How is Process Mining Different From…
Illustration by Wil van der Aalst
We humans are great at deriving concepts. It starts when we are a kid and learn about categories like cats and dogs, or tables and chairs, just by being exposed to many examples. So, if we meet something new, we try to put it into perspective with what we already know.
People who hear about process mining for the first time need to understand how it is different from what they are already familiar with. This is why we have frequently written about how process mining relates to other technologies like data mining or BI on this blog.
However, over time these old blog posts are a little more difficult to find, and since I keep getting these questions I thought it would make sense to revisit them in an overview article.
Above, you see a picture that Wil van der Aalst uses to explain what process mining is: In essence, process mining bridges traditional process analysis techniques like modeling (which are not based on data) and data-driven techniques like data mining (which are not process-oriented).
Read on to learn in more detail how process mining relates to specific techniques and technologies.
1. Business Process Management
The BPM life-cycle shown above is often used to describe how BPM iterates through multiple phases in designing, implementing, analyzing, and then re-designing the processes.
Process mining clearly fits into the analysis phase of the BPM life-cycle. While traditional BPM approaches start with modeling the process, process mining starts by understanding the processes that are already there by discovering the actual processes from data.
Note that BPM is not about implementing BPM systems. Instead, BPM an activity, a practice, about improving processes and might not involve any technical system implementation at all.
2. Lean Six Sigma
Process mining as a technology is agnostic to the specific process improvement (or auditing, risk management, etc.) methodology that is used with it. One popular method that is used in many organizations today is Lean Six Sigma and one of the common approaches is the DMAIC (Define, Measure, Analyze, Improve, Control) approach illustrated above.
Process mining can be used in the ‘As-is’ analysis phase to identify waste and improvement opportunities much faster and more accurately than it would be possible with a manual process mapping approach, but it also provides the opportunity to repeat the process analysis and help with controlling and sustaining the change (something that is otherwise rarely feasible today).
3. Data Mining
Data mining is much older than process mining and, like shown in Wil’s picture above, rarely focusses at processes. A typical data algorithm can be used to derive rules from data about, for example, what people are buying together in a supermarket, or predict in which suburb a marketing campaign would be most effective.
Process mining, like data mining, uses data but discovers and analyzes process models to understand how the ‘As-is’ processes look like. Research-wise, process mining comes more out of the BPM community than the data mining community. There are many possibilities to combine the two areas as they are largely complementary.
4. Business Intelligence
Business intelligence tools and dashboards have to deal with many of the same challenges that process mining has to deal with when analyzing end-to-end processes. For example, data must be pulled together from multiple IT systems and process mining can often benefit from these existing data preparation routines (many of our customers use Disco based on data from the same data warehouse that also feeds their BI dashboards).
These dashboards then analyze specific, pre-programmed Key Performance Indicators (KPIs), but they do not show how the processes work. Process mining is a complementary tool that allows to analyze the processes and find out the root causes of why the KPIs are out of bound. That’s why we sometimes compare BI to a fever thermometer (showing you whether you are sick) while process mining is like an x-ray (looking inside to see what is actually going on).
While people sometimes mistake our process mining animation for simulation, you could almost say that process mining is the opposite of simulation: Process mining starts with the current behavior and automatically discovers a model to show how the process really looks like. Simulation starts with a model and allows to explore alternative ‘what-if’ scenarios.
One big challenge for business process simulation is of course that you need to have a good model of reality to start with. Here, combining process mining and simulation (taking output from a process mining tool and use it as input in a simulation tool) provides powerful opportunities to get a more accurate starting point for the simulations.
6. Big Data
There is still a lot of buzz around Big Data, and one of the challenges that I see people having with Big Data is to extract value out of it. Process mining as a new data analysis technique that is focused on processes provides clear benefits for anyone who seeks to understand their underlying business processes in the pile of data (big or small) that accumulates.
One thing that Big Data has achieved is that it has raised the awareness about how much data there is today. Ten years ago, nobody believed that they had the data to do process mining. Nowadays, when you show process mining to someone they almost instantly recognise the opportunities and think of the data they could analyze with it.
Excel and other query tools (or auditing tools like ACL and IDEA) can be powerful at answering questions that you already have. But they do not allow you to discover new things that you would have never thought of. Furthermore, just like data mining and BI tools they do not provide you with a process perspective.
For concrete examples see this article on Why Process Mining is better than Excel for Process Analysis.
What other techniques or tools would you like to see compared to process mining? Let us know in the comments!