Process Mining vs Data Mining vs Business Process Management
Share on social media
Process Mining? Data Mining? You hear the three terms mentioned during meetings, but - to be honest - you’re not sure whether they are not actually the same thing. Well, they’re not.
Table of Contents
And where does even Business Process Management (BPM) fits in?
Here we are, demystifying these terms, explaining where they do indeed overlap, and where they definitely diverge.
Process Mining Definition
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 gain objective insights and uncover hidden problems.
Process Mining executes a non-invasive procedure, despite how it sounds.
An IT department can export the event logs from your IT systems overnight, 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 that was created as part of the BPM cycle, giving you the most accurate picture possible of where bottlenecks, inefficiencies, or gaps may exist in your processes.
Data Mining Definition
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.
There is both art and science involved.
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 in order 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 department of the company.
Data Mining example: When companies like Facebook and Google have anonymous data relating to internet users browsing habits, it’s the Data Mining teams who take that data and tell the ads team where to target ads for hair care products vs home improvement products.
Process Mining vs. Data Mining: So, What Do They Have in Common?
In short, Process Mining is the use of Data Mining techniques and mathematical alghorithms to sort out business processes with the end goal of streamlining and simplifying them to the benefit of the company’s bottom line.
The essential element Process Mining and Data Mining both work 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.
Process Mining vs. Data Mining: What Is the Difference?
There is one area where Data Mining shows its weakness. You have probably guessed it right - processes.
In contrast to Data Mining, Process Mining looks at a particular process and its performance/execution within a specific time frame in order to re-create an as-is process map including all the single steps that have been performed.
The business value in Process Mining lays in highlighting all the bottlenecks, unproductive variants, deviations, and rework. These insights are later used for further process improvement.
Data Mining, on the other side, analyzes large data sets and searches for general rules, providing predictions and behavior patterns based on the input data.
The Role of Business Process Management
Business Process Management is the operations level view of the processes being performed by the company. The BPM lifecycle generally includes the following stages: design, modeling, execution, monitoring, optimization, and reengineering. This is an ongoing process often managed by a dedicated business process manager and an interdisciplinary team.
BPM can be applied to the manufacturing floor, the finance department, and IT equally, as all departments use processes to make work happen. This is the framework within which these processes are created and managed.
Process Mining vs BPM
Process Mining emerged several years ago from the BPM world, integrating the use of algorithms from Data Mining to analyze the event log data that exists in most IT system within a company.
Historically, BPM stages were managed manually. Even the process analyses involved employee interviews and models being drawn up using modeling software.
This is where Process Mining gains plus points, making the BPM lifecycle much more efficient and effective by taking the routine work off of your team’s hands, allowing them to the higher level modeling and re-engineering work.
The essential difference between Process Mining and BPM lies within the outcome. While BPM cycle gives you the map of the ideal process (also known as to-be process), Process Mining gives you the map of the actual process (the so called as-is process).
How Process Mining, Data Mining and BPM Interact Together
When it comes time to analyzing the processes put in place during your most recent BPM cycle, process management techniques and software automate the routine task - formerly manual - of establishing a real-world map of your processes in action.
BPM gave you the perfect world outline, Process Mining gives you the version after humans get their hands on it and points out the highs and the lows of the process.
Using full strength Data Mining on your process analysis would entail hiring PhDs in computer science and turning them loose on all your company’s data. Then waiting for them to design algorithms to sort through just the specific pieces needed to craft a map of your business processes.
Contrast that with Process Mining, and you’ll see that with a simple export of event logs from your IT systems, anyone can craft the same map in far less time and with far lower expenses.
How to Put Process Mining to Work in Your Business Structure
Where can Process Mining bring the most benefits?
- As a part of initial operational process design. Assess existing processes and help highlight where they need to be streamlined before you ever compose an initial process map.
- As an auditing tool. Occasionally running an analysis on your event logs will tell you if any inefficiencies have snuck back into the process so you can nip them in the bud immediately.
- As a part of a full suite Process Mining project. Of course, once you see the power of Process Mining analysis, you’ll understand how powerful this tool can be for other processes within your business. Nearly every department is using IT systems, and these systems all generate event logs that Process Mining can use to point out pain points, bottlenecks, and inefficiencies.
Do you wan to understand how to best put Process Mining to use in your company? Get in touch.
Process Mining: 4 Success Stories
Learn about successful Process Mining implementations, along with specific use cases and best practices.