maandag 8 december 2014

Let's Disco!


Disco is a process mining tool that let's you discover processes by analyzing event logs. It's suitable for analyzing transactions that happen in a process. For instance, a sales process or a order process are examples that can be analyzed. You can examine bottlenecks in your process, loops, time, durations, averages, fast/slow lanes, conformance issues, resource performance, etc.

So, processmining with a tool like ProM, Disco (and I've even seen a demo of SAS Visual Analytics with a Sankey Diagram) are very well suited for process model analysis. Different tooling are enhancing process analysis. Process analysis can be a great addition to Business Intelligence. Where Business intelligence is more like phishing with a fishing rod in an organisation for KPI's and Business Metrics, Process Mining is much more 'on' the process. Business Intelligence and Process Mining can work together to optimize processes.

A simple tour through Disco

In this blogpost, I've included some screenshots about Disco. I've used a small file that I've borrowed from the Coursera Course "Process mining:Data Science in Action" lectured by TUE, Wil van der Aalst. Below the opening screen of Disco.

The first thing that you have to is load the data. In this case a csv file.

Then you have to set the CaseID, Events, Timestamps and the resources. This is needed for analysis of process.

The next step is importing the data and generating the processmap. Below an example of the duration of the processsteps.

Here an example of the mean of durations

And below, an example when you play the data on the model. The yellow/red dots are markers that flow through the model

 And below some statistical analysis with Disco:

Some more information about the cases.


Disco is a great tool for analysis of processes. Process mining can be a great addition to Business Intelligence and very helpful for analysis of processes. Both analyses processes but on different levels.


1 opmerking:

  1. Hi Hennie,

    Thanks for sharing your inputs. I absolutely agree with what you wrote about adding process mining to BI.
    You can find in my presentation practical examples of such integration. Here is the link: