A Quote by Steven McDonnell
One [Big Data] challenge is how we can understand and use big data when it comes in an unstructured format.
Related Quotes
People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.
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.
We get more data about people than any other data company gets about people, about anything - and it's not even close. We're looking at what you know, what you don't know, how you learn best. The big difference between us and other big data companies is that we're not ever marketing your data to a third party for any reason.
Big data will never give you big ideas... Big data doesn't facilitate big leaps of the imagination. It will never conjure up a PC revolution or any kind of paradigm shift. And while it might tell you what to aim for, it can't tell you how to get there
Big data is great when you want to verify and quantify small data - as big data is all about seeking a correlation - small data about seeking the causation.
It amazes me how people are often more willing to act based on little or no data than to use data that is a challenge to assemble.
MapReduce has become the assembly language for big data processing, and SnapReduce employs sophisticated techniques to compile SnapLogic data integration pipelines into this new big data target language. Applying everything we know about the two worlds of integration and Hadoop, we built our technology to directly fit MapReduce, making the process of connectivity and large scale data integration seamless and simple.
Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.
EMA research evidences strong and growing interest in leveraging log data across multiple infrastructure planning and operations management use cases. But to fully realize the potential complementary value of unstructured log data, it must be aligned and integrated with structured management data, and manual analysis must be replaced with automated approaches. By combining the RapidEngines capabilities with its existing solution, SevOne will be the first to truly integrate log data into an enterprise-class, carrier-grade performance management system.
Big data has been used by human beings for a long time - just in bricks-and-mortar applications. Insurance and standardized tests are both examples of big data from before the Internet.
The first wave of the Internet was really about data transport. And we didn't worry much about how much power we were consuming, how much cooling requirements were needed in the data centers, how big the data center is in terms of real estate. Those were almost afterthoughts.
With too little data, you won't be able to make any conclusions that you trust. With loads of data you will find relationships that aren't real... Big data isn't about bits, it's about talent.
The problem with data is that it says a lot, but it also says nothing. 'Big data' is terrific, but it's usually thin. To understand why something is happening, we have to engage in both forensics and guess work.
The biggest challenge in big data today is asking the right questions of data. There are so many questions to ask that you don't have the time to ask them all, so it doesn't even make sense to think about where to start your analysis.
One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.
Let's look at lending, where they're using big data for the credit side. And it's just credit data enhanced, by the way, which we do, too. It's nothing mystical. But they're very good at reducing the pain points. They can underwrite it quicker using - I'm just going to call it big data, for lack of a better term: "Why does it take two weeks? Why can't you do it in 15 minutes?"
Related Authors
Carson Palmer Quotes
American
Footballer
Charles Woodson Quotes
American
Footballer
Cristiano Ronaldo Quotes
Portuguese
Footballer
David Beckham Quotes
English
Footballer
David Icke Quotes
English
Footballer
Gary Lineker Quotes
English
Footballer
Howie Long Quotes
American
Footballer
LeSean McCoy Quotes
American
Footballer
Pedro Quotes
Spanish
Footballer
Ray Lewis Quotes
American
Footballer
Stanley A. McChrystal Quotes
American
Footballer
Terrell Suggs Quotes
American
Footballer
Tobin Heath Quotes
American
Footballer
Tom Brady Quotes
American
Footballer
Wes Fesler Quotes
American
Footballer