Top 1200 Gathering Data Quotes & Sayings

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.

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.

Can you collect chaos? Not collecting, that is the ultimate gathering. What can you gather without gathering yourself.

If we had complete knowledge, you wouldn't need any intelligence gathering whatsoever. The president isn't god. We do have intelligence gathering.

The thing I'm particularly interested in is natural history. In its heyday, the mid- and late-nineteenth century, when people were going out and gathering the first huge caches of data and trying to understand what was living and growing everywhere, there was such a sense of freshness to that pursuit. It's very exciting.

This is a gathering of Lovers. In this gathering there is no high, no low, no smart, no ignorant, no special assembly, no grand discourse, no proper schooling required. There is no master, no disciple. This gathering is more like a drunken party, full of tricksters, fools, mad men and mad women. This is a gathering of Lovers.

Fifty years would seem to be time enough to prepare a definitive history of the Second World War. In an age of instant data-gathering, one might think that the historians could have arrived at a consensus for interpreting the main events of the war. In reality, no such consensus exists.

The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.

I just love that Free the Slaves does such a great job of gathering communities, gathering people together, and giving them the information, arming them, educating them. That's how we're going to change the world.

When dealing with data, scientists have often struggled to account for the risks and harms using it might inflict. One primary concern has been privacy - the disclosure of sensitive data about individuals, either directly to the public or indirectly from anonymised data sets through computational processes of re-identification.

What greater blessing to give thanks for at a family gathering than the family and the gathering.

A gathering of Democrats is more sweaty, disorderly, offhand, and rowdy than a gathering of Republicans; it is also likely to be more cheerful, imaginative, tolerant of dissent, and skillful at the game of give-and-take. A gathering of Republicans is more respectable, sober, purposeful, and businesslike than a gathering of Democrats; it is also likely to be more self-righteous, pompous, cut-and-dried, and just plain boring.

Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.

We all say data is the next white oil. [Owning the oil field is not as important as owning the refinery because what will make the big money is in refining the oil. Same goes with data, and making sure you extract the real value out of the data.]

We use nearly 5 thousand different data points about you to craft and target a message. The data points are not just a representative model of you. The data points are about you, specifically.

Sometimes you feel some artists are doing the same thing that you're doing but in a different field. But they have the same approach. Their method of research and gathering data is the same as yours.

I'm kind of fascinated by this idea that we can surround ourselves with information: we can just pile up data after data after data and arm ourselves with facts and yet still not be able to answer the questions that we have.

Facebook collects a lot of data from people and admits it. And it also collects data which isn't admitted. And Google does too. As for Microsoft, I don't know. But I do know that Windows has features that send data about the user.

You have to imagine a world in which there's this abundance of data, with all of these connected devices generating tons and tons of data. And you're able to reason over the data with new computer science and make your product and service better. What does your business look like then? That's the question every CEO should be asking.

Our intelligence communities spend a lot of time and effort gathering a lot of strands and a lot of data [on Russian hacking]. There are times where they're very cautious and they say, "We think this is what happened, but we're not certain."

People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.

Every company has messy data, and even the best of AI companies are not fully satisfied with their data. If you have data, it is probably a good idea to get an AI team to have a look at it and give feedback. This can develop into a positive feedback loop for both the IT and AI teams in any company.

For me myself, I feel it's always interesting gathering data. I have my team who do that. I think they feed me through specific things that I might find valuable.

Scientists who think science consists of unprejudiced data-gathering without speculation are merely cows grazing on the pasture of knowledge.

Wisdom in groups is earned by gathering useful data, exploring diverse perspectives, respecting different viewpoints, and then shaped through critical reflection on behalf of tangible outcomes.

Gathering of the Vibes is a gathering of the elders, a gathering of the youth, a gathering of family

If I'm in a gathering of filmmakers, I'm first and foremost a British Indian; if I'm in a gathering of British Indians, I'm a woman director. There are so many sides to who I am that I change all the time.

Data!data!data!" he cried impatiently. "I can't make bricks without clay.

A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.

I will talk about two sets of things. One is how productivity and collaboration are reinventing the nature of work, and how this will be very important for the global economy. And two, data. In other words, the profound impact of digital technology that stems from data and the data feedback loop.

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.

Any time scientists disagree, it's because we have insufficient data. Then we can agree on what kind of data to get; we get the data; and the data solves the problem. Either I'm right, or you're right, or we're both wrong. And we move on. That kind of conflict resolution does not exist in politics or religion.

I've seen people spend days, if not months, researching and gathering data, but only at the end did they finally figure out what they were really looking for; then they have to redo a lot of stuff. If after a day or so you force yourself to put together your tentative conclusions, then you'll have guidance for the rest of your research.

When an idea comes, spend silent time with it. Remember Keats's idea of Negative Capability and Kipling's advice to "drift, wait and obey". Along with your gathering of hard data, allow yourself also to dream your idea into being.

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.

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.

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.

I'm doing a lot of cognitive processing. I'm gathering research. I'm processing it. I'm arranging the data. I'm sorting out the narrative. I'm designing. It's almost as if I do all the cognitive work that you then don't have to do. I digest it, process it, and then offer something that's very easy for you to digest.

The USA Freedom Act does not propose that we abandon any and all efforts to analyze telephone data, what we're talking about here is a program that currently contemplates the collection of all data just as a routine matter and the aggregation of all that data in one database. That causes concerns for a lot of people... There's a lot of potential for abuse.

I was interested in data mining, which means analyzing large amounts of data, discovering patterns and trends. At the same time, Larry started downloading the Web, which turns out to be the most interesting data you can possibly mine.

There's a lot of power in executing data - generating data and executing data.

Our racing simulator is more about gathering data about the car, trying different setups, and trying to find speed in the actual racecar as opposed to speed in the actual driver. There's no other way to get that kind of testing in, without doing the actual event, or getting outside and spending the money to make it happen. And it costs a lot to go to the racetrack.

I don't believe in data-driven anything, it's the most stupid phrase. Data should always serve people, people should never serve data.

I'm worried about privacy - the companies out there gathering data on us, the stuff we do on Twitter, the publicly scrapeable stuff on Facebook. It's amazing how much data there is out there on us. I'm worried that it can be abused and will be abused.

Nilekani's technocratic obsession with gathering data is consistent with that of Bill Gates, as though lack of information is what is causing world hunger.

Young people live in a society in which every institution becomes an "inspection regime" - recording, watching, gathering information and storing data.

Scientific data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action. The step intermediate between the collection of data and the action is prediction.

Tape with LTFS has several advantages over the other external storage devices it would typically be compared to. First, tape has been designed from Day 1 to be an offline device and to sit on a shelf. An LTFS-formatted LTO-6 tape can store 2.5 TB of uncompressed data and almost 6 TB with compression. That means many data centers could fit their entire data set into a small FedEx box. With LTFS the sending and receiving data centers no longer need to be running the same application to access the data on the tape.

If you decide on having an alcoholic at your party, make sure it's a large gathering. This way, until the alcoholic begins removing their clothes or dangling the cat out the window, they can sort of blend in. An alcoholic at a small gathering is called an intervention.

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.

We're in this period where we're getting good data rates. I would say we're getting data rates that are like the data rates we got when we launched RealAudio in 1995.

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.

TIA was being used by real users, working on real data - foreign data. Data where privacy is not an issue.

In my view, our approach to global warming exemplifies everything that is wrong with our approach to the environment. We are basing our decisions on speculation, not evidence. Proponents are pressing their views with more PR than scientific data. Indeed, we have allowed the whole issue to be politicized-red vs blue, Republican vs Democrat. This is in my view absurd. Data aren't political. Data are data. Politics leads you in the direction of a belief. Data, if you follow them, lead you to truth.

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.

The normal way of gathering information is through sound: when you hear information that you want to gather, you look in its direction, you see what it is, if you choose you can get closer, you can see it, you can touch, and then, finally, the most committed form of data gathering is to taste it and eat it. But for the urbanite, we're cut off from our primary sense, and I want to stress that - our primary sense of gathering information about the place that we're living in - and instead, we're in a war zone.

When I'm covering a story, it's not just about gathering facts, but it's gathering the human element as well.

The data set of proxies of past climate used in Mann...for the estimation of temperature from 1400 to 1980 contains collation errors, unjustifiable truncation or extrapolation of source data, obsolete data, geographical location errors, incorrect calculation of principal components and other quality control defects.

Some people say they're gathering DNA. Perhaps they're gathering it for the future when the human race is stronger or weaker, who knows. That's science fiction and mere speculation.

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.