Thursday, February 04, 2021

Process Discovery vs. Process Mining

The first step in automation is to gain a complete understanding of the current state business processes. Often enterprises do not have a knowledge base of all their existing processes and hence identifying automation opportunities becomes a challenge. 

There are two techniques to address this challenge:

Process Discovery

Process Discovery tools typically record an end-user's interactions with the business applications. This recording is then intelligently transformed into process maps, BPMN diagrams and other technical documentation. The output from the Process Discovery tool can also be used to serve as a foundation for building automation bots. Given below are some examples of Process Discovery tools:

Process Mining

Process Mining is all about extracting knowledge from event data. Today all IT systems and applications emit event logs that can be captured and analysed centrally using tools such as Splunk or ELK stack. As one can imagine, the biggest drawback of Process Mining is that it would not work if your legacy IT systems do not emit logs/events. 

By analysing event data, Process Mining tools can model business process and also capture information on how the process performs in terms of latency, cost and other KPIs (from the log/event data). 

Given below are some examples of Process Mining tools:


Tuesday, February 02, 2021

Ticket Triaging with AI

The below link is a comprehensive article that introduces "ticket traiging" and also explains how AI can be used to automate this important step. 

https://monkeylearn.com/blog/how-to-implement-a-ticket-triaging-system-with-ai/

Some snippets from the article:

"Automated ticket triaging involves evaluating and directing support tickets to the right person, quickly and effectively, and even sorting tickets in order of importance, topic or urgency. Triage powered by Natural Language Processing (NLP) technology is well-equipped to understand support ticket content to ensure the right ticket gets to the right agent.

NLP can process language like a human, by reading between the lines and detecting language variations, to make sense of text data before it categorizes it and allocates it to a customer support agent. The big advantages of using NLP is that it can triage faster, more accurately and more objectively than a human, making it a no-brainer for businesses when deciding to switch from manual triage to auto triage."