I've seen a few mentions recently of process mining. Do you see process mining playing an ever growing role in BPM going forward?
Only as an opportunity for the vendors to charge an arm and a leg. Beyond that, like most things "analytics," few will do it, fewer still will do it well.
I think Process Mining will play a big role and won't be called process mining as it does. When the products come out that leverage process mining it will be buried behind other names or labels (cognitive computing anyone?) but the hallmarks of process mining will still be there.
It definitely will play a bigger role indeed. However, and that's IMO the crux of the matter, it needs to be positioned as a part of the whole. Currently you have to be pretty careful with positioning process mining as some sort of a holy grail. I mean: it's a snapshot, can be pretty incomplete (danger of sub-optimization). I do look at business processes as not just transaction based only: what to do with the white space?
I do believe that more sophisticated technology can and will make a difference here. But as long as people (emotion, politics) are involved I wouldn't put all my analysis bets on this interesting mining technology... :-)
Did some projects with it in the last 7 years. Although some think it is still new, to me the hype is a little over. Always been very sceptical about it, because it just shows bi in a process oriented way.
if you don't have the right data, it will not add much value. Besides that its only shows you symptoms of process performance, not the causes.
because of better availabilty of data, it might get more usefull the next years, but in the end it's just a tool to support process improvement with the common drawback that you improve in the locker room, not during the game.
Process mining is an exciting, growing science. Its potential is significant, but its true power lies in the ability of the vendors / consultants to actually use process mining as a tool for continous improvement and decision / operational support. I dream of this as Operations Research 2.0.
I think it can be used during the game as well, I can imagine a simple scenario of load-balancing human resources and other resources during process runtime (referring to your article today, @Emiel)...
But I'm afraid that a potential hype will motivate clueless IT marketing people into devoiding the concept of any meaning. Just as it happened to IoT, Big Data etc.
I don’t think process mining will have a role by itself. Maybe it would be a nice capability of big BPM Suites, but not as a standalone discipline or tool. I think that process will be more agile, and measuring and discovering how the run should be too.
So, if process mining evolves greatly, and can be used without consultancy services, it has a future as part of SaaS or agile BPMS’s.
But, in a similar way as BI failed to explode its potential without consultancy services, if process mining is not enough easy and out-of-the-box ready to use, it won’t be massively adopted, no matter how you name it.
I think that process mining is a useful tool that hasn't reached its full potential yet.
Most KPIs used in BPM are useful to identify bottlenecks but fail at discovering how those processes are executed. Process mining, if used right, can identify alternative courses, patterns and common mistakes. This improvement opportunity can be filled with process mining.
Process mining is indispensable for understanding customer’s journey (which is also a process – see ref1 and ref2).
Process mining is good for telling you what people are doing now, not necessarily what people should be doing now. But with the "digitization" of industries the strongest role for process definition is understanding what people should be doing in the future. Creating that blueprint for the future, in process terms takes business owners + (virtual ) conference room + strategic thinking + whiteboard (or tech equivalent).
So it this context process mining is "fighting for a better yesterday" whilst the winners are "designing a better future".
Yes where data is buried in the "mine" and is required for businesses operations so the process will automatically trigger the digging out of required data. Process will also control creation of new data as one version of the truth begins to reduce unnecessary duplication and thus storage requirements.
Nice metaphor from @Emiel, to wit "improvement happens on the field, not in the locker room". I think thought that this metaphor is actually an argument for process mining - because PM is very much about making changes in near real time "on the field".
There is suspicion reflected in several comments that the past is not a good basis for predicting or modeling the future (which is also a fallacy inherent in a lot of big data hype). This is a valid concern, but I think it misses the power of what PM is all about.
All current BPM (or even all IT) is at heart "Platonic", with executive gurus imagining a future, top down. Process mining on the other hand as it develops would be expected to be deployed for all managers, including front-line managers. You could even say that process mining is the first "technology of the tacit".
And there's no reason that PM can't be integrated with modeling (e.g. something like Lanner). Process mining is dependent on logs, but with the rise of IoT and cheap sensors, it's hard to imagine there will be a shortage of data. PM itself is still in early stages; the software at this point can only handle a single dimension of data, when reality of course is multi-dimensional.
I expect that PM will become a feature of all BPM products, and certainly consultants will dine out on it. But insofar as BPM is at the core of the work of business, over time I expect process mining to become the core of BPM. Process mining is surprisingly easy to use, and the reaction of business people to a PM demo is a joy to see.
The challenge for champions of process mining technology I think is mostly around business readiness. If BPM puts pressure on people to think systematically about their "deep domain knowledge" (pace @Bogdan), then PM really puts pressure on executives. No more "wait states on the golf course" while IT figures out how to implement your latest idea.
Problem today? Solution tomorrow. Implementation the following day.
And again and again in a process mining-driven fast-cycle environment of constant business evolution.
Enthusiasm aside, it will likely take years for process mining to be absorbed by executive cadres.
I'm not a process mining expert, and I don't even play one on TV. As a result, I won't speak to process mining specifically; however, I agree with the gist of Ian's position above (at least, as I understand it). To wit: everybody is way too focused in general on the "as-is" or current state of a process. Sure, there is some information that's worth knowing—for example, who the stakeholders are, or where problems have generally arisen—but in general, I find the obsession with the precise documentation of a process you're about to replace a little odd.
"As-is" research is like preparing to drive to work on the first day of your new job by pulling up Google Maps to examine the route to your previous job. No doubt there are some streets in common, but you've been driving those roads for a long time. Surely it would be better to focus on where you're going, rather than where you've been.
One of the challenges that process mining faces is it did not move to hot or real time analytics, despite it having that built-in capability.
As it becomes more important to monitor operations in real time, it is critical, from an adoption perspective, that vendors integrate process mining in their monitoring toolset.
These words I wrote some years ago continue to feel fresh today:
Organizations live in a world where interdependence, self-organization and emergence are factors for agility, adaptability and flexibility plunged into networks. Software-based information systems go into a service oriented architecture direction and the same goes to Infrastructures where services are become structures available in networks. inspired into empirical studies of networked systems such as Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us structurally understand or predict the behavior of these systems. Those findings are characterized by been supported on the “complex networks” concepts