In them, many algorithm participants will build interaction, and not within the framework of one company. A community of independent performers will emerge, ready to perform various tasks in a distributed business process; it will be formed autonomously.
Prospects of Process Mining Methodology
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The most effective business process options can be identified among the vast number of existing ones. This will allow searching for and analyzing optimal work algorithms in colossal volumes of data.
However, this futuristic forecast refers to the distant future. In the next decade, Russian organizations will have to analyze internal business processes integrated into ERP systems or electronic document management structures using PM tools.
Process Mining is a methodology for studying workflows that has the potential to significantly increase the productivity of existing business operations. Although this approach is not a panacea for all the difficulties in the field of business analytics, it will undoubtedly find followers and occupy a certain niche in the domestic information technology market.
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Frequently Asked Questions about Process Mining
Successful implementations of Process Mining demonstrate italy business mailing list that companies are capable of achieving significant cost reductions, increased process efficiency and improved product/service quality.
What is the difference between Process Mining and Data Mining?
Data Mining:
It is mainly used to identify hierarchical relationships in large data sets. For example, analyzing which customer groups prefer certain product categories in different sales channels and with what frequency.
Tables with diverse information from any area are used as source data.
Operates on multidimensional representations (cubes) that allow changing the level of detail (different degrees of aggregation) of the material.
Process Mining:
Focuses not on the semantic connections between data, but on their interpretation as processes.
The source materials are transaction records on accounting objects. Typically, these are tasks, orders, requests, work orders, etc. Examples of transaction data may be event logs, audit records, information on cases and states of objects (including their status or change of the responsible department).
Uses sampling methods to create a process model based on the most representative scenarios. Process Mining does not simply look for relationships between data. Its goal is to determine the relationships between process steps, deviations from normal flow, factors influencing these changes, the efficiency of the algorithm, its variability, and its bottlenecks.
What are the differences between Process Mining and BI (Business Intelligence)?
Process Mining and Business Intelligence share the same goal of helping companies make effective decisions based on factual data. However, these tools differ in the depth of process analysis:
BI allows you to detect and visualize errors, but determining the root cause of problems requires expert interpretation.
Process Mining analyzes not individual facts, but sequences of events from different information systems, which allows us to answer not only the question “how”, but also “why”.
Traditional Business Intelligence is unable to display:
Discrepancies between actual and planned business processes.
Alternative implementation options for algorithms.
Various deviations from the process (skipping stages, looping, blocking).
In essence, Process Mining is an advanced, algorithm-oriented version of BI. It not only visualizes data, but also solves specific problems and identifies the roots of problems in detail.
When is it appropriate to use Process Mining?
In principle, PM can be applied in any situation where the activities of employees are reflected in information systems and one of two conditions is met:
We are talking about simple mass processes performed thousands (tens, hundreds of thousands) of times daily. For example, customer service in a bank. Here the sequence of actions is honed to the smallest detail, and the main attention is paid to the efficiency of individual employees and departments. Although it is unlikely that it will be possible to radically change the mechanics of the algorithm, optimization of each link can give a significant overall effect.
Complex, extended individual processes are considered, covering the activities of many departments. In this case, Process Mining is aimed not so much at acceleration, but at simplifying the algorithm, optimizing the number of stages, eliminating defects, unnecessary cycles, unjustified involvement of new participants, etc.
In both scenarios, the use of PM can bring tangible economic benefits by increasing the efficiency and transparency of business processes.
How do Process Mining and Machine Learning interact?
Machine learning is often integrated into the process of algorithm mining. Using Machine Learning technologies allows not only to record a fact or identify a "process word", but also to deeply penetrate the essence of the problem. In the context of Process Mining, the following machine learning methods are most often used:
Association rule mining—the automatic identification of basic and specific process paths—helps to understand typical and non-standard task execution scenarios.
Robust methods—automatically detecting outliers in time, cost, and frequency—can identify abrupt changes that may not be noticeable when data is aggregated over long periods.
Time series analysis is the prediction of the process execution time and the acceptable range of deviations. This makes it possible to estimate the limits of the algorithm variability and determine the need for intervention.
By what parameters can Process Mining systems be compared?
Modern PM tools are compatible with various systems (ERP, CRM, etc.) and can be integrated into any existing IT infrastructure. When choosing a supplier, pay attention to the following functional capabilities:
Analyze event logs to identify real business processes.
Conduct compliance checks to detect anomalies.
Providing information about employees who deviate from standard procedures.
Integration with existing software and IT infrastructure of the company.
Among the leading developers of Process Mining class systems, we can highlight:
Celonis (Germany).
Lana Labs (Germany).
Minit (Slovakia).
QPR (Finland).
"Infomaximum" (Russia) - included in the register of domestic software.
Signavio (Germany).
Disco (Netherlands).
In the conditions of increasing competition and market instability, the use of PM is becoming a key element of a successful business strategy. Following this trend, Russian companies can reach a new level of efficiency and competitiveness, ensuring a stable position in the domestic and global markets.