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Data Mining vs Process Mining

Both process mining and data mining can help business leaders gain a competitive edge, but they work differently.

Data mining is specifically looking for relationships in large data sets to gain new insights. By analyzing static data from databases, 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 analyzing large data sets, while business process management is focused on modeling, controlling, and improving business processes. Process mining bridges the gap between the two, combining data analysis with modeling, controlling, and improving business processes.

What is Process Mining?

Process Mining enables you to automatically analyze business processes based on the event logs from company systems (ERP, CRM, Service Management, etc.) to identify specific areas for improvement on the operational level. It is an innovative analytical approach to gaining objective insights and uncovering hidden problems.

Business process management is a closely related practice, but whereas that uses interviewing and other subjective measures, process mining uses concrete data in corporate systems. Process Mining executes a non-invasive procedure, despite how it sounds.

IT department can export the event logs from your IT systems overnight, and then the next day, your team can sit down and feed those exports into your Process Mining software, which will set about creating a visual mapping of your processes in real-time.

This view can then be compared to the map created as part of the BPM cycle, giving you the most accurate picture of where bottlenecks, inefficiencies, or gaps may exist in your processes.

Process mining software analyzes complex processes, such as supply chain management and order to cash. For example, process mining might uncover bottlenecks in the supply chain. And, for order-to-cash processes, it might log and analyze keystrokes to uncover that credit holds for certain customers are causing unnecessary delays.

Pros and Cons of Process Mining

Process mining makes a science out of business process management, but certain prerequisites must be met before it can be applied:

  • Pro: Process mining extracts the data from IT system event logs and renders it practical and usable for enterprise teams.
  • Pro: Process-mining algorithms can make existing process models more accurate and generate hypothetical models of what would happen if a process were changed.
  • Pro: Process mining provides a data-driven view of existing workflows and their outcomes, supplying the enterprise with more objective business intelligence to guide resource allocation, automation initiatives, workflow optimization, and other key business decisions.
  • Con: Organizations need to use specialized tools to deploy process mining because the method relies on advanced data-mining algorithms. Employees don't necessarily need data science backgrounds to conduct process mining, as most process-mining tools automate the application of algorithms and the generation of models.

What is Data Mining?

Data Mining aims to discover patterns in massive quantities of raw data and large data sets to predict future outcomes based on previously unknown relationships within the data.

Data Mining sits at a junction of its own, between statistics and computer science. Data scientists use algorithms to sift and sort through massive amounts of raw data to make sense of what the data is saying. Then they transform it into actionable information for marketing and sales teams, software designers, and nearly every other company department.

Data mining software creates association rules by searching for frequent if-then patterns in the data. An if-then pattern illustrates a variable and a consequence. A simple example would be: If a product goes on sale, then more people will buy it. Companies can use data mining software to make better business decisions.

For example, they can use data mining to uncover commonalities of loyal customers, spot unhappy ones, decide where to place products on supermarket shelves, or predict the risk of giving a loan to a certain customer. Manufacturers use data mining software to improve their product safety, identify issues in quality, manage their supply chain, and streamline operations. Retailers can use a type of data mining called web mining, which uses traditional data mining methods to understand customer behavior and gain insights into a website's effectiveness. Retailers can use information gained by web mining to better tailor their websites to satisfy their customers' desires. Healthcare companies can analyze data to spot fraud, and doctors can use it to understand better how to treat patients.

Similarities between Process Mining and Data Mining

Process Mining is the use of Data Mining techniques and mathematical algorithms to sort out business processes with the end goal of streamlining and simplifying them to benefit the company's bottom line. The essential element Process Mining and Data Mining both works with is the data.

Both of them have emerged as a part of Business Intelligence (BI), providing necessary information for data backed-up business decisions. Based on their origin, it is logical that there is an overlap between Data Mining and Process Mining.

Difference between Process Mining and Data Mining

Data mining and process mining share several commonalities, but they are different. Both data mining and process mining fall under the umbrella of business intelligence. Both use algorithms to understand big data and may also use machine learning. Both can help businesses improve performance.

However, the two areas are distinct. Process mining is more concerned with generating and fitting information into a process, whereas data mining relies on available data. Data mining is more concerned with the patterns themselves, while process mining seeks to answer the why. As part of that, process mining is concerned with exceptions and the story those exceptions help to tell about the holistic answer. In contrast, data mining discards exceptions, as outliers can prevent finding the dominant patterns. Below are some more differences between process mining and data mining, such as:

Process Mining Data Mining
Process mining gives us a true, end-to-end view of business processes by analyzing data derived from the IT systems that support our processes. We use data mining to analyze data and detect or predict patterns, for example, which target groups buy which products, where my marketing campaign has the greatest effect, etc. Data mining has no direct link with business processes instead than process mining. The latter focuses on discovering, controlling, and improving actual business processes.
On the other hand, process mining looks at how the data was 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. Data mining analyzes static information. In other words: data that is available at the time of analysis.
On the other hand, process mining techniques allow you to look for answers to clear and predefined questions specifically. Data mining will look for hidden patterns in data collections but does not answer specific questions.
On the other hand, process mining can provide insight into how results were arrived at. The technique does not search for patterns in the data but for causal processes. A data mining analysis reveals certain patterns but does not answer how those patterns have been established. Data mining is limited solely to the analysis of results.
In process mining, exceptions can sometimes be at least as important. Exceptions may be an early indicator of inefficiencies or opportunities for improvement. It is important to focus on major patterns within a data set in data mining. Data that fall outside these mainstream patterns are often not included in the analysis.

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