Data mining vs. process mining: what’s the difference?

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Big data, data mining, business intelligence, process mining…  We are regularly bombarded with these terms, but what do they all mean? In short: they all have something to do with the processing of large portions of data provided to us by our many information systems. But there are differences too.

With data mining we are specifically looking for relationships in large data sets with the intention to come to new insights. By analyzing static data from databases – the so-called big data – we are trying to transform these hidden connections into information that we can use for various purposes. Data mining is applied, among other things, in scientific research, retail and journalism.

Process mining is a relatively new discipline that has emerged from the need to connect the worlds of data mining and business process management. Data mining focuses on the analysis of large data sets, while business process management is focused on modeling, controlling and improving business processes. Process mining bridges the gap between the two, as it combines data analysis with modeling, control and improvement of business processes.

Data mining and process mining: what do they have in common?

Data mining and process mining have a lot in common. Both techniques are part of Business Intelligence, viz. the analysis of large volumes of data in order to achieve greater insights. Both approach things in a similar way. Both data mining and process mining apply specific algorithms to data in order to uncover hidden patterns and relationships. The ultimate goal of data mining and process mining is to provide insight and to let users come to better decisions.

Data mining vs. process mining: what are the differences?

Patterns versus processes

We use data mining to analyze data and to detect or predict patterns. For example: which target groups buy which products, where does my marketing campaign have the greatest effect, etc ... Data mining has no direct link with business processes, as opposed to process mining. The latter focuses on discovering, controlling and improving actual business processes. By analyzing data derived from the IT systems that support our processes, process mining gives us a true, end-to-end view of how business processes operate.

Static versus dynamic

Data mining analyzes static information. In other words: data that is available at the time of analysis. Process mining on the other hand looks at how the data was actually created. Process mining techniques also allow users to generate processes dynamically based on the most recent data. Process mining can even provide a real-time view of business processes through a live feed.

Arbitrary versus specific

Data mining will look for hidden patterns in data collections, but does not answer specific questions. Process mining techniques on the other hand allow you to specifically look for answers to clear and predefined questions.

Results versus causes

A data mining analysis reveals certain patterns, but does not answer the question of how those patterns have been established. Data mining is limited solely to the analysis of results. Process mining on the other hand can provide insight into how results were arrived at. The technique does not search for patterns in the data, but for causal processes.

Mainstream versus deviations

In data mining, it is important to focus on major patterns within a data set. Data that fall outside this mainstream patterns are often not included in the analysis. In process mining, exceptions can sometimes be at least as important. Exceptions may be an early indicator of inefficiencies or opportunities for improvement.

Want to know how you can apply process mining for process optimization in your company? Then contact one of our process mining consultants.

Dennis Houthoofd - December 11, 2015