data extracted from the system is often composed of multiple files, often ten or more. It can be a file that records activity and time stamps, etc., as well as a file that contains the master data.
Basically, all the files must be combined into a single file in order to analyze by a process mining tool. In addition, the original files contain a lot of data that cannot be analyzed as it is, such as garbled parts and empty cells that should have contained some kind of value.
Therefore, it is necessary to remove or adjust for those noisy data, that is, perform data cleaning similar to the removal of unfavorable parts of food. Data preparation is the process of processing the original data into clean data that can be analyzed by a process mining tool
Data preparetaion is done by data scientists who know how to process data to make it clean, using ETL tools, Python, and other tools, languages.
4 Analysis = Cooking
Once the data has been pre-processed and the clean data is ready for analysis, it can finally be fed into process mining tools for various analyses.
The process mining tool is a very versatile tool. It takes some training and experience to become proficient, but it’s fun to visualize business processes as a flowchart from event log data that looks like nothing more than a litany of numbers to uncover inefficiencies and bottlenecks.
Analysis with process mining tools requires tool experts who are familiar with the tools used, but it is the process analyst who gives the analytical perspective on how to do the analysis. The data scientist also has a better understanding of the original data through pre-processing of the data, so they can assist in the analytical work.
5 Reporting = Dishing up and serving
create reports using graphs, tables, etc. on the issues and problems of the target process identified from various analysis results with process mining tools. Since the people receiving the report are not necessarily familiar with data analysis, it is necessary to keep in mind the visual presentation that makes it easy to understand what the issue or problem is.
Ideally, the report should be written by a process analyst, with the assistance of a process consultant with process improvement know-how (Lean, Six Sigma, etc.). It’s also good to have the support of a data scientist or tool expert, as additional analysis may be required.
Role of Project Manager
It is the project manager who correspond to the master chef of the restaurant who runs the entire process mining analysis project. A project manager does not have to be familiar with the entire process. However, you must have a good understanding of each step of the process and above all, you must have the skills to execute the project smoothly.
So far I have used the culinary metaphor to explain the standard procedure for process mining analysis. Each process is a highly challenging one that requires a certain level of skill and experience, so it is necessary for experts in each field to work well together to advance the project.
このように、プロセスマイニングとデータマイニング・AI、BPMはお互いに補完しあえる関係にあると言えます。プロセスマイニングのゴッドファーザー、Wil van der Aalst教授は、「プロセスマイニングは、データマイニングとBPMをつなぐ橋である」と述べられていますが、まさに、BPMの取り組みにおいて、プロセスに特化したデータマイニングとしての「プロセスマイニング」は大きな役割を果たしていくと思われます。
