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Data sources for Process Mining technology

Posted: Sun Dec 22, 2024 7:09 am
by Mimakte
A high level of process automation is a key factor for using PM technologies. Data sources can come from various systems. The most effective are those whose architecture was initially designed to support processes and manage workflows.

These include Process Mining systems of the ERP/EAM, CRM, BPMS, ITSM class. These platforms store historical data on changes in the state of objects, be it assets, work orders or user requests.

Data sources for Process Mining technology

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In fact, information for process analysis can be extracted from any transactional system. Process Mining tools can work with data from the following classes of systems:

data warehouses;

OLAP repositories;

enterprise resource planning (ERP) systems;

customer relationship management (CRM);

processing of applications;

Application Performance Monitoring (APM);

error tracking;

management: product data (PDM);

documents (DMS);

IT services (ITSM);

software configuration (SCM);


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projects.

The main condition is the presence of multiple event logs italy business mailing list and traces in the system - the actual process is reconstructed using them.

The key parameter for assessing the suitability of a source system for the application of the PM methodology is the answer to the question: "Is it possible to reconstruct a picture of the process execution based on the collected information?"

The effects of the implementation of process analysis tools, it becomes obvious that this technology is rapidly evolving. This is due to its practical use in real conditions of commercial organizations.

It is safe to say that as automation of current business operations increases, the number of adherents to Process Mining methods will steadily increase, since optimization of operational activities is a key trend in modern entrepreneurship.

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Stages of implementation and configuration of Process Mining tools
For more productive use of Process Mining, we recommend following these steps:

Step 1: Selecting the optimal solution
There are about fifteen business process analysis systems on the market today, mostly from American and European developers. When choosing, two key aspects must be taken into account:

Recently, there have been cases when foreign suppliers of various software stopped functioning of cloud services in Russia, stopped technical support of their products. Therefore, it is recommended to pay attention to domestic platforms, among which there are quite mature and reliable solutions.

For large corporations, the ability to deploy a system on their own servers (which is not always possible), the availability of integrations with existing enterprise information systems, flexibility in customizing and adapting the solution to the specific tasks of the company are often critically important.

Step 2: Process Identification
It is recommended to start with the process that has the highest degree of digitalization: only in this case the system will be able to conduct its full analysis. But ideal conditions for such a study are rare.

Step 2 Process Identification

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A fairly typical situation is when a process is only partially digitized or is conducted in several unrelated information systems. In this case, it is necessary to analyze it step by step and gradually transfer the "analog" components to digital format. This will allow, over time, to cover the entire process and obtain a more complete picture for optimization.

Step 3: Data accumulation
The analytical base may consist of the following sources of information:

corporate platforms (such as SAP, 1C, Salesforce);

relational databases (PostgreSQL, MySQL and others);

various file depositories;

event logs generated from electronic correspondence or spreadsheet documents during the work of personnel.

There is no need to concentrate all data in a single repository. If necessary, it can be exported using special connectors, spreadsheets, or by directly connecting to databases.

Naturally, the question of the optimal volume of material arises. The principle "the more, the better" works here, but usually a sample for a year and a half of operation is enough. For mass and cyclic processes, even a month or a week of information may be enough.

Step 4: Assessing the quality of information
For effective classification, data must have certain characteristics. The most important of these are:

unique identifier of the process instance (this could be the number of a purchase, contract, application or request to the technical support service);

stage name (may include status, department, or a combination of department and status - essentially, an identifier for a specific action);

chronological marker.

Additional attributes may include performers, branches, geographic regions, various types and attributes, and in some cases even financial amounts. It is important to understand that the more characteristics you can collect, the wider the range of hypotheses for potential process improvement.

Stage 5: Project Initiation
In an optimal scenario, by this point all process segments should be digitized and have an end-to-end instance identifier.

The next step is to set up direct access to the event log data of the selected process through integration with source systems. After this, a full-fledged opportunity for its deep analysis, identification of potential optimization points and daily monitoring of the effectiveness of the implemented changes opens up.