Data Quotes
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We need a data network that can easily carry voice, instead of what we have today, a voice network struggling to carry data.
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It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.
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There is no data that can be displayed in a pie chart, that cannot be displayed BETTER in some other type of chart.
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Don't measure anything unless the data helps you make a better decision or change your actions. If you're not prepared to change your diet or your workouts, don't get on the scale.
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One of the best things data can enable us to do is to ask questions we didn't know to ask.
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If someone's criticism is completely unfounded on data, then I don't want to hear it. It doesn't hold up to scrutiny.
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Data is not useful until it becomes information.
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Even the best data security systems can't protect private taxpayer information from entrepreneurial foreign businesses than can make huge profits selling U.S. taxpayer information.
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Data is not intelligence.
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Uncontrolled access to data, with no audit trail of activity and no oversight would be going too far. This applies to both commercial and government use of data about people.
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We must be careful not to confuse data with the abstractions we use to analyse them.
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This is where the world is going: direct access from anywhere to any type of data, whether it's a small piece of data or a small answer but a long algorithm to create that answer. The user doesn't care about this.
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In the future, I think movies are going to be more of data sets that viewers have a hand in controlling - where the narrative originates and what happens to the content.
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When you read findings like the one above, and see that Jamal doesn’t get the job, it’s easy to shake your head at the few racist hiring managers who’ve tilted the odds against him. But the data we see in this chapter shows racism isn’t a problem of outliers. It is pervasive.
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If someone does a study which, for statistical reasons, I think is hopelessly underpowered or nonidentified, my best and most useful advice will not be tips on how to calculate p-values better, or how to construct an explanation for some particular data pattern. Rather, my advice will be to start over, to reconsider what you think you already know, maybe to question some prominent work in your subfield, and quite possibly to think a lot harder about measurement, and about the relation of your data to your underlying constructs of interest.
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I related to the whole hippie, acid-test confluence of the early Internet. The idea that we should be open and interoperate with our data resonated with me.
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We have so much data on the web, almost all of it available for free, that we dive into the the data ocean hoping that magically awesome things will follow. They never do.
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We cannot be guided by wishful thinking. We need objective data.
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I think big data companies only like good news. So I think they're just hoping that they don't get sued, essentially.
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I don't use scientific data as a foundation for believing in God - I use it as an enrichment of my knowledge of God.
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I try and wake up relatively early. I listen to some music and check Twitter. I also make sure I weigh myself and check how long I slept. I do that because knowing that data seems better than not knowing it.
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In God we trust, everybody else bring data to the table.
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If I see something that seems out of sync with what's already known, the first thing I do is try to find out what's wrong with the data. Once you've done that, and it still seems wrong, that's when things get interesting. It means you've found something new to understand.
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You cannot write well without data.
