 Wednesday, March 22, 2006
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CNN:
[Robert] Propst is the father of the cubicle. More than 30 years after he unleashed it on the world, we are still trying to get out of the box. The cubicle has been called many things in its long and terrible reign. But what it has lacked in beauty and amenity, it has made up for in crabgrass-like persistence.
Reviled by workers, demonized by designers, disowned by its very creator, it still claims the largest share of office furniture sales--$3 billion or so a year--and has outlived every "office of the future" meant to replace it. It is the Fidel Castro of office furniture.
Three billion bucks last year. Whew!
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Earlier this week in a Microsoft-sponsored report from its center in Cambridge, England, scientists forecast how over the next few years experimentation will generate masses of data:
Scientists are simply becoming swamped with data. Over the last 12 months, more data has been collected than since the beginning of science, said Alexander Szalay, a professor of astronomy at Johns Hopkins University.
"There is this explosion of data and scientists, like it or not, they have to cope with it," Szalay said.
The onslaught of data has to be dealt with by the scientific process, but it is increasingly difficult as gigabytes of data has grown into terabytes, Szalay said. The amount of data will eventually exceed the raw computing power capable of absorbing it, so new tools and algorithms are needed to analyze it, he said.
It's no different, really, in the business world. With RFID and other sensing technologies coming online over the next few years the sheer amounts of raw data processed by business will rise exponentially. Sensors give us the ability to monitor at ever-increasing levels of granularity: we can track not only the movements of trucks full of strawberries, not only the location of a palette of strawberries on the truck, but the ambient temperature in and around any given basket of those red goodies, as well as the length of time since picking, the highest and lowest temperatures it was exposed to, who picked it, and how long it stayed in the warehouse.
From a technical perspective this implies considerable amounts of processing distributed out to the "edge", nearby the sensor network, with enough intelligence to detect anomalies which can be processed upstream.
And for the business a new way of creating, managing and deploying rules and processes is required: what should those pattern recognizers on the edge be looking for, and when should they report it?
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