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About this Blog

As enterprise supply chains and consumer demand chains have beome globalized, they continue to inefficiently share information “one-up/one-down”. Profound "bullwhip effects" in the chains cause managers to scramble with inventory shortages and consumers attempting to understand product recalls, especially food safety recalls. Add to this the increasing usage of personal mobile devices by managers and consumers seeking real-time information about products, materials and ingredient sources. The popularity of mobile devices with consumers is inexorably tugging at enterprise IT departments to shifting to apps and services. But both consumer and enterprise data is a proprietary asset that must be selectively shared to be efficiently shared.

About Steve Holcombe

Unless otherwise noted, all content on this company blog site is authored by Steve Holcombe as President & CEO of Pardalis, Inc. More profile information: View Steve Holcombe's profile on LinkedIn

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Wednesday
Oct012008

The Economist: Know-alls (Data Mining)

The following excerpt is from The Economist article published 27 September 2008:

In November 2002 news reports revealed the existence of a big, secret Pentagon programme called Total Information Awareness. This aimed to identify suspicious patterns of behaviour by “data mining” (also known as “pattern recognition”): computer-driven searches of large quantities of electronic information. After a public outcry it was dubbed, perhaps more palatably, Terrorism Information Awareness. But protests continued, and in September 2003 Congress blocked its funding.

That, many people may have assumed, was that. But six of TIA’s seven components survived as secret stand-alone projects with classified funding. A report in February by America’s Department of Homeland Security named three programmes it operates to sniff out suspicious patterns in the transport of goods. Similar projects have mushroomed in, among other countries, Britain, China, France, Germany and Isreal ....

For the full ariticle, go to Know-alls (Data Mining).

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