Data Mining Vs. Process Mining

process-mining

Data mining is a part of Business Intelligence (BI) that seeks to understand relationships and patterns in large datasets found in big data. Big data is a term referring to massive databases of both structured and unstructured static information that can be exploited for business intelligence needs. This data can have the potential for improving business operations. The data is mined from such sources as emails, data storage, phones, applications, and databases. This data is mined, processed, and analyzed. Companies gain insights from these harvested data.

Process mining seeks to understand real-time procedure steps to detect inefficiencies or make improvements in the accomplishing of a business task. Process mining is the analyzing and monitoring of business processes. Data is gathered through, or mined from, corporate information systems which displays the actual process. It does this by capturing a time-stamp and an event log of each of the process steps. The process mining is accomplished by using strong algorithms combined with advanced data transformation enabling the discovery and improvement of the business processes.

Similarities

There are similarities between data mining and process mining. Both are a subset of business intelligence (BI) and both access large volumes of data to achieve information for action. Both use algorithms to obtain hidden patterns and relationships within the data.

Differences

Data Mining Finds Static Data

Data mining is static and used by corporations to analyze big datasets to predict business patterns. The data analyzed is harvested from static datasets such as databases, which are available records. It looks for things like what group of consumers will buy what product, or where does a marketing effort have the greatest impact. Data mining has no concern with business processes.

Process Mining Finds Dynamic Data

Process mining is dynamic and gathers needed information from created actions. It can be from real-time events provided through a live feed. It looks for steps that are inefficient or time-consuming to control and improve those steps. It reveals a true, end-to-end process.

Data Mining Looks At Arbitrary Data

Data mining obtains information from what happens to be available. Data mining arbitrarily gains information from large databases without targeting a specific inquiry.

Process Mining Looks At Real-Time Data

Process mining targets a specific question about a process. Process mining gets current activity.

Data Mining Looks At Results

Data mining can only look at the results of available data. It cannot answer how those data came to be.

Process Mining Looks At Causes

Process mining can see the cause of actions.

Data Mining Analyzes Patterns

Mainstream patterns are analyzed by data mining. Exceptions to those mainstream patterns are not considered for analysis.

Process Mining Sees Exceptions

But exceptions and irregularities can be very useful for the process mining technique. They could provide clues to what is not working well and what needs improvement.

Conclusion

Both data mining and process mining serve important purposes in the realm of business intelligence (BI). They are necessary for successful, efficient business operations. Data mining provides the source of market knowledge for companies to make smart decisions. The analyzed results are applied in various industries such as retail, journalism, and scientific research. But process mining provides the knowledge of operations that help companies improve and function smartly.

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