Automation and process analytics companies are great at naming new technologies. The hard part is to understand what those actually are and how those differ from each other. After that one can start to think if that specific technology helps with the problems in mind. In this post, I’m trying to clarify this space.
Let’s start with the good news: there are lots of excellent tools to help you. Many of those also end with a word mining - which has nothing to do with traditional mining, just like RPA has nothing to do with mechanical robots. (Disclaimer, process mining can naturally be used in traditional mining, and RPA be combined with mechanical robots, e.g. UiPath has a funny demo on that on YouTube:
Ok, back to the topic. Below is a summary of the terminology. The hardest term to describe is a process discovery since it has so many different meanings: e.g. BluePrism Process Discovery is about an “online survey that assesses and ranks your processes in term of most automation-ready to least” whereas Kryon Process Discovery is about automated process discovery where a “robot” identifies work processes and visually maps and evaluates those to be exported for their RPA Studio product. Automation Anywhere Discovery Bot has a similar target. Based on these findings, I would summarize that process discovery is basically task mining for RPA - similar thoughts have been in the Process Mining groups on LinkedIn.
“Analysis of business processes based on event logs” -Wikipedia
Aimed for end-to-end process analysis, design, and enactment: trigger further operations
Task mining / process discovery
“Discovery, monitoring, and analysis of user interaction data on a desktop.” -myInvenio
RPA vendors aim using on speeding & scaling automation
Process mining vendors aim using on filling the gaps in system logs with work that happen on user desktop
Task capture / automatic PDD
“Document work just by doing it” -UiPath
Aimed for automated process documentation for as-is automation
As we quickly find out from the table, those different technologies are addressing very different problems.
The question is not about which technology to use, but rather what are you trying to achieve?
Process mining brings visibility into processes within enterprise systems, like ERP and CRM, which are typically already automated and have huge transaction volumes with high business criticality. Automated process discovery is about finding parts of process paths to be automated with RPA. Task capture is about documenting what the RPA bots are designed to be doing.
How does Workfellow.ai fit into these categories?
We at Workfellow.ai are close to task mining by definition, but with a different focus: identifying manual work across and around all systems regardless of the process. I would like to call it Work and task mining:
Workfellow spots all manual knowledge work
Objective identification and classification of manual work is essential to understand the best approaches to get rid of manual work and friction at scale.
Why does Work Mining matter?
Half of the knowledge work activities are solvable already with the existing solutions. I think it is safe to say that there is no magic wand that would make all these manual activities vanish overnight. Instead, we should be converting and making sure that as much as possible of the manual work is converted into automation platforms and core systems. Typical solutions that are identified with work mining are (not in a specific order):
Correct settings and automation within existing systems
Training, tips & tricks to make most out the current systems
Scripts & low-code: small solutions touching and distributed to many
Integrations and APIs
RPA front office for attended automation
RPA back office for unattended automation
Lean and process/task development and optimization
New SaaS software for specific problems
Thank you for your interest! Please let me know your thoughts, e.g. using LinkedIn: https://www.linkedin.com/in/hwiik/
I’m happy to network with you!