I’ve talked about big data on this blog before (Demand for GIS Analysts on the Rise? and Big Data Articles Everywhere) and also mentioned it at the end of my recent talk with James Fee. So it was with much interest that I read Harvard Business Review’s headline article this month titled “Big Data: The Management Revolution” by Andrew McAfee and Erik Brynjolfsson.
In it, the authors discuss how Big Data is different from regular data and report on the results of their study of 330 public North American companies. They sought to find out if businesses that use Big Data analytics actually perform better financially than businesses that don’t. I highly recommend getting the article–which you could read at your local library if you don’t have a subscription, but I find the kindle version to be quite worth the cost–to find out what the results of the study are.
Now, I realize that those of us who specialize in spatial analytics and mapping are interested in making a difference in all kinds of different ways, not just in big-business financials, but we could assume that the outcomes of this research are applicable to increasing performance in whatever field of expertise you currently work in, whether its local government, natural resources, utilities, or something else.
In terms of natural resources, which is the field I work in, I’ve thought about it in terms of one of the longest standing analytical subjects that I’ve been involved in: salmon habitat in the Pacific Northwest. For over 10 years the approach has been to work with the salmon scientists to determine what factors are important in the salmon habitat equation, where those resources are available on the ground, how those resources might fare in the future, and where the potential risks are, to name a few relevant metrics.
And yes, those datasets can be quite large–think individual tree ages based on LiDAR (more on that here and here)–but they are not real time. One of the differences between regular data and Big Data is the real-time nature of Big Data. What I foresee as something that can make a big impact along these lines is to see data on fecal coliform levels in real time or septic system failures in real-time, for example, so that measures can be taken to immediately ameliorate their impacts to salmon. This is only one way in which Big Data could impact my work, there are, I am sure, a large number of other things that could be done once this concept takes hold, that I haven’t even begun to be cognizant of. The possibilities!
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