I would like to start by summarizing what I believe the author is saying in this chapter: That as the world becomes further depended on data and data analysis tools for much of our daily lives and professions, those with access to this information now have a knife edge to walk. That is to say, as the benefits of further analytics increase, as do the risks and possible negative outcomes as well.
Overall, I very much agree with this author, both before and after reading the article. While this article has what is essentially a fire-hose of data to support each argument (ironic), this is a subject that has been at the forefront of my mind as of late thanks to classes and personal readings I’ve engaged in over the last year or so.
One area where I can confidently disagree with the author, however, is on the area of application of big-data to the Agriculture industry. That’s not to say that the author is wrong- quite the contrary, actually. I’d say the author would be more or less correct with their assessment of the application of big-data if the chapter was written around, say, 1975. (A year that I can substantiate with data of my own).
For example, the chapter states that:
“The Green Revolution introduced new technologies and practices involving hybrid seeds, irrigation, pesticides, and fertilizer. Even Since then though, farmers have tended to work off a fixed schedule for planting, fertilizing, pruning, and harvesting their crops without much regard for changing weather and climate conditions or the changing little details in each field; farming as an extension of the industrial age.”
It was at this point that I knew the author, to say it politely, had no concept of how a farm actually operated, in any way, whatsoever. I can confidently say this, as I grew up farming, and my family has farmed for the past 16 generations or so (the last 8 of which were in the United States). The quote above regarding the practices of farming not flexing to conditions is not only wrong, it is quite frankly the literal opposite of how farming operates. I would argue, in fact, that the Agriculture industry was actually one of the first areas where what we now call “big-data” was born- in the form of almanacs. According to Wikipedia, they have been around for thousands of years, and had as many as 400,000 being produced in England annually around the mid 16th century. An almanac is essentially the prediction of future weather events, based on previous years of complied data, as weather patterns usually are cyclical over the course of years, decades and centuries, and thus could be argued is the first wide spread example of big-data.
While modern advances in technology have made the computations easier, all of the benefits of big-data computations can, and have, been seen by farmers in the past. The “tractor being able to sense what each square inch of soil needs” as the author hints that “might be possible in the future” not has existed in actual technological form since the mid-1980s, but any farmer worth his salt and that knows basic plant science and chemical deficiencies of the field already should know. This is math that I could, and did do as a 9 year old, and I joined a major that doesn’t use math because it doesn’t come easily to me.
And in case you were wondering how I came up with the year 1975 earlier as the approximate date of data application the author was implying, his fascination with planters that were “up to 30 feet wide” made me laugh out loud. No. Seriously. We operate a relatively small farm, and we’ve been using 30ft planters for the last 25 years. Our old planter (which was ANCIENT as far as planters go) was a 30ft, and it was only the second largest model in the line-up when it was built in 1975. The largest production model planter in the world was introduced in 2009 and is 120 feet wide, 4 times the width that boggled he mind of the author.
Again, the author is absolutely correct in his assessment that big-data will make a big splash through Precision Agriculture, but he is about 45 years late.
Other than this large discrepancy in actuality in that one portion of the chapter, I entirely agree with his overall assessment that big-data and data analytics are much like nuclear energy, in that while there are significant benefits to be reaped, there are also significant risks and nefarious uses for the same technology. The cat can’t be put back in the bag as far as this technology is concerned, and it is our job as Christians to look discerningly at it it and continually re-evaluate if we are using it in a manner that is in line with being a good steward of that data.
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