IBM: Большие данные, большая картина16 февраля 2012 Развитие активности в социальных сетях и рост популярности дешевого компьютерного оборудования сделали возможным не только хранить невероятные обьемы данных, но и просматривать эти данные для выявления каких-либо трендов. Настоящий трюк в том, чтоб извлекать из этого денежную выгоду. Компании, делающие такие закономерности в больших обьемах данных, ищут корреляцию между различными показателями, надеясь определить соответствующие модели. (Новость опубликована на английском языке).
It’s not just about Big Data. For the big players in enterprise technology algorithms, it’s about finding big patterns beyond the data itself.
The explosion of online life and cheap computer hardware have made it possible to store immense amounts of unstructured information, like e-mails or Internet clickstreams, then search the stored information to find some trend that can be exploited. The real trick is to do this cost-effectively. Companies doing this at a large scale look for similarities between one field and another, hoping for a common means of analysis.
When it comes to algorithms, “if I can do a power grid, I can do water supply,” said Steve Mills, I.B.M.’s senior vice president for software and systems. Even traffic, which like water and electricity has value when it flows effectively, can reuse some of the same algorithms. Mr. Mills, speaking at a Goldman Sachs technology conference in San Francisco on Wednesday, called it “leveraging the cost structure of new mathematics.”
That kind of cross-pollination is reminiscent of the way Wall Street, starting in the 1990s, hired astrophysicists and theoretical mathematicians to design arcane financial products. Now the cost of computing has come down so much that it is useful to bring such talent to other industries. I.B.M., Mr. Mills said, is now the largest employer of Ph.D. mathematicians in the world, bringing their talents to things like oil exploration and medicine. “On the side we’re doing astrophysics, genomics, proteomics,” he said.
In the last five years, I.B.M. has spent some $14 billion purchasing analytics companies, in the service of its Big Data initiative. “We look for adjacencies” between one business and another, said Mr. Mills. “If we can’t get an adjacency, we’ll never get a return.”
The trend of looking for commonalities and overlapping interests is emerging in many parts of both academia and business. At the ultrasmall nanoscale examination of a cell, researchers say, the disciplines of biology, chemistry and physics begin to collapse in on each other. In a broader search for patterns, students of the statistical computing language known as R have used methods of counting algae blooms to prove patterns of genocide against native peoples in Central America. Online marketers look at your behavior in a number of contexts to sell you something you may not even know you wanted.
While it is attractive to contemplate the way everything may become connected to everything else, it presents a number of large challenges. The lab research model has been important for over a century in both scientific advancement and product development; soon it may also have to accommodate a search for truth based only on pattern-spotting. Nearer term, companies will have to make tough choices about where to invest and which signals to watch. Trying to do everything will still amount to doing nothing.