Big Data and Analytics Improve Next-Generation BPM

One of the most significant emerging trends in BPM (business process management) is the use of real-time data analytics within business processes themselves to optimize process performance and, more importantly, improve business outcomes. Dynamic, adaptable and self-optimizing are the hallmarks of these new Intelligent Business Processes, made possible by the strategic use of analytics within the processes themselves. Since the data used to support intelligent processes can come from a wide variety of sources, including social media, this BPM trend is naturally linked to a company’s Big Data efforts.

Although companies have been collecting and analyzing data about their processes for a long time, current approaches are woefully inadequate because:

  • The data collected is narrowly focused–typically restricted to a few process performance metrics, such as queue lengths and process duration.
  • The data is often analyzed offline.
  • The resulting analytics are not available to the executing processes; hence, the analytics cannot directly influence process performance or process outcomes.

Today’s meager use of analytics is limited to what is traditionally called Process Intelligence (or more precisely, Process Performance Intelligence). However, traditional Process Intelligence does not lead to “Intelligent Processes,” where analytics are used directly within the business processes.

In contrast, Intelligent Business Processes use analytics to achieve five distinct kinds of “intelligence”:

  • Performance Intelligence. When processes in execution have access to performance analytics, the processes can automatically adapt and optimize performance. For example, processes may perform re-schedule or omit entirely a backlogged step, such as a credit check. Processes can automatically re-prioritize work queues to ensure, for example, that the best customers are receiving the best service. Customer-facing processes can identify negatively impacted customer and proactively mitigate or compensate for the problem – such as immediately offering a discount on a future service.
  • Outcome intelligence for maximizing business goals. Although optimizing business process performance is helpful, this alone does not necessarily lead to better business outcomes. Every business process executes to support one or more business goals, whose achievement can be maximized by Intelligent Processes. Goal-related process data can be collected and analyzed with respect to business outcomes (e.g., Did the customer submit his shopping cart? Was the product or service successfully delivered?). Using analytics over outcomes, intelligent processes can adapt their behavior accordingly. For example, in marketing processes that vary both the selection and sequencing of “offers,” outcome analytics can be used to dynamically adjust both the selection and their sequencing.
  • Social Intelligence. Using analytics over social media activity, processes involved in marketing and 1-1 selling can improve their recommendations and targeting. Sentiment analysis and trend analysis can guide automated marketing processes toward hot products and away from problem areas.
  • Situational Intelligence. Many business processes are triggered or influenced by external situational factors (e.g., Order Fulfillment processes are dependent on the supply chain, workforce management processes are dependent on weather and traffic.) By monitoring and analyzing situational factors, business processes can adapt and optimize even in the presence of rapid change.
  • Predictive Intelligence. While many forms of analytics can help mitigate problems when they occur, Predictive Analytics can do more, helping to avoid problems before they begin. For example, Predictive Analytics can give an indication of when an important order may be delayed, even before it is delayed. It can also flag customers likely to abandon their shopping carts, even before abandonment occurs. With this information, an intelligent Business Process can take steps to avoid these problems, such as re-prioritizing the important order or adding an incentive to the shopper.

In summary, the use of analytics over a wide variety of internal, external and social data (i.e., “big data”) allows business processes to become “intelligent,” resulting in better business and process outcomes. Next-generation BPM tools with integrated real-time analytics are enabling this important trend.

Dr. Dale Skeen co-founded Vitria with Dr. JoMei Chang in 1994 and oversees the technology direction of the company. He is credited with inventing distributed publish-subscribe communication, with over a dozen patents in this and other related technologies. Dr. Skeen has more than 20 years of experience designing and implementing large-scale computing systems in the areas of distributed computing and database systems.


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