Dawn Wright, Chief Scientist at Esri, presented a one-hour talk today on The Age of Science and Big Data at Colorado State University. I was in attendance due to my current interest in the topic of Big Data and, of course, how it relates to spatial scientists like ourselves. She delivered a great talk with many takeaways, some of which I will try to enumerate here.
The first interesting thing I took note of was the fact that the journal nature actually ran a cover story on Big Data back in 2008, a full three years before most people started discussing it in the spatial community. The second interesting thing was this thought that crossed my mind as Wright said the word “interoperability”. My thought? That this word, interoperability, is really one of those self-limiting words in that the minute that things actually become interoperable we will no longer have a need to use that word.
Wright mentioned GeoDesign, which in my mind is really just urban planning with GIS, but she emphasized that it is about both how we see the world and how we manipulate the world to be the way we want to see it. Just think on that for a bit.
The main learning points of this talk centered on the 3 traditional characteristics of Big Data as well as 2 additional characteristics that they think about at Esri. First, Big Data is characterized by big volume: often in the petabytes. Second, we’re talking about data that has a high velocity, by which we mean near real-time or real-time data from automated sensors, and which we have to ask questions such as: what do we keep and what do we discard? The third is variety, which she argues is the thing that geospatial experts are most interested in since we like to combine different datasets to derive novel conclusions. The two Esri add-ons are: veracity and values.
Examples of Big Data sets that Wright mentioned are: air and ship traffic, Yahoo! Finance data (# of nodes=42,000!), critical zone observatories, and GEOSS. She also mentioned the US NSF Earth Cube, which seems to be an attempt to organize and hold this data. Of course, we’ve seen attempts like this before that never got off the ground, so we’ll see if this one is any different. Wright went on to emphasize that if you are at all interested in data intensive science then you had better read The Fourth Paradigm, which Wright asserts is the text to have on the subject.
The only thing I was disappointed in was the fact that Wright did not discuss the importance of Big Data visualization, which I posit–as long as we are all adding “v” words to the list of Big Data tenets, is going to be the make-it or break-it aspect to whether results of these analyses make any difference in the world. In other words, without a good way to show-off your Big Data results, the public won’t listen in the first place, let alone try to understand. So that’s what I propose: Visualization needs to be the 6th Big Data tenet.
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