Process Mining Expectations vs. Reality
Emerging technology tends to gain a reputation beyond its years. This may be in the form of undeserved credit — a silver bullet to solve all business problems — or an undervaluing of its capabilities. The field of BPM and BPI have become umbrella disciplines which house many of these new technologies, one of which is Process Mining. As with other technologies, Process Mining has its boundaries under which it thrives and delivers the most business value. But within these borders, may also live unexpected capabilities.
When introducing process mining into an organization, it’s important to understand expectations vs. reality. Finding a balanced truth between what is expected of the tool and the actual capacity of the tool will help an organization set smart goals, create a supportive environment and ensure the results of Process Mining flourish. Here are a few assumptions about Process Mining and what they really mean.
#1: Process Mining is the same as Business Intelligence (BI)
The vocabulary used across Business Process Management (BPM), Business Analytics (BA) and Data Science can overlap. To an outsider’s ear, a Process Mining tool may appear to be the same as, or replace a business intelligence tool. They both work with large volumes of data, and both rely on “fact-based” support systems. In reality, Process Mining is complementary to BI tools, but they are not the same.
The most distinguishable characteristic between a process mining tool and a BI tool is in the former’s analytical power and the focus on the way a process is happening. Where BI is strong in monitoring and reporting data, Process Mining’s strength is in the analysis. Don’t expect to replace existing BI tools with Process Mining, but rather expect to let them live alongside each other in complementary symmetry.
#2: Process Mining will take care of itself
Process Mining transformed the “old way” of process analysis into a swift form of automation. Because automation reigns over manual work, the assumption holds that Process Mining can be “configured by IT” and set up to spit out results. In reality, Process Mining is a discipline unto itself and a Process Mining tool simply supports this discipline. Process Mining experts, an overarching BPM strategy and an effective plan for change management must also be in place.
#3: Process Mining makes the process improvement method null
As alluded to above, there’s an expectation that Process Mining will replace the need for process improvement architecture. However, Process Mining is a piece of the improvement method puzzle and helps deliver fact-based process maps essential to modern day BPI. When you take a look at the larger BPM lifecycle, you’ll find the areas in which Process Mining rules and where other tools and techniques are needed. Process discovery, process analysis, process monitoring, and process refinement are all areas under which process mining thrives. However, process redesign and process implementation require something else.
#4: Process Mining is only good for ERP systems
The assumption goes that process mining is only used to discover, map and analyze defined processes that exist in bulky ERP systems. However, Process Mining is also good at discovering processes regardless of the underlying systems involved. For example, the route a user takes while navigating a website isn’t necessarily process-minded, but their clickstream may tell an interesting story of how easy or hard it is to complete a process, such as making a purchase or filing a complaint. In fact, once you begin to look you’ll find that data eligible for Process Mining is in many systems: CRM, Service Management, BPM, LOB, Call Centers, custom-built legacy systems, Excel, PLM and more.
#5: Process modeling and Process Mining reveal the same ‘as-is’ process
Process modeling and Process Mining are two branches of the same tree, both intent on illustrating a process flow. However, where Process Mining creates a factual ‘as-is’ process, process modeling maps the ideal ‘should-be’ workflow. Process modeling uses employees and documentation as core inputs, while Process Mining uses verifiable event logs from IT systems. The ‘as-is’ flow created by modeling includes decision logic and option rules to demonstrate the best case scenarios within an organization.
While Process Mining will always be more factually accurate than process modeling, they serve different purposes. Take the example of an onboarding process which will be published to an organization’s internal portal for new employees to access. When a new employee is getting started, which do you think will serve them better? The intended process (process modeling), or the actual process, flaws and all (Process Mining). Now consider an HR leader trying to understand why new employees seem to lack understanding of how sick leave works. Process Mining may help them determine flaws in the process and lead them towards a resolution.
Understanding the true capacity (and intentions) of the tools in which an organization invests is critical for the technology to succeed. Get all assumptions and expectations out on the table before deciding to move forward with the investment. This will serve your organization in the long run and avoid quick wins followed by disappointment.
05. 11. 2018