by Angela Guess
A recent article discusses the importance of Big Analytics in addition to Big Data, noting that, “IBM’s Watson‘s impressive Jeopardy! win demonstrated the awesome strides in computing power and ingenuity, but just as impressive was the way in which Watson’s creators attacked an avalanche of information to come out victorious. Notably, Watson wasn’t concerned with big data alone. ‘Big data’ is often cited as the core problem holding back companies from gaining a competitive advantage in this age of information overflow. Most organizations are fairly adept at capturing that information, but what ultimately matters is what they do with it, how quickly they utilize it to glean value. This is ‘big analytics.’ And though Watson is clearly a different animal than database analytics solutions for business, fundamentally, Watson is big analytics.”
The article continues, “Working from just a single terabyte of data, Watson performed complex analyses at incredibly high speeds to come up with correct answers. For those of us in the business of data storage and analytics — in fact, most companies — this illustrated the power and challenge of big analytics, not just big data.”
It goes on, “A recent IDC report predicts data will grow some 44 times over the course of the next decade! Too often, the industry focuses its attention primarily on this piece of the data problem. But today, those are simply big numbers. But the second piece, often ignored or pushed aside, is the problem of big analytics, because even 100 terabytes of data is entirely useless if companies haven’t solved the big analytics problem. This of course includes the aforementioned problems of scale. But modern analytic platforms must also be extremely fast in answering creative, often difficult questions drawn from multiple sources in a variety of programming languages. That is, these platforms require velocity, agility and the capacity to deal with complexity.”
photo credit: IBM
