Top 1200 Data Quotes & Sayings

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.

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.

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.

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.]

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.

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

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

Welcome to the information age. Data, data, everywhere, but no one knows a thing.

I love that the world is data intensive … unfortunately, it's called 'Big Data.'

Design has a powerful impact on the viewer. It has authority, and data also has the same air of authenticity and detail. It can be hard to argue with a graph, and it's hard to argue with data. So to combine data with a strong visual impact creates a powerful message.

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.

What is clear is that users own their data and should have control of how their data is used.

Knowledge about limitations of your data collection process affects what inferences you can draw from the data.

Size doesn't matter, fast data is better than big data

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.

Most of 'big data' is a fraud because it is really 'dumb data.'

I will never speculate on data. I always need to see data.

Teach where data can be found or how it can be derived, not the recording of data.

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.

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.

Data is cost. It takes money to create data, store it, clean it, and throw resources at it to learn anything from it.

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.

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.

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.

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.

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.

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.

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.

If the data do not prove that indexing wins, well, the data are wrong.

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.

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.

We have way more unsupervised data in the world than supervised data.

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 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.

There are two sources of error: Either you lack sufficient data, or you fail to take advantage of the data that you have.

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.

We need a basic protection for people having access to their data and knowing where their data is.

Data is the new science. Big Data holds the answers. Are you asking the right questions?

scientists ... resist ... making more of the data than the data make of themselves.

We know now data is so powerful, and you can learn so much about yourself and creating product with data.

Every day we go over data and use science and data to drive policy and decision-making.

While hard data may inform the intellect, it is largely soft data that generates wisdom.

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.

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.

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.

Nobody should try to use data unless he has collected data.

When I started out as an equity analyst, we had no securitization data. We relied on company data.

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.

My answer to someone who is in contrast with me - by not seeing God in the scientific data - is that you don't see God in the scientific data because you're not me. I have other experiences than you have, that bring me to look at this data as enriching my experience of God.

I wanted to separate data from programs, because data and instructions are very different.

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

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.

I promoted Hyderabad to the world by saying that there was privacy in India and their data will be sage. Data is wealth.

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.

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.

On the allegation of withholding temperature data, we find that CRU was not in a position to withhold access to such data or tamper with it.

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.

Random search for data on ... off-chance is hardly scientific. A questionnaire on 'Intellectual Immoralities' was circulated by a well-known institution. 'Intellectual Immorality No. 4' read: 'Generalizing beyond one's data'. [Wilder Dwight] Bancroft asked whether it would not be more correct to word question no. 4 'Not generalizing beyond one's data.