Reliable and Easy!
March 06, 2022

Reliable and Easy!

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with IBM Process Mining

IBM Process Mining has helped in delivering value when the model generation has been a focus, whether it be data derived, reference, or comparison of both models in terms of metrics with frequency and root cause analysis. The product focus also delivers value to Business Process Mapping, Statistical Analysis, and looking at information on metrics.
  • Level of detail and functionality with assets provided by IBM Process Mining
  • Automation assets
  • REST API information
  • More AI capabilities could be provided
  • Has a learning curve, but it is a good one
  • Time to implement may take a while, but it is not bad
  • Automation - improved time
  • Accuracy of results - well understood/articulated
  • Root Cause Analysis - information about where problems are
IBM Process Mining always has a support representative readily available to provide assistance, without any concerns or waiting time. In addition, the documentation and community of folks utilizing the product has also been instrumental. IBM Process Mining support representatives assist with technical questions and clarification on concepts/how to use the product.
Turbonomic has more functionality on AI and ML, while IBM Process Mining has some of that but also focuses more on visualization and KPI understanding. Also, metadata management could be done well with both. IBM Process Mining focuses especially on a digital transformation and has a more cost-effective solution.

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IBM Process Mining is good for data-driven-making insights when it comes to KPI comparison, where thresholds can be added and rolled up by an aggregate function. This has also allowed for comparisons to easily be done without necessarily resorting to a visualization tool. Additionally, IBM Process Mining is well suited for application performance monitoring of data and improving any workflows that need adjusting.