Process Mining vs. Data Mining: 4 Key Differences

When it comes to data-driven decision-making, businesses face the challenge of choosing the right technologies. Two of the most popular technologies are process mining and data mining.

Data mining is a process of analyzing large datasets to extract patterns and relationships. It involves using statistical and machine learning techniques to uncover insights that can inform decision-making.

Process mining, on the other hand, is the analysis of event logs generated by business processes to identify inefficiencies, bottlenecks, and opportunities for improvement. This approach uses specialized algorithms to construct process models that visualize the flow of activities within a process. Allowing organizations to identify areas where the process can be optimized and improved.

Process Mining vs. Data Mining

Process mining focuses on analyzing business processes by extracting data from event logs. It provides a detailed view of how processes are executed in real-time. Thus, helping users to identify inefficiencies and opportunities for improvement. Business process mining involves the discovery, monitoring, and improvement of real processes by extracting knowledge from event logs readily available in today’s information systems.

Data mining, on the other hand, is the process of discovering patterns in large data sets involving machine learning, statistics, and database systems. It is primarily focused on generating new insights that can be used to improve business processes. 

In this blog post, we’ll highlight four key differences between these technologies to help you make an informed decision for your organization.

Learn More: What is Process Intelligence?

End-to-end process view vs. Patterns    

Process mining looks at understanding how processes work and identifies areas for improvement. It focuses on identifying bottlenecks and inefficiencies. Business process mining identifies process-related insights to understand what drives processes and how they can be improved to achieve desired outcomes.

Data mining provides insights into patterns and relationships in data, helping in identifying customer behavior, sales trends, and market opportunities. It aims to extract information from data and transform it into an understandable format.

Learn More: What is Process Mining?

Insights vs. Predictions

Process mining offers high-level insights into potential inefficiencies, such as identifying redundancies in identical operations occurring in different business units.

It provides insight into the implementation of a procedure by displaying the steps taken, length of activities, and occurrences of deviations. Business process mining provides both macro and micro level insights that can be used for process optimization.

Data mining, on the other hand identifies insights and makes predictions based on the data. It provides a way to extract useful information from large datasets by discovering underlying patterns.

Learn More: What is Business Process Mining?

Structured vs. Unstructured Data

Process mining requires event logs that capture the activities and interactions within a process, which can be generated from various sources, such as ERPs, CRMs, and workflow management systems.

Data mining, on the other hand, can use any type of data, including structured and unstructured data. This includes looking at data from customer reviews, social media, web analytics etc.

Use Cases

Process mining requires specialized tools to visualize and analyze business processes. This technology is mostly used in industries such as manufacturing, healthcare, logistics, and finance to optimize business processes.

Data mining, on the other hand, can be done using a variety of tools, including Excel, Python, and R. Generally used in industries such as retail, banking, and telecommunications for fraud detection, customer segmentation, and predictive maintenance.

Getting Started with Process Mining & Data Mining

Today, over 60% of the teams’ workday is spent on unstructured interactions in documents, emails, communications, custom applications, and websites – outside of ERP, CRM, and other systems of record. This massive unstructured and undocumented interaction dataset between people and software is untapped and contains a goldmine of insights that could give a significant competitive edge to enterprises. Traditional task solutions used for identifying and building automation pipelines have significant privacy, accuracy, and scalability issues and fail to capture work done outside the systems of record, such as ERPs and CRMs. 

Through Soroco Scout^TM, powered by the world’s first work graph platform, organizations can unlock this data source to discover processes where inefficiencies occur and are prime candidates for intelligent process automation.

Request a demo of Soroco Scout^TM today!