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Embedded BI: Intelligence at Your Service

As organizations grow to depend on globally distributed service networks, their business intelligence (BI) capability must adapt. Here's how autonomous agents will "embed" BI in business processes to overcome new challenges.

Outsourcing individual business operations and services is a major trend in global business operation models today. In such scenarios, often the only legal obligations that govern the service provider are the terms stated in the service-level agreement (SLA) for a specific service performed and rendered.

What companies most commonly want to outsource are service- and manufacturing-oriented business operations. Manufacturing processes, although complex, have less direct interaction with customers; current SLAs are normally sufficient regardless of where the actual manufacturing, assembly, and delivery process takes place. Customer service operations, however, require service providers to have direct interaction with customers. Beyond the technical aspects of problem resolution and support, it matters a great deal how "tuned" a service provider is to the customer's psyche. It's very hard to spell out cultural, linguistic, and psychological characteristics in SLAs.

Today, when customers call to address problems or follow up on unresolved problems, they usually end up interacting with voice-response prompting systems, which put them through sequences of prompts, and eventually to customer service representatives. By the time customers reach live representatives, they're already frustrated. Further delays and unsatisfactory answers stoke agitation, eroding customer confidence and possibly causing the company to lose business. Is it possible to turn such a situation around?

Let's fast forward a few years. In a future version of this scenario, before the customer gets upset, embedded mobile intelligent agents pick up the conversation pattern and voice tone change. The system proactively addresses the rising anger by playing an appropriate subliminal message that calms both the caller and the customer rep before it gets too late. This "intelligence" is built right in the "communication" layer, where an intelligent agent performs voice mining on the fly to understand not only the language, voice envelope, and culture involved, but also the psyche of the caller. The system understands the customer's behavior with far more sensitivity than what's possible with today's customer profiling techniques.

Globally distributed intelligent agents can search and aggregate information from heterogeneous sources. Internet devices, making extensive use of in-memory database technologies, broadcast and receive information continuously through mobile agents as part of business process service chains. In a number of scenarios, such agents and devices will eliminate the need for traditional, central enterprise data warehouses where the SQL engine determines what "intelligence" can be derived from the warehouse's relational DBMS.

The key shift that will bring this vision into reality is the new wave of embedded business intelligence (BI) agent technologies, which are well suited for service-oriented business models. By definition, network-based services are decentralized; this new services environment changes the requirements for implementing intelligent business solutions.

Embedded BI Today

The buzz around "embedded BI" is growing among BI vendors. However, merely generating and publishing reports on a scheduled basis isn't embedded intelligence. Some vendors also describe embedded BI as the integration of information into portals to implement dashboards. I would classify current discussion about such BI solutions into four categories:

BI-centered analytics. BI vendors provide views against data warehouses. These views (primarily reports) are embedded in decision-support solutions and portals. This sort of "embedding" simply renders data into formats best suited for end-user views. Analytics aren't woven into business processes. Emerging technology for integrated OLAP, online analytic mining (OLAM), text mining, and visualization from vendors such as PolyVista will improve stand-alone, BI-centered analytics. However, rather than close integration with business processes, the focus remains on traditional, after-the-fact information processing from available data warehouses.

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