A Quote by Daniel Lyons
To make a vehicle autonomous, you need to gather massive streams of data from loads of sensors and cameras and process that data on the fly so that the car can 'see' what's around it.
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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.
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.
There will soon be streams of data coming from all manner of products - appliances, clothing, sporting goods, you name it. Wouldn't you rather live in a world where you can export the data from your son's football helmet to a new app that monitors force and impact against a cohort of high school players around the country?
I will never speculate on data. I always need to see data.
Cloud computing, smartphones, social media platforms, and Internet of Things devices have already transformed how we communicate, work, shop, and socialize. These technologies gather unprecedented data streams leading to formidable challenges around privacy, profiling, manipulation, and personal safety.
Data!data!data!" he cried impatiently. "I can't make bricks without clay.
Go out and collect data and, instead of having the answer, just look at the data and see if the data tells you anything. When we're allowed to do this with companies, it's almost magical.
A lot of people seem to think that data science is just a process of adding up a bunch of data and looking at the results, but that's actually not at all what the process is.
We are ... led to a somewhat vague distinction between what we may call "hard" data and "soft" data. This distinction is a matter of degree, and must not be pressed; but if not taken too seriously it may help to make the situation clear. I mean by "hard" data those which resist the solvent influence of critical reflection, and by "soft" data those which, under the operation of this process, become to our minds more or less doubtful.
Personal computing today is a rich ecosystem encompassing massive PC-based data centers, notebook and Tablet PCs, handheld devices, and smart cell phones. It has expanded from the desktop and the data center to wherever people need it - at their desks, in a meeting, on the road or even in the air.
Connectivity offers a great opportunity for General Motors. When you look at the investment we have made in OnStar and putting 4GLT in and the access you have to not only put data in, and we haven't really tapped into the data you can use from the vehicle.
There is so much information that our ability to focus on any piece of it is interrupted by other information, so that we bathe in information but hardly absorb or analyse it. Data are interrupted by other data before we've thought about the first round, and contemplating three streams of data at once may be a way to think about none of them.
Evolving technologies that allow economists to gather new types of data and to manipulate millions of data points are just one factor among several that are likely to transform the field in coming years.
We need a new generation of executives who understand how to manage and lead through data. And we also need a new generation of employees who are able to help us organize and structure our businesses around that data.
Knowledge about limitations of your data collection process affects what inferences you can draw from the data.
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.
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