David Weinberger Quotes

The advantage of the analytical approach is that it is widely applicable, and it can provide a considerable amount of quantitative information even with a relatively poor resolving power.

During this time I had the singular good fortune of being able to discuss the problem constantly with Einstein. Some experiments done at Einstein's suggestion yielded no decisively new result.

I never did any training in journalism or in finance, so I really was in the deep end. I got very good at going to press conferences and nodding. I'd figure it out when I got back to the office. Charts and numbers. I've never been great with facts, ever, my whole life. For a journalist, that's not a very good trait.

In the future, I think it's pretty plausible that collective intelligence tools and skills will be important in order to be a part of global dialog, global business, and global creativity. People who know how to negotiate collective intelligence networks are going to be in a good position to contribute to global society.

Take the situation of a scientist solving a problem, where he has certain data, which call for certain responses. Some of this set of data call for his applying such and such a law, while others call for another law.

I love data. I think it's very important to get it right, and I think it's good to question it.

Math is a language that you use to describe statistics, but really it's about collecting information and putting it in an order that makes sense.

I think that there are changes that have occurred in technology that make is that more people can have the same level of information that I have. My advantage is that I'm very good at interpreting the information.

There's something that happens with the collection of a large amount of data when it's dumped into an Excel spreadsheet or put into a pie chart. You run the risk of completely missing what it's about.

No observational problem will not be solved by more data.

You have to put together bits and pieces of information before you can draw overall conclusions.

Just as houses are made of stone, so is science made of facts; but a pile of stones is not a house, and a collection of facts is not necessarily science

The best moments are when, together with... you bring information, you bring data to bear in a way that helps illuminate something that you just don't really understand. Even if it doesn't completely clarify it, it just, you know, helps bring it together.

The job of the data scientist is to ask the right questions.

As a consultant at McKinsey, I learned the value of data and the ability to shape that information into an answer.

Sometimes one must make extended conclusions from limited data.

The trouble with taxonomic boxes is... that that they tend to be empty, however beautiful they are on the outside.

It is difficult finding intelligence that is actionable in a lot of these places, but we have to keep trying.

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

It's amazing how much data is out there. The question is how do we put it in a form that's usable?

Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowcharts; they'll be obvious.

The analysis of data will not by itself produce new ideas.

Our problems are not with the data, itself, but arise from our interpretation of the data.

The impulse to collect data preceded the ability to make sense of it. People facing a complicated problem measure whatever they can easily measure. But the measurements by themselves don’t lead to understanding.

If the statistics are boring, you've got the wrong numbers.