Automated Intelligent Decision Making

By Tony Shaw  |  Posted 2011-04-06 Email Print this article Print
 
 
 
 
 
 
 

Web 3.0 will enable an unprecedented level of intelligence in almost all systems and applications.

Automated Intelligent Decision Making: Amdocs

Amdocs is a $3 billion enterprise that provides “customer experience” on behalf of its clients in service industries such as telecom, health care, utilities and insurance. Creating high-quality, cost-effective customer interaction requires real-time contextual knowledge across all business channels, from internal sources, such as service disruptions and policy management, to external forces, such as social trends and competitive offers.

Only with holistic system awareness can Amdocs provide service representatives with the ability to deliver customer satisfaction and drive the profitability of the business—and do it at a scale of millions of transactions a day.

To address this need, Amdocs created a system that is predictive, personalized and self-learning. Using Web 3.0 semantic technologies, the company combines information from a variety of sources in real time to anticipate the reason for a customer’s call. Appropriate responses to customers can be generated 30 percent faster, which makes for much happier customers and more consistent, productive service operations. It also reduces customer churn and improves up-selling.

New Data Access Flexibility: U.S. Department of Defense

Supporting the business activities of the U.S. Department of Defense accounts for more than half of the DoD’s budget. A long history of piecemeal IT systems and services implementation has produced a level of complexity, redundancy and cost that is simply unsustainable.

The solution to this quagmire might be emerging from the test bed of a new SOA-based business intelligence architecture. The system uses Web 3.0 semantic technologies, from Revelytix, to create an agile, federated query environment.

The concept is relatively simple: By defining pieces of information unambiguously, they become “plug and play.” Information is usable and recombinable quickly and cheaply.

This approach to building information systems is similar to the way electronic systems are created with different combinations of standard components. The goal is to pull data in real time, as needed, from individual systems rather than going through time-consuming and time-delayed ETL (extraction, transformation and loading) processes. The system will be more flexible, lightweight, less invasive and less costly to maintain over time.

Planning and Configuration Management: Elastra

Elastra is using Web 3.0 technologies to support the automated planning of its cloud-based computing services. This functionality exploits one of the key Web 3.0 “smart” capabilities: the ability to reason, or automatically make intelligent decisions about the optimal course of action.

Consider Elastra’s high-end, large-scale service offerings, which require complex configuration-management processes to deploy multitier applications—possibly across a hybrid public/private cloud, and potentially with hundreds or thousands of security, policy, network and services options.

Elastra’s approach is to use an automated hierarchical planning tool in combination with the industry-standard Pellet reasoning engine (both from Clark & Parsia). Ordinarily, this could be accomplished with standard rules-based system approaches, but the reasoning tools allow the company to manage thousands of potential configuration possibilities within seconds, without having to specifically program every rule. This allows Elastra to keep the system updated for new options with relatively modest maintenance effort.



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Tony Shaw is publisher of the Semantic Universe journal, and educational chairman of SemTech, the world’s largest semantic technology conference. Shaw also facilitates the TTI/Vanguard strategy forum for CTOs.
 
 
 
 
 
 

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