Big Data Quotes

Quotes tagged as "big-data" (showing 1-30 of 31)
Seth Stephens-Davidowitz
“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.”
Seth Stephens-Davidowitz, Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

Friedrich Engels
“A change in Quantity also entails a change in Quality”
Engels Friedrich

Cathy O'Neil
“At the federal level, this problem could be greatly alleviated by abolishing the Electoral College system. It's the winner-take-all mathematics from state to state that delivers so much power to a relative handful of voters. It's as if in politics, as in economics, we have a privileged 1 percent. And the money from the financial 1 percent underwrites the microtargeting to secure the votes of the political 1 percent. Without the Electoral College, by contrast, every vote would be worth exactly the same. That would be a step toward democracy.”
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Edward R. Tufte
“To clarify, *add* data.”
Edward R. Tufte

Amit Ray
“The greatest danger of Big data and Artificial Intelligence is robots and bots will track you and manipulate you in every step.”
Amit Ray, Peace on the Earth A Nuclear Weapons Free World

Liu Cixin
“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.”
Liu Cixin, The Three-Body Problem

Pearl Zhu
“We are moving slowly into an era where Big Data is the starting point, not the end.”
Pearl Zhu, Digital Master

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

Jon Ronson
“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.”
Jon Ronson, The Psychopath Test: A Journey Through the Madness Industry

“Huge volumes of data may be compelling at first glance, but without an interpretive structure they are meaningless.”
Tom Boellstorff, Ethnography and Virtual Worlds: A Handbook of Method

Paul Gibbons
“The human side of analytics is the biggest challenge to implementing big data.”
Paul Gibbons, The Science of Successful Organizational Change: How Leaders Set Strategy, Change Behavior, and Create an Agile Culture

“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.”
John Hagel III

Dexter Palmer
“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.”
Dexter Palmer, Version Control

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

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

“THIS JET ERA IS RUN/DRIVEN BY DATA. YOUR ANALYSIS WOULD DETERMINE YOUR VALIDITY.”
Wisdom Kwashie Mensah

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scalsys.com

Cathy O'Neil
“Will those insights be tested,or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies use data, I'm often reminded of phrenology, a pseudoscience that was briefly the rage in the nineteenth century. Phrenologists would run their fingers over the patient's skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits that existed in twenty-seven regions of the brain. Usually the conclusion of the phrenologist jibed with the observations he made. If the patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation - which, in turn, bolstered faith in the science of phrenology. Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and black-balled foreign medical students and St. George's can lock people out, even when the "science" inside them is little more than a bundle of untested assumptions.”
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Cathy O'Neil
“Will those insights be tested, or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies use data, I'm often reminded of phrenology, a pseudoscience that was briefly the rage in the nineteenth century. Phrenologists would run their fingers over the patient's skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits that existed in twenty-seven regions of the brain. Usually the conclusion of the phrenologist jibed with the observations he made. If the patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation - which, in turn, bolstered faith in the science of phrenology. Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and black-balled foreign medical students and St. George's can lock people out, even when the "science" inside them is little more than a bundle of untested assumptions.”
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Cathy O'Neil
“From a mathematical point of view, however, trust is hard to quantify. That's a challenge for people building models. Sadly, it's far easier to keep counting arrests, to build models that assume we're birds of a feather and treat us as such. Innocent people surrounded by criminals get treated badly, and criminals surrounded by law-abiding public get a pass. And because of the strong correlation between poverty and reported crime, the poor continue to get caught up in the digital dragnets. The rest of us barely have to think about them.”
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

“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!”
James Scott, Senior Fellow, Institute for Critical Infrastructure Technology

“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".”
Neil Dunlop

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Charles Duhigg
“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.”
Charles Duhigg, The Power of Habit: Why We Do What We Do in Life and Business

“Unlike oil, Big Data’s reserves are growing exponentially every year.”
Khang Kijarro Nguyen

“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.”
Eugene C. Goldfield, Bioinspired Devices: Emulating Nature’s Assembly and Repair Process

Seth Stephens-Davidowitz
“If you can't understand a study, the problem is with the study, not with you.”
Seth Stephens-Davidowitz, Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

“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.”
Luke Lonergan

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

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