What Analysts Need to Understand About Business Events

Paul Vincent, CTO of Business Rules and CEP at TIBCO (and possibly the only person at Building Business Capability sporting a bow tie), presented a less technical view of events that you would normally see in one of his presentation, intended to have the business analysts here at Building Business Capability understand what events are, how they impact business processes, and how to model them. He started with a basic definition of events – an observation, a change in state, or a message – and why we should care about them. I cover events in the context of processes in many of the presentations that I give (including the BPM in EA tutorial that I did here on Monday), and his message is the same: life is event-driven, and our business processes need to learn to deal with that fact. Events are one of the fundamentals of business and business systems, but many systems do not handle external events well. Furthermore, many process analysts don’t understand events or how to model them, and can end up creating massive spaghetti process models to try and capture the result of events since they don’t understand how to model events explicitly.

He went through several different model types that allow for events to be captured and modeled explicitly, and compared the pros and cons of each: state models, event process chain models, resources events agents (REA) models, and BPMN models. The BPMN model is the only one that really models events in the context of business processes, and relates events as drivers of process tasks, but is really only appropriate for fairly structured processes. It does, however, allow for modeling 63 different types of events, meaning that there’s probably nothing that can happen that can’t be modeled by a BPMN event. The heavy use of events in BPMN models can make sense for heavily automated processes, and can make the process models much more succinct. Once the event notation is understood, it’s fairly easy to trace through them, but events are the one thing in BPMN that probably won’t be immediately obvious to the novice process analyst.

In many cases, individual events are not the interesting part, but rather a correlation between many events; for example, fraud events may be detected only have many small related transactions have occurred. This is the heart of complex event processing (CEP), which can be applied to a wide variety of business situations that rely on large volumes of events, and distinguishes between simple process patterns and business rules that can be applied to individual transactions.

Looking at events from an analyst’s view, it’s necessary to identify actors and roles, just as in most use cases, then identify what they do and (more importantly) when they do it in order to drive out the events, their sources and destinations. Events can be classified as positive (e.g., something that you are expecting to happen actually happened), negative (e.g., something that you are expecting to happen didn’t happen within a specific time interval) or sets (e.g., the percentage of a particular type of event is exceeding an SLA). In many cases, the more complex events that we start to see in sets are the ones that you’re really interested in from a business standpoint: fraud, missed SLAs, gradual equipment failure, or customer churn.

He presented the EPTS event reference architecture for complex events, then discussed how the different components are developed during analysis:

  • Event production and consumption, namely, where events come from and where they go
  • Event preparation, or what selection operations need to be performed to extract the events, such as monitoring, identification and filtering
  • Event analysis, or the computations that need to be performed on the individual events
  • Complex event detection, that is, the event correlations and patterns that need to performed in order to determine if the complex event of interest has occurred
  • Event reaction, or what event actions need to be performed in reaction to the detected complex event; this can overlap to some degree with predictive analytics in order to predict and learn the appropriate reactions

He discussed event dependencies models, which show event orderings, and relate events together as meaningful facts that can then be used in rules. Although not a common practice, this model type does show relationships between events as well as linking to business rules.

He finished with some customer case studies that include CEP and event decision-making: FedEx achieving zero latency in determining where a package is right now; and Allstate using CEP to adjust their rules on a daily basis, resulting in a 15% increase in closing rates.

A final thought that he left us with: we want agile processes and agile decisions; process changes and rule changes are just events. Analyzing business events is good, but exploiting business events is even better.

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