What is our primary use case?
We use this solution to get deeper insights into the process and to see if there are any opportunities on how we can improve the processes. In most of the cases, Process Mining would actually throw out analysis or insights which are already known to the users, but in some cases it would be more granular and backed with data.
Those database-backed insights are very valuable for the business partners or the operations teams. It allows them to make decisions about how they can improve their process, like whether there's any process changes or technology changes required. Do they need to change the workflow systems or the technology assets which they currently use? Is there a better way they can perform the work or is there any scope of automation? Are there any compliance issues which wouldn't have been visible to a human eye just by viewing the process or the reporting?
For process mining, we understand some of the hypotheses they have, like what is that they want to understand? Are they looking to gain insights around how their teams are performing? I think you gain value about how the process is overall behaving. Is there any kind of rework or are they looking at automating or redesigning their own process?
This is a hybrid solution.
What is most valuable?
One of the reasons we have gone with UiPath is because it was a business decision and because our company subscribes to the UiPath automation suite, which includes RPA, etc. We thought it would be efficient to keep everything on the same platform.
The cost was the other part of it because we compared it to Celonis. We are using UiPath mostly for strategic reasons. It was easy for us to get onboarded with Process Mining rather than setting up a whole new infrastructure for a different tool.
What needs improvement?
We faced issues with the data sharding process. UiPath has its own limitations in terms of the number of rules of data it can actually handle at a given point of time. As a tactical step, they provided the data sharding approach, but that was not the best. It kind of complicated the analysis rather than making it easy for people to analyze large data sets. So, the handling of large data sets is a major problem we want them to improve.
I think they can improve their overall feature set around the visualizations, which they can provide out-of-the-box. Some of these visualizations are not already there.
Finally, it is about the ETL process. I think the ETL process could have been much simpler compared to what we do right now, which consumes a lot of time. If I talk about a typical project life cycle, I would spend about 30% of the time getting the data into the system, which is obviously not ideal compared to some of the other tools we have in the market.
Their ability to create out-of-box integrations and solve for the ETL problems could be an improvement, and their ability to help us scale and do parallel processing when handling large data sets. They could also improve their overall look and feel on how the analytics will show up on the tool. They just do the job, but when I'm showing an analysis to a very senior leader, I would obviously want that to look better than how it does now. I'm not sure what the product roadmap looks like right now, but I'm sure they would have something on the product roadmap. If these are not already a part of the product roadmap to improve the product, then I think they would be missing the bus.
For how long have I used the solution?
I have been using this solution for seven and a half years.
What do I think about the stability of the solution?
It's stable. We haven't seen any issues.
What do I think about the scalability of the solution?
We haven't seen any issues with scalability. The problem is with the handling of large data sets. If you're using smaller data sets, then I think it's good enough to manage parallel activities. But if I am working on a project with a large data set, that's where we would run into problems where the other projects would have to be stored or they cannot be run parallel. It's scalable, but they would need to solve for handling their data sets internally to allow parallel processing.
From a traditional process mining, they allow for everything a process mining or platform would need to give. But if we're talking about using this for other purposes like execution management, getting more automated data feeds, etc., I think that's where it would probably have a problem. When we're dealing with process mining, especially in a banking organization where we are dealing with millions of rows of data at a time, I think that's where we would probably want them to be able to scale or allow us to scale much faster on that.
I would rate the scalability 3 out of 5.
How are customer service and support?
We have a full subscription for technical support. We have on-demand support at all times.
What's my experience with pricing, setup cost, and licensing?
It's reasonable. It provides you with all the basic features that somebody would expect. If those basic features are what you're looking for, I think UiPath is reasonably priced compared to some of the other tools.
Which other solutions did I evaluate?
We compared the solution to Celonis. Celonis provides you with more out-of-the-box functionalities and drag-and-drop features which are user-friendly, and it has a very powerful execution management system, but that is something we weren't looking at because we wanted to familiarize the audience with what Process Mining can do before we moved to execution.
What other advice do I have?
I would rate this solution 5 out of 10.
It's good for doing traditional analysis, but if we have to do more, I think that's where they would lag behind their competitors.
Which deployment model are you using for this solution?
Hybrid Cloud
Disclosure: My company does not have a business relationship with this vendor other than being a customer.