As companies grow, behind-the-scenes business processes get more complex. And as the complexity grows, it takes a toll on efficiency. With the advent of big data analytics, companies are finding that techniques like machine learning can help identify inefficient processes and reclaim millions in lost productivity.
Business process modeling (BPM) is not a new discipline. But thanks to rapidly emerging analytic technologies, a new class of vendors is emerging to give companies even more power to track and measure how well business processes are working at a very granular level.
One of the new process miners staking a claim in enterprise is Celonis. The company was born in Germany 5.5 years ago with the idea that companies can weed out inefficient processes fairly easily using the power of software. By mapping the actual flow of work among all the different back-office applications and comparing it to ideal workflow scenarios, Celonis can identify bottlenecks and other business pain points that leak profits.
“Honestly, what we’re doing is removing the need to pay a Deloitte or McKinsey to come in and do all this discovery and find the issue,” says Celonis Chief Marketing Officer Sam Werner. “We can replace the expensive consultants that get paid today to go do this work, which by the way can be very disruptive for your organization.”
Process mining starts with data discovery. Its product, called Proactive Insights (or PI), Celnois scans mainstream ERP systems from the likes of SAP, Oracle, and Microsoft, identifies the business processes, and then constructs a visual representation of how the process works. This gives architects a low-level view of how many different places each individual purchase order waits for approval, for example.
If you’ve ever seen a BPM tool, you know that visualizations of business processes can get quite messy. The so-called “process spaghetti” demonstrates that, while there typically is a standard way to do things, that variations inevitably creep into the process. Sometimes these variations are required, such as for compliance purposes. But other times, the variations are unnecessary shortcuts, or even instances of outright fraud.
But tracking the flow of every business item is a big undertaking. That’s where big data analytics comes in. After mapping the actual workflow, Celnois applies data science techniques, like statistical analysis and machine learning, to tease the inefficiencies out of the resulting data.
As Werner explains, it’s all about business process optimization and transformation. “Companies are not delivering on their business commitment to customers, and there’s very avoidable things that are contributing to that,” he tells Datanami.
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