Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.
At the recent ABBYY Reimagine conference, executives discussed how process mining and analytics are distinct technologies that can complement each other to help teams better understand business processes.
Process mining helps identify inefficiencies or opportunities for improving how companies do things, while analytics help businesses measure performance and identify opportunities. Together, they can deliver the best of both worlds. Better analytics and data prep workflows allow process mining tools to offer a glimpse inside various business processes. And process mining tools help executives understand and improve data science processes used by applications and improve overall reporting.
Better process modeling through analytics
Process mining applications are optimized for processes that live entirely within ERP or CRM applications but require manual work to handle other applications. With more applications and data moving to the cloud, manual steps become an issue.
“Everybody’s got too much data, and everybody’s processes are time-consuming and cumbersome,” Capitalize Analytics managing partner Eric Soden said. “Somehow we’ve got to get better and do more and be faster, and the data isn’t getting any smaller.”
Soden found that analytics tools like Alteryx help organize, prepare, and reformat data in a form suited for process analytics. This makes it easier to identify more dependencies or bottlenecks within a process. For example, improved visibility into a driver monitoring app may uncover a manual step that is causing bottlenecks for processing shipping manifest logs. This step can be automated using something like robotic process automation (RPA) technology.
When many people are involved, processes can become harder to understand, Soden said. An agreement system may coordinate the marketing and sales teams and also interact with other applications for purchase orders, invoices, and shipping. And an analytics tool can reformat the data used by the system into the format required by the process mining tool. With process mining, it becomes easier to visualize a wider variety of processes, generate better data, and improve business results.
Better analytics through process mining
Alteryx Global Partner marketing VP Steve Wooledge said scaling up analytics can lead to process bottlenecks in enterprises. Process mining tools like ABBYY Timeline make it easier to understand, streamline, and automate analytics workflows, as well as building analytics modules that can be used in downstream applications.
For example, an Alteryx customer ran a monthly process to calculate its fixed assets that took 40 hours and required a team of 10 contract workers to manage. Modeling this with process mining and creating a repeatable workflow allowed them to reduce that to 2.5 hours. Process mining automatically documented the process, which was useful for compliance and governance.
Process mining comes in once an enterprise has established repeatable analytic workflows — such as analyzing volume discount opportunities, running fraud detection models, and analyzing a constantly evolving mix of assets — that executives monitor and optimize. Many analytics use cases involve blending data from multiple applications, databases, or spreadsheets — or even documents that may all be maintained by different departments.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Learn more about membership.