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The topic of real-time business intelligence (BI) has had steady interest since the turn of the century. Back in 2003, The Data Warehouse Institute (TDWI) performed a study that showed 50 percent of respondents were deploying or planned to deploy "Real-Time BI" projects. Global 24/7 markets, just-in-time business processes, adaptive sales and marketing processes, and increased customer service all contribute to the need for faster decision making and actions.



To date, Complex Event Processing (CEP) software has been considered the domain of development teams building sophisticated real-time applications such as algorithmic trading or real-time risk management in financial markets. But a growing number of savvy customers have applied CEP software for next generation real-time analytic applications in traditional BI environments that drive situational awareness, faster decisions and immediate actions.

Second-generation CEP platforms have emerged with features that enable BI and data management professionals to more easily incorporate event processing into their analytic application suites. Using CEP, these professionals can incorporate Continuous Intelligence (TM) in a fast, affordable manner to speed decisions and actions that respond to market activity and opportunities, raise customer revenue and lower churn, and increase service assurance.

How do we get faster, actionable analytics?

As many of you know, not all business processes are capable of or even designed for working in "real-time." But many customer-facing, revenue-related and operational processes could fully take advantage of just-in-time, situational intelligence to drive faster actions that enhance customer relationships, maximize revenue opportunities, and ensure the risk-free delivery of products and services.

Faster, actionable analytics is easier said then done -- at least so far. When BI and development teams look at such applications, they encounter daunting challenges:

  • Gathering large volumes of constantly changing data from various customer or operational touch-points
  • Integrating this high volume data with existing customer and operational information to provide full situational context
  • Continuously analyzing all this information to identify problems and opportunities and derive the proper course of action

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