Jesse James Garrett Quotes

Information is expanding daily. How to get it out visually is important.

Except in expert hands, stats can get in the way of story; an array of data that might better be presented in a table instead clogs up sentences.

Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories.

The greatest moments are those when you see the result pop up in a graph or in your statistics analysis - that moment you realise you know something no one else does and you get the pleasure of thinking about how to tell them.

By visualizing information, we turn it into a landscape that you can explore with your eyes: a sort of information map. And when you're lost in information, an information map is kind of useful.

The statistics might have a Eurosceptic cast, but they are not exactly a fun read. Few of us want to wade through ONS graphs or European Commission tables.

There is a rampant tendency in any industry where someone is trying to sell something with a bunch of data, where they cherry pick a little bit... bias a little bit. This becomes quite easy when there is an enormous amount of data to cherry pick from.

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.

Put simply, if an interface is poorly designed, I will not see the data I looked for, even if it is right there on the page.

I think of myself as a translator. I just change the dry, unfeeling language of data into a visual language that allows for feeling.

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

Statistics are just a compilation of people's stories put on paper, and legislation is our attempt to address those real challenges for people.

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

Even more important than statistics is then having the staff that can take the data and ensure it's presented in a way that improves individuals and teams.

Good information architecture enables people to find and do what they came for. Great information architecture takes find out of the equation: the site behaves as the visitor expects. Poor or missing information architecture neuters content, design, and programming and devalues the site for its owners as well as the audience it was created to serve. It’s like a film with no director. The actors may be good, the sets may be lovely, but audiences will leave soon after the opening credits.

Above all else show the data.

Some of the commercial work I do is helping people to improve their presentations and add some design thinking. There are so many amazing things in science, and such great data, which can often be locked away. It's in the minds of these amazing practitioners, who can't necessarily express what they want in a visual way.

Without the hard little bits of marble which are called 'facts' or 'data' one cannot compose a mosaic; what matters, however, are not so much the individual bits, but the successive patterns into which you arrange them, then break them up and rearrange them.

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

The biggest challenge in big data today is asking the right questions of data. There are so many questions to ask that you don't have the time to ask them all, so it doesn't even make sense to think about where to start your analysis.

As storytellers, we tend to want to force-feed everything and have everybody understand everything right away, and we freak out if they don't.

Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.

To learn something from your data, the forming of a hypothesis lies with the human being, which turns into a query, which becomes a result. The problem is that there are too many queries to make, too many questions to ask.

The next darwin is more likely to be a data wonk than a naturalist wandering through an exotic landscape.

Simplification seems to be the removal of objects for the goal of making a graphic as clean and uncluttered as possible. Whereas, with optimization, it feels like there's more intelligence in that. It maintains the usability, but tries to distill something down to its essence. But with some data-sets you have to be careful because, as with linear, print journalism, it's easy to shave off facts that don't quite fit the flow.