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BI, Data Mining and Process Mining: how to make the difference?

Difference between BI, Data Mining and Process Mining

More than ever, data helps leaders to make the right decisions (evidence-based insights).

Many solutions occurred recently, and it is not always easy to decide on which one to go and what for. You’ll find here inputs and differences between what really are Business Intelligence, Data Mining, and Process Mining.

Business Intelligence

Business Intelligence captures data from anywhere in the company and formalizes them in analytical findings reports, summaries, dashboards, graphs to provide users with a useful overview of their state of art.

In other words, BI refers to solutions that give you access to easy-to-digest data information.

Examples of common applications for BI are:

  • Sales intelligence: it for example helps leaders to predict their revenue with a statistic on conversion rate, geographical aspects, types of customers, etc. You can also identify what is your right persona, customers.
  • Activity reporting: it helps leaders to visualize in clear dashboards what is going on in the field: you can then compare it to your objectives and see the gaps between the two.

Data Mining

Data Mining is probably a step further. It is based on a large and very often complex set of data and transforms it into intelligent correlations, patterns thanks to statistics and pattern recognition methods.

Examples of common applications for Data Mining are:

  • Marketing: it helps people to divide their customers into the accurate segment and have more effective marketing campaigns.
  • Fraud detection: Data mining builds patterns of fraudulent and non-fraudulent behavior to anticipate and supervise a wide range of the population.

Process Mining

Process Mining is a data-based technology that allows leaders to monitor in real-time what is happening in their processes. It gives them inputs on deviations, bottlenecks, reworks that are currently occurring and that might have an impact on their business. It gives also a deep level of analysis based on root causes and for some of them they can predict what will happen in their processes in the future.

Examples of common applications for Process Mining are:

  • Automation: by highlighting reworks and recurring tasks, it helps managers to identify where to automate and measure the results.
  • Supply Chain, Finance, etc: in very specific areas it helps people to easily identify process deviations and optimize them.

In short

Data is key to make smart decisions. Business Intelligence allows simple and adaptable visualization of the data. Data Mining highlights correlations and predictions. Process Mining helps you visualize in real-time your processes to automate and optimize your business.