I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.
From Gurjeet Singh
To learn something from your data, the forming of a hypothesis lies with the human being, which turns into a query, which becomes a result. The problem is that there are too many queries to make, too many questions to ask.
The number of queries in a large dataset is exponential, and it's growing exponentially. No matter how fast you make your system, you're never going to be able to get all that information.
We don't use the term 'big data' - not on our website, not with customers. Saying it sets up expectations, the wrong expectations.
People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.
Technologies like Ayasdi's exist now to automatically discover information from data without having someone making guesses up front.
If you know people with Type 2 diabetes, there's a high likelihood they will have different medication regimes and different lifestyle options. When we label all these various types as the same thing, we treat them the same way, and they should not be treated the same way.
Every company today is a data company whether they realize it or not.
The world is looking for big data scientists, and there just aren't enough to go around.
Data is cost. It takes money to create data, store it, clean it, and throw resources at it to learn anything from it.
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