Let's Disco!

Introduction

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.

Conclusion

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.

Greetz,

Hennie

Process mining

Introduction

Currently following the course processmining on Coursera and this course is combination of data mining and processmodels. Like the same as with Business intelligence (sort of), processmining analyzes data about processes in an organisation. I'm quite enthusiastic about this approach because it analyzes processes on a scientific manner with data mining. Datamining analyzes data but not with the (direct) aim of looking at processes in an organisation. Processmining does. There is also a relation between BPM (Business Process modelling). In the course sometimes BPM models are used aside petrinets.

Although I've just started with the course, I want to share some interesting things, I've come along during the course. In this blog post I'll describe this.

Defining process mining

The definition of Processmining according to Wikipedia :

"Process mining is a process management technique that allows for the analysis of business processes based on event logs.".

But in my opinion event logs is a bit of a narrow keyword. A more broader definition could be applicable. Think about facts in a star schema or satellite information in a Datavault model that are very often used in business intelligence and data warehousing. These are transactions that happens in the operations of an organisation These are also events. Events that happened. Think about an order entry system with statuses. Sometimes, customers told me how the order entry process worked and when I studied the different statuses an order should have, I sometimes found out that different sequences of the order process were possible. With process mining you can identify undiscovered routes of your business process in automated way. This is truly an addition in the  field of Business intelligence, Lean Six Sigma,  datamining and BPM.

Just a simple example, suppose from the customer you hear that the model is this (orders with order statusses):

But when we study the transactions of the order entry system the following is noticed (records are identified by a case ID (the grouping of the records) and the activity at a certain moment):

Here we see that order 4568 is reopened and this should not have supposed to happen according to the designed model. After analyzing the events in a log or perhaps a transactional modelled star schema the model appears like this (corrected):

It could mean that in the operational process order entry personnel has reopened the order for some reason. If you want optimize the process in order to reduce the wastes (Lean Six sigma) than this is very interesting information. Process mining can do this for you.

Conclusion

Although I've just started with studying process mining, this seems a very interesting approach for analyzing processes with datamining. And, this is also applicable on huge log files and analyzing log files is one of the applications of Big data analytics. 

Hope you have read this blogpost with pleasure..

Greetz,

Hennie