bpmNEXT 2016 demo session: Signavio and Princeton Blue

Second demo round, and the last for this first day of bpmNEXT 2016.

Process Intelligence – Sven Wagner-Boysen, Signavio

Signavio allows creating a BPMN model with definitions of KPIs for the process such as backlog size and end-to-end cycle time. The demo today was their process intelligence application, which allows a process model to be uploaded as well as an activity log of historical process instance data from an operational system — either a BPMS or some other system such as an ERP or CRM system — in CSV format. Since the process model is already known (in theory), this doesn’t do process mining to derive the model, but rather aggregates the instance data and creates a dashboard that shows the problem areas relative to the KPIs defined in the process model. Drilling down into a particular problem area shows some aggregate statistics as well as the individual instance data. Hovering over an instance shows the trace overlaid on the defined process model, that is, what path that that instance took as it executed. There’s an interesting feature to show instances that deviate from the process model, typically by skipping or repeating steps where there is no explicit path in the process model to allow that. This is similar in nature to what SAP demonstrated in the previous session, although it is using imported process log data rather than a direct connection to the history data. Given that Signavio can model DMN integrated with BPMN, future versions of this could include intelligence around decisions as well as processes; this is a first version with some limitations.

Leveraging Cognitive Computing and Decision Management to Deliver Actionable Customer Insight – Pramod Sachdeva, Princeton Blue

Sentiment analysis of unstructured social media data, creating a dashboard of escalations and activities integrated with internal customer data. Uses Watson for much of the analysis, IBM ODM to apply rules for escalation, and future enhancements may add IBM BPM to automatically spawn action/escalation processes. Includes a history of sentiment for the individual, tied to service requests that responded to social media activity. There are other social listening and sentiment analysis tools that have been around for a while, but they mostly just drive dashboards and visualizations; the goal here is to apply decisions about escalations, and trigger automated actions based on the results. Interesting work, but this was not a demo up to the standards of bpmNEXT: it was only static screenshots and some additional PowerPoint slides after the Ignite portion, effectively just an extended presentation.

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