RPA Vs. Process Mining: What Companies Should Know

The next big leap: Context aware decisions with RPA, ML and Process mining

Putting process mining and RPA together offers clear benefits—the first finds the places where more automation should be applied and the second brings the engine that can do the work. This led process mining outfits such as Celonis to build out sturdy integrations with RPA bots—the company has connectors to UiPath, Blue Prism, and Automate Anywhere.

It’s a natural combination. One method provides the prognosis, the other provides the medicine. That Celonis was the first company to hit upon this construct in a big way is remarkable, given that companies have been employing vast collections of applications and data sources for decades—most of which include transactional data and app logs that could inform a holistic reading on process flow.

Task Mining: Finding Other Work Processes Such As Email, Scheduling Tools, SMS

The other pieces of the work universe that these companies are now bringing into focus are all the work processes that take place outside of packaged applications. Email, for one, remains a mainstay in many workflows, from submitting and receiving invoices to providing confirmations and notifications.

Software to track these less structured electronic environments has emerged. The industry refers to this as task mining. Application logs and data tables are usually not enough to paint the entire picture of what happens in these apps, so tracking processes here requires more nuanced tools.

Task mining software companies have been duly snapped up by Celonis, UiPath and other major players, as these companies seek to provide their users with complete solutions. This is an evolving part of the ecosystem, and we expect it to be one of the more active spaces in the enterprise application space.

Getting More Data Out of ERPs and Business Apps

The persistent challenge for larger companies running enterprise-level systems such as Oracle and SAP have been getting data out of those ERPs and into other software platforms, including standard data warehouses and BI programs. That challenge remains for companies who want to leverage data from their core ERP processes inside RPA and process mining applications.

UiPath, Celonis and others are getting better at pulling data from these systems, but Oracle and SAP don’t make it easy, as they’d prefer that companies stay at work inside their application rather than bouncing out of it.

Closing the Loop to Action

The obvious endgame for any of these solutions is enabling automation to make informed decisions, and then act, based on the data and business conditions at hand. That may mean issuing an incremental invoice to a client who only paid part of what they owe, for instance. RPA bots came about to solve these exact kinds of problems.

The issue, however, for RPA platforms is that they were designed to do one thing: automate a particular task. They’re not built to make decisions based on a raft of existing data and past decisions. They’re more about human-devised if/else statements than developing decision trees and algorithms on their own.

But that next level of automation is coming. In the future, the winning machines will make context-aware decisions based on a confluence of factors that sample data from across the enterprise. The race is on to integrate machine learning into RPA and process mining to achieve that.

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