A conversation with an old friend of the house.
He has an absurd amount of consulting experience and was a pioneer in process mining: “no one has time for anything because everything is done at the very edge of advisable resources.”
And that includes everything from the shortage of waiters to—yes, you guessed it—finding the time (and the attitude, I’d add) for something like process mining.
The legendary founder of this discipline suggested in a recent article that if there’s anything hot in 2022, it’s learning process mining (by the way, you can sign up for our courses here). We’re eight months into 2022—everyone can draw their own conclusions, and all of them are valid.
But that’s not the point here.
The issue is that the combination of resource scarcity (human, perhaps?), potentially not just limited but also misaligned, plus the sustained focus and effort it takes to learn new things and change methods across multiple minds simultaneously, seems to be the perfect antidote to adopting the idea of analyzing the digital footprint of our actions and processes as a default analytical routine.
If you’re thinking this is a complaint about the adoption of our beloved technique—well, people come prepared to cry, as the saying goes.
But really, it’s a classic issue that fills thousands of hours and hundreds of pages describing the problems of human organizations: and now that it’s trendy to talk about cognitive biases in decision-making, it’s worth acknowledging that simply knowing something doesn’t imply change or action.
So while the data revolution is firmly embedded in the investments and concerns of every self-respecting CEO, the mere idea of making transparent the gap between how we design execution and what actually gets done remains as appealing as it is non-urgent.
It sounds more sophisticated to talk about “adoption barriers” than to say there’s a reluctance to make the most of a different concept. Sometimes, it feels like instead of learning “process mining,” what’s truly essential is just getting back to talking about processes at all.
But maybe there’s a more provocative idea: if you agree with that familiar line we keep repeating—that data is the new oil, even if still in need of refining—why does it seem so far-fetched to allocate resources toward designing data models and capture methods that help us learn and improve how things are really done?
Short answer: Because you’d rather watch someone else go first before spending your own resources.
In the end, the most surprising barrier is that when people talk about being data-driven and focused on outcomes, the hardest thing to accept is that it makes sense to analyze and measure sequences of actions. And yet, professional sports have been doing just that for quite some time now, and the glory of modern athletics is increasingly tied to it.
So, in the end, everything will fall into place in due course.
Check some of our videos:
- New ➽ ICU discharges: where time slips away
- Fast Track vs Standard Path: Which is Better for Diagnosing Colorectal Cancer
- What causes discards in parenteral nutrition preparation? Key factors revealed!
- How Process Mining Uncovers Medication Administration Issues
The Prompter.io is our open project to share our experience—and that of others—in integrating language models and data-to-text techniques into process intelligence. Don’t miss it!
