A Quote by Ben Goldacre

Data is the fabric of the modern world: just like we walk down pavements, so we trace routes through data, and build knowledge and products out of it. — © Ben Goldacre
Data is the fabric of the modern world: just like we walk down pavements, so we trace routes through data, and build knowledge and products out of it.
My job is to analyze our data set to understand it and build products on it. I look at raw data, do the math to clean it up, and build systems to make it easy to understand.
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.
Data isn't information. ... Information, unlike data, is useful. While there's a gulf between data and information, there's a wide ocean between information and knowledge. What turns the gears in our brains isn't information, but ideas, inventions, and inspiration. Knowledge-not information-implies understanding. And beyond knowledge lies what we should be seeking: wisdom.
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 think there's data, and then there's information that comes from data, and then there's knowledge that comes from information. And then, after knowledge, there is wisdom. I am interested in how to get from data to wisdom.
Knowledge about limitations of your data collection process affects what inferences you can draw from the data.
'Data exhaust' is probably my least favorite phrase in the big data world 'cause it sounds like something you're trying to get rid of or something noxious that comes out of the back of your car.
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.
Data is just like crude. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.
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.
There will soon be streams of data coming from all manner of products - appliances, clothing, sporting goods, you name it. Wouldn't you rather live in a world where you can export the data from your son's football helmet to a new app that monitors force and impact against a cohort of high school players around the country?
We just kind of relied on written scouting reports through the eighties and even the early nineties. I've really been amazed by some of the data that's out there, especially with regards to tendencies of hitters, and certainly tendencies of pitchers as well. I would have loved to have gotten that data when I played.
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.
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.
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 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.
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