Which feature helps identify inefficiencies in processes using data analysis in Appian?

Process Mining is the feature that specifically focuses on identifying inefficiencies in processes by utilizing data analysis. This approach analyzes event logs generated by processes to visualize how processes are actually executed in practice compared to how they are intended to function. By examining these discrepancies, organizations can pinpoint where delays, bottlenecks, and redundancies occur, allowing for targeted improvements.

Through the visualizations and insights derived from Process Mining, teams can make data-driven decisions to optimize workflows, enhancing overall efficiency. This capability is particularly powerful as it goes beyond just surface-level observations, diving deep into the actual performance metrics of the processes.

In contrast, automation typically focuses on streamlining tasks, data fabric pertains to integrating various data sources for seamless access, and total experience involves enhancing the overall experience across multiple touchpoints rather than specifically identifying process inefficiencies. These features serve valuable purposes, but they do not center on the rigorous data analysis aspect that is inherent to Process Mining.