The success and failure of an automation starts with the right use-case. Sitting next to an employee and asking them questions gives a lot of insights, but process discovery tools bring a data driven approach.
In the good old days, someone booked a for a week, and the entire wall was lined with brown paper, locking people up from multiple disciplines and feeding them once in a while. Colleagues looking weirdly into the room like they are in a zoo. But the output was always fruitful, a list of processes to start working with. These types of sessions are still high in output but not always as scientific, and during covid-19 days, they are not always easy to organize.
Tools now have emerged to support this. Process Discovery is the most prevalent, creating a detailed, click-by-click design of the automations and a good idea of the potential benefits. Combined with the more old school methods, it rapidly builds up a pipeline based on actual measurements.
Process Discovery vs Process Mining
More and more tools hit the market, claiming to be of value for RPA/Automation teams, be aware of this small nuance in naming. Process Discovery tools have as primary goal identifying potential automation opportunities with the added information of a detailed view of what happened, with the aim of automating this process based on the same information. Sounds quite futuristic, self-creating robots?
The first vendors already start experimenting with this, and probably one of the hot next technologies to arise.
Process Mining also gives valuable information, but with a higher level, based on events provided by other systems, this gives more information around how data flows and how processes connect together. Golden information for consultants and process experts, but lacking detail for most of the RPA needs.