Quotes About Big data

By definition, big data cannot yield complicated descriptions of causality. Especially in healthcare. Almost all of our diseases occur in the intersections of systems in the body.

In a slower economy, companies look for more value. The cloud provides this. So does Big Data.

Data is the exhaust of the information age.

An Internet of Things is not a consumer society. It's a materialised network society. It's like a Google or Facebook writ large on the landscape.  Google and Facebook don't have "users" or "customers". Instead, they have participants under machine surveillance, whose activities are algorithmically combined within Big Data silos.

Google and Facebook don't have "users" or "customers". Instead, they have participants under machine surveillance, whose activities are algorithmically combined within Big Data silos.

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.

One other problem is that too many people—and vendors in particular—are already using big data to mean any use of analytics, or in extreme cases even reporting and conventional business intelligence.

Data for data's sake, or the mindless gathering of big data, without any conceptual framework for organizing and understanding it, may actually be bad or even dangerous.

From aerial robotics to big data analytics, technology presents the opportunity to expedite and magnify the impact of humanitarian relief efforts through greater efficiency and responsiveness: reaching more people, sooner, more cost-effectively, and saving more lives.

In the wake of digitalization megatrends such as mobile Internet, the Internet of things, and big data, digital innovations are creating development opportunities faster than ever.

The success of Google Flu Trends became emblematic of the hot new trend in business, technology, and science: big data and algorithms. "Big data" can mean many things, but let's focus on the found data we discussed in the previous chapter, the digital exhaust of web searches, credit card payments, and mobile phones pinging the nearest cell tower, perhaps buttressed by the administrative data generated as organizations organize themselves.

But the change in tone also reflects a change in the zeitgeist between 2013 and 2016. In 2013, the relatively few people who were paying attention to big data often imagined themselves to be the carpenters; by 2016, many of us had realized that we were nails.

Census taking is among the oldest ways of collecting statistics. Much newer, but with similar aspirations to reach everyone, is "big data." Professor Viktor Mayer-Schönberger of Oxford's Internet Institute, and coauthor of the book Big Data, told me that his favored definition of a big dataset is one where "N = All"—where we no longer have to sample, because we have the entire background population.[18

In the age of Big Data, the von Neumann bottleneck has philosophical implications. The more knowledge that is put into a von Neumann machine, the bigger and more crowded its memory, the further away its average data address, and the slower its functioning.

Facebook and Twitter have a ton of information they're trying to make sense of.

Some of the network graphs that Able Danger produced were twenty feet long and almost wholly unintelligible because the print was so small.8 Krebs himself concluded that there would be no substitute for human intelligence in the war on terrorism; the alternative would be to drown in big data.

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.

Forget artificial intelligence - in the brave new world of big data, it's artificial idiocy we should be looking out for.

Philips is uniquely positioned to help reshape and optimize population health management by leveraging big data and delivering care across the health continuum, from healthy living and prevention to diagnosis, minimally invasive treatment, recovery, and home care.

While incarcerated, I had no bank statements, no bills, no credit history. In our interconnected world of big data, I appeared to be no different than a deceased person.

We need new, dynamic models for growth through the sharing economy, using big data to unlock new insights and adopting closed-loop cycles.

Social media has given companies access to unprecedented amounts of information on client behavior and preferences - so-called Big Data. But making sense of it all and turning it into actionable policy has been elusive.

Thanks to the digital and big data revolution, we can start to do what was previously unthinkable - to improve patient outcomes and lower healthcare costs while delivering personalized care to each individual.

Big data is mostly about taking numbers and using those numbers to make predictions about the future. The bigger the data set you have, the more accurate the predictions about the future will be.