Best 25 quotes in «big data quotes» category

THIS JET ERA IS RUN/DRIVEN BY DATA. YOUR ANALYSIS WOULD DETERMINE YOUR VALIDITY.

To clarify, *add* data.

Unlike oil, Big Data’s reserves are growing exponentially every year.

A change in Quantity also entails a change in Quality

We classify things for the purpose of doing something to them. Any classification which does not assist manipulation is worse than useless.

You can keep the Office of Personnel Management records, I don't need Electronic Health Records, give me the metadata, big data analytics and a custom tailored algorithm and a budget and during election time, I can cut to the psychological core of any population, period!

We are moving slowly into an era where Big Data is the starting point, not the end.

And yet Rebecca felt that it was hard to tell whether the secret algorithms of Big Data did not so much reveal you to yourself as they tried to dictate to you what you were to be. To accept that the machines knew you better than you knew yourself involved a kind of silent assent: you liked the things Big Data told you you were likely to like, and you loved the people it said you were likely to love. To believe entirely in the data entailed a slight diminishment of the self, small but crucial and, perhaps, irreversible.

Aim for simplicity in Data Science. Real creativity won’t make things more complex. Instead, it will simplify them.

Although the method is simple, it shows how, mathematically, random brute force can overcome precise logic. It's a numerical approach that uses quantity to derive quality.

Huge volumes of data may be compelling at first glance, but without an interpretive structure they are meaningless.

As one Google Translate engineer put it, "when you go from 10,000 training examples to 10 billion training examples, it all starts to work. Data trumps everything.

Here Luke Lonergan has discussed the numerous advantages of big data and its ways which will provide the help to increased customer engagement and reduce the cost.

At the heart of the decoding problem is how to understand the vast information contained in neural signals, the challenge of what is being called "big data". For neuroscientists, big data is a means for exploring populations of neurons to discover the macroscopic signatures of dynamical systems, rather than attempting to make sense of the activity of individual neurons. Two surprising results from numerous experiments recording from neurons in different brain regions have revealed a wonderful secret of nature about the relation between the number of neurons recorded and and their dimensionality (the number of principal components required to explain a fixed percentage of variance). First, the dimensionality of the neural data is much smaller than the number of recorded neurons. Second, when dimensionality procedures are used to extract neuronal state dynamics, the resulting low-dimensional neural trajectories reveal portraits of the behavior of a dynamical system. This means that it may not be necessary to record from many more neurons within a brain region in order to accurately recover its internal state-space dynamics.

Big data is based on the feedback economy where the Internet of Things places sensors on more and more equipment. More and more data is being generated as medical records are digitized, more stores have loyalty cards to track consumer purchases, and people are wearing health-tracking devices. Generally, big data is more about looking at behavior, rather than monitoring transactions, which is the domain of traditional relational databases. As the cost of storage is dropping, companies track more and more data to look for patterns and build predictive models".

The next Freud will be a data scientist. The next Marx will be a data scientist. The next Salk might very well be a data scientist.

If you can't understand a study, the problem is with the study, not with you.

Nein, die Kirche hatte es nicht leicht in Zeiten, in denen das ewige Leben eine Aufgabe von Programmierern geworden war.

The human side of analytics is the biggest challenge to implementing big data.

Then I get worried that if anyone is really paying attention to Happy's predilections, they might become wary of his wholesale compassion and suspect him of being an imaginary character, created by a journalist, to trick businesses into inadvertently revealing their data-trafficking practices. So I untick tigers.

Os políticos são um pouco como músicos, e o instrumento que eles tocam é o sistema emocional e bioquímico humano. Eles fazem um discurso e uma onda de medo varre o país. Publicam um tweet e há uma explosão de ódio. Não creio que devamos dar a estes músicos um instrumento mais sofisticado no qual tocar. Uma vez conseguindo manipular diretamente as nossas alavancas emocionais, gerando ansiedade, ódio, satisfação e tédio à sua vontade, a política vai tornar-se um mero circo emocional.Por muito que devamos recear o poder das grandes empresas, a História sugere que não estamos mais seguros nas mãos de governos todo- poderosos. Em janeiro de 2018, prefiro que as minhas informações estejam nas mãos de Mark Zuckerberg do que nas mãos de Vladimir Putin(...)

Pull approaches differ significantly from push approaches in terms of how they organize and manage resources. Push approaches are typified by "programs" - tightly scripted specifications of activities designed to be invoked by known parties in pre-determined contexts. Of course, we don't mean that all push approaches are software programs - we are using this as a broader metaphor to describe one way of organizing activities and resources. Think of thick process manuals in most enterprises or standardized curricula in most primary and secondary educational institutions, not to mention the programming of network television, and you will see that institutions heavily rely on programs of many types to deliver resources in pre-determined contexts. Pull approaches, in contrast, tend to be implemented on "platforms" designed to flexibly accommodate diverse providers and consumers of resources. These platforms are much more open-ended and designed to evolve based on the learning and changing needs of the participants. Once again, we do not mean to use platforms in the literal sense of a tangible foundation, but in a broader, metaphorical sense to describe frameworks for orchestrating a set of resources that can be configured quickly and easily to serve a broad range of needs. Think of Expedia's travel service or the emergency ward of a hospital and you will see the contrast with the hard-wired push programs.

Starting a little over a decade ago, Target began building a vast data warehouse that assigned every shopper an identification code—known internally as the “Guest ID number”—that kept tabs on how each person shopped. When a customer used a Target-issued credit card, handed over a frequent-buyer tag at the register, redeemed a coupon that was mailed to their house, filled out a survey, mailed in a refund, phoned the customer help line, opened an email from Target, visited Target.com, or purchased anything online, the company’s computers took note. A record of each purchase was linked to that shopper’s Guest ID number along with information on everything else they’d ever bought. Also linked to that Guest ID number was demographic information that Target collected or purchased from other firms, including the shopper’s age, whether they were married and had kids, which part of town they lived in, how long it took them to drive to the store, an estimate of how much money they earned, if they’d moved recently, which websites they visited, the credit cards they carried in their wallet, and their home and mobile phone numbers. Target can purchase data that indicates a shopper’s ethnicity, their job history, what magazines they read, if they have ever declared bankruptcy, the year they bought (or lost) their house, where they went to college or graduate school, and whether they prefer certain brands of coffee, toilet paper, cereal, or applesauce. There are data peddlers such as InfiniGraph that “listen” to shoppers’ online conversations on message boards and Internet forums, and track which products people mention favorably. A firm named Rapleaf sells information on shoppers’ political leanings, reading habits, charitable giving, the number of cars they own, and whether they prefer religious news or deals on cigarettes. Other companies analyze photos that consumers post online, cataloging if they are obese or skinny, short or tall, hairy or bald, and what kinds of products they might want to buy as a result.

The greatest danger of Big data and Artificial Intelligence is robots and bots will track you and manipulate you in every step.

The only way to stop big data from becoming big brother is introduce privacy laws that protect the basic human rights online.