Yes, with the added requirement that it be processed (usually aggregated or summarized) for some purpose. For example, a moving average used as a naive forecast is information — to a person who needs a naive forecast.

“Insights” is a current business buzzword. A lot of former VP’s of Marketing Research became VP’s of Insights over the past few years. It’s a cooler, less descriptive title.

Information might also be called “insights” or “intelligence” or something else, but involves some form of summary that makes the information useful to a decision maker.

This distinction is neither new nor ignored.

Are gigantic databases the new world? Sure.

Do they change the way we deal with statistics? Sure.

For one thing, there’s my old motto: “Data drives out analysis.” For example, if you are just able to keep aggregate data, you might need to model how much of a particular product to send to a store depending on size of store, seasonality, demographics of the trading area, and other analytic factors. Once you have all this data, in many cases you can simply say: what’s the average sales rate in this store at this price?

For another thing, large databases put a premium on the ability to automate analysis, so the statistician’s task isn’t to analyze a particular data set, but to figure out how to analyze data sets of this type (and define “type”) so useful results can be displayed. This puts a big premium on “robust”.

The NY Times chart on deaths in Iraq is interesting and loaded with data. I’m not sure it wouldn’t be better to use multiple charts to present different aspects of the data separately. For example, a timeline of death count by day would give a better picture of this particular stat than does looking at rows and rows of boxes (it’s hard to note days with zero deaths unless one studies the dates).

Similar timelines showing the different causes of fatalities would help to note patterns in prevalence of, say, hostile fire vs suicide bombings. I find it harder to get this from the jumbled blobs of color in the large chart than I would from multiple-series timelines.

But as Nathan has reminded me in a private communication, you often need to attract readers to your analysis with colorful, attractive, “fun” charts.