SO MUCH DATA, SO LITTLE TIME
Frequently, the necessary data is never recorded when it could have been, or it’s recorded for a different purpose or without the care and precision necessary for the current problem. While there are myriad potential obstacles to attaining reliable information, here are a few of the most common missteps:
• Taking inexact measurements. • Using improper and inconsistent collection procedures. • Inaccurate data recording and retrieval. • Measuring the wrong things. • Poor data management, access and security. • Information hiding (dishonesty and fraud).
BIG DATA
Flashy software and more data may not be what is needed to improve decision support. (Image by ArtHead/Getty Images)
Good data collection requires planning, dedicated effort and long-term care to avoid all these sources of error. With limited resources, this is a management effort that requires setting clear priorities and leadership because we can’t collect quality data on everything. Rather, we need to carefully focus on what we really need to know. Best-selling novelist W. Bruce Cameron wrote,
“Not everything that can be counted counts. Not everything that counts can be counted.” Tis reminds us to focus on meaning- ful, accurate measurement of our objectives, not measurement for measurement’s sake.
For a sense of how large this is, it has been estimated that print- ing one zettabyte in book form would require paper amounting to three times the trees on the Earth today. By 2020, the world data quantity is expected to be over 40 zettabytes. Tis is a truly staggering number. According to the National Oceanographic and Atmospheric Administration, 4.5 zettabytes is about equal to the number of ounces of water in all the world’s oceans.
Still, reliable information is the lifeblood of any process of under- standing. In fact, in our information age, high-quality data should be thought of as a strategic asset and a force multiplier. But, as the late David A. Schum wrote in “Te Evidential Foundations of Probabilistic Reasoning,” our current methods for gathering, stor- ing, retrieving and transmitting information far exceed in number and effectiveness our methods for putting it to use and drawing conclusions. And modern machine learning, in many cases, can make this problem worse by finding unimportant correlations that can distract from the real issue being addressed.
Carpenters teach an important lesson about the importance of having good information before taking action: “Measure twice; cut once.” Te same is true of any data collection effort. But good data is often much harder to obtain than we might expect.
126 Army AL&T Magazine Summer 2019
Te importance of sober thought about the effort required to satisfy data requirements is a major theme in Tomas Sowell’s landmark book, “Knowledge and Decisions.” Sowell, an econ- omist and social theorist, points out that, most of the time, we grossly underestimate the cost of the information collection required to make informed, top-down decisions in complex envi- ronments. Consequently, the extent to which the processes we design require detailed information is an important concern that deserves significant resources and effort upfront rather than being assumed away, leading to cost overruns or disappointing results later. Unfortunately this occurs all too often.
A great practical example of the principle of proper focus comes from Cormac Herley. He asked, “Why do Nigerian scammers say they are from Nigeria?” His counterintuitive insight is that criminals have a big data problem just like the rest of us. For them, finding gullible victims is a “needles in a haystack” prob- lem. Why? Because the number of people receptive to their scam is small. Like the rest of us, crooks have limited time and energy, and they need to quickly filter out the vast number of people who are unlikely to give up their money to focus on those who most likely will. Otherwise, they will spend too much time on the hard targets and never get to the soft ones. By making them- selves very obvious, they filter out all but the easiest victims, i.e.,
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