Big Data and automation are on everyone’s mind. Process Mining, the innovative technology for the automated analysis of business processes, was therefore named a disruptive tech trend by Gartner for 2019. But with the hype come uncertainties. Is Process Mining really such a miracle cure? Isn’t it rather a niche phenomenon? And automation – that also means job losses, doesn’t it?
The good news first: Yes, Process Mining is very versatile, and no, process managers don’t have to worry about their careers.
However, you also have to be honest about the capabilities of the technology. Process Mining is not a panacea for all process difficulties.
Let’s take a detailed look at the 5 most common misunderstandings and misconceptions about Process Mining.
Misconception #1 – Process Mining replaces process managers and analysts
The phrase “automation” invariably also evokes the fear that people will be replaced. We see the basis for such concerns in process management: the preparation, analysis, controlling and optimization of business processes – all this is nothing new in principle. Up to now, these activities have been implemented manually with a high investment of time. If Process Mining now automates all this, what will become of the process managers and analysts?
Good Process Mining needs good process experts. The human component should never be underestimated here. The software is always only as effective as its users, and at least in the implementation of optimization measures in actual operations the whole team is needed.
Misconception #2 – Process Mining only revolves around data
Of course, the input on which the technology is based is first and foremost process data. To be more precise, Process Mining requires the stored data as an event log in order to visualize and analyze the process. The assumption that Process Mining is therefore a pure numbers game is, however, too short-sighted.
Process analysis and optimization, which enables Process Mining, is only one part of process management. It requires a process strategy, the implementation and execution of processes, and a well thought-out optimization plan. These are all aspects in which strategic, company-conscious thinking is required – even beyond key figures. Methods such as the DMAIC cycle help to maintain an overview in this area.
Misconception #3 – Process Mining only pays off with Big Data
Thanks to its automated functionalities, Process Mining is of course particularly suitable for large amounts of data. However, this does not mean that companies with more straightforward processes should keep their hands off it altogether. The (missing) transparency of processes plays an important role in Process Mining. It does not, however, necessarily depend on the size or complexity of the process variants.
Even smaller companies or those that only want to analyze simpler processes benefit from the insights into process performance and conformance. And it may even lead to larger optimization projects in the future.
Misconception #4 – Process Mining “outs” inefficient employees
This problem is particularly frequently brought to our attention by workers’ councils. If Process Mining systematically reveals weak points and bottlenecks in processes, aren’t employees with below-average work efficiency also exposed? Doesn’t this also violate data protection?
With regard to data protection concerns, we can give the all-clear. Data protection is a top priority for tools such as LANA Process Mining. In the case of sensitive personal data, maximum security can even be guaranteed by pseudonymizing the data.
The argument of employee worries is certainly justified. However, it is intended in the wrong direction. If the human factor is actually identified as a reason in the root cause analysis, process optimization is not about assigning blame, but about optimization potential. Not “with whom“, but “why” do bottlenecks occur?
Misconception #5 – Process Mining optimizes all process deviations by itself
Of course, we must also be self-critical. Process Mining does not work miracles, it is not the answer to all conceivable process difficulties. At the current state of the art, the tool offers in-depth insight into processes and root causes of deviations. However, the optimization itself is still the responsibility of the companies themselves.
Naturally, all this does not exclude the possibility that the technology will continue to evolve in the years to come. Theoretically, even completely self-optimizing processes will be possible in the foreseeable future.
But until then, it’s still up to you.
The best way to explain it is still a personal conversation. Whether you just want general information or want to get started right away with a test account – we will be happy to help you.