A Quote by Deborah Meier

The only thing they [government] want is better data. But data doesn't tell people someone is well educated. It's a vicious circle. There is some myth involved. Some of this attitude has a long history.
If I was in government and running government, I think I would use the government data, because I wouldn't know where else to look, quite frankly. And if I didn't like that data, I would work hard to make sure it got better and better and better, whether it was at the state or local or federal level.
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.
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.
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.
Scientists do not collect data randomly and utterly comprehensively. The data they collect are only those that they consider *relevant* to some hypothesis or theory.
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.
AIs are only as good as the data they are trained on. And while many of the tech giants working on AI, like Google and Facebook, have open-sourced some of their algorithms, they hold back most of their data.
Government and businesses cannot function without enormous amounts of data, and many people have to have access to that data.
People treat citizens like they're some kind of unreliable source, but citizens are data. They are a data set.
Data will always bear the marks of its history. That is human history held in those data sets.
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.
I am not against the pharmaceutical companies. I love them. That's not the issue. The issue is, in some cases, when they do these clinical trials, they control the data. They analyze the data. In some cases, they even write the article. And that leads to at least the perception, if not the reality, that there's a conflict of interest.
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.
There are a number of fascinating stories included in 'The Human Face of Big Data' that represent some of the most innovative applications of data that are shaping our future.
I would only have been too pleased if someone had asked me for my data. If you really believed in your data, you wouldn't mind someone looking at it. You should be able to respond that if you don't believe me go out and do the measurements yourself.
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