Innovative types of analysis-Doing the same old analysis on more data is generally a good sign youвЂ™re doing scale-up and not big data.The most promising aspect of big data is the innovation that allows a choice to trade off some aspects of a solution to gain unprecedented lower cost of building and deploying solutions. Technically this is accurate, however, many of these solutions rely on expensive scale-up machines with custom hardware and SAN storages underneath to get enough horsepower.

Cost-effective processing-As we mentioned, many of the vendors claim theyвЂ™ve been doing big data for decades.

High variety-Embraces the ability for data shape and meaning to evolve over time.

High velocity-Arriving at a very high rate, with usually an assumption of low latency between data arrival and deriving value.

High volume-Both in terms of data items and dimensionality.On the Excel team, weвЂ™ve taken pointers from analysts to define big data as data thatВ includes any of the following: They then compare notes and learn that they are in complete disagreement. Each man feels a different part, but only one part, such as the tail or the tusk. The wide range of interpretations sometimes reminds us of the old parable of the blind men andВ an elephant, where a group of men touch an elephant to learn what it is. And weвЂ™ve heard from vendors who claim to have been doing big data for decades and donвЂ™t see it as something new. WeвЂ™ve heard from some folks who thought big data was working two thousand rows of data. It is therefore unsurprising that some folks have come up with wildly different ways to define what вЂњbig dataвЂќ means. In talking with Excel users, itвЂ™s obvious that significant confusion exists about what exactly is вЂњbig data.вЂќ Many customers are left on their own to make sense of a cacophony of acronyms, technologies, architectures, business models and vertical scenarios. One of the great things about being on the Excel team is the opportunity to meet with a broad set of customers.

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