The purpose of visualization is insight, not pictures
Data is something we create, but it's also something we imagine.
The problem with data is that it says a lot, but it also says nothing. 'Big data' is terrific, but it's usually thin. To understand why something is happening, we have to engage in both forensics and guess work.
There's been the emergence of a philosophy that big data is all you need. We would suggest that, actually, numbers don't speak for themselves.
Data isn't information, any more than fifty tons of cement is a skyscraper.
Analytics is not about sitting behind a computer and pushing enter and having it produce an answer.
There's a very low amount of data literacy around people.
There is a tendency to mistake data for wisdom…
When you have mastered numbers, you will in fact no longer be reading numbers, any more than you read words when reading books You will be reading meanings.
You could claim that moving from pixelated perception, where the robot looks at sensor data, to understanding and predicting the environment is a Holy Grail of artificial intelligence.
It is the greatest truth of our age: Information is not knowledge.
There is a reasonable concern that posting raw data can be misleading for those who are not trained in its use and who do not have the broader perspective within which to place a particular piece of data that is raw.
The 'data' (given) of research are not so much given as taken out of a constantly elusive matrix of happenings. We should speak of capta rather than data.
Everyone talks about how much data's in the world. Except, actually, 80% of it is pretty blind to computers. I mean, it can store it. But if it's a movie, a poem, a song, it doesn't know what it's actually saying or doing.
Well, all information looks like noise until you break the code.
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.
Graphics has lately made a great shift towards machine learning, which itself is about understanding data.
Statistics and numbers are no good unless you have good people to analyse and then interpret their meaning and importance.
It is of equal importance with the discovery of facts to know what to do with them.
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.
