%0 Journal Article
%T So Much Data, So Little Knowledge: Using Formal Logic to Aggregate Data and Interpret Information
%A J. Russell Hoverman
%J About the Ed Book | ASCO Educational Book
%D 2018
%R https://doi.org/10.1200/EDBK_209527
%X Section: ChooseTop of pageAbstract <<References We, as human beings, have limited access to the enormous data feed to which we are exposed every day. We do not see the full spectrum of wave energy, we do not perceive the full audio range, and one only has to walk with one¡¯s dog to realize how limited our olfactory senses are. Even the data we could theoretically perceive are too much for us. We cannot devote attention to every ray of light that hits our retina or every sound that reaches our ears. From an evolutionary perspective, we could not survive with constant data overload. We therefore filter and abstract. There are physiologic filters to much that we perceive, and the brain does the rest. Imagine an impala in the Lowveld of Southern Africa. Because impalas are under constant pressure from predators, they do not wait until they see a full-sized lion before they run. How much is enough¡ªmovement in the grass, a warning baboon call, a flash of brown? There is a fine balance between being eaten and being exhausted. In evolutionary terms, the only meaningful outcome is survival, but for our purposes, we can use outcomes (O) as a surrogate for survival in applying logic to our current use of data. Formal logic is the use of symbols to structure how we make inferences such that the structure can be applied beyond specific cases.1-3 First think of how we aggregate data. We tend to think of entities, things in our environment. For example, for another human, we group a set of data points, or characteristics to identify an individual. We can organize that into a formula for that individual as follows: I (a1 + a2 + a3 + a4 + ¡­an). Individuals can then be grouped by relevant characteristics to define tribes or subspecies or candidates for clinical trials. One of these populations has the following structure: x{(I (a1 + a2 + a3 + a4 + ¡­an)}, where "I" is an individual with the characteristics a1 to an and x is the number of those individuals with those characteristics.. By this process, we have progressed from raw data to information about our environment. We can step back now and see where ¡°data¡± become ¡°knowledge.¡± The discrete characteristics are data points, but when put together they apply to individuals and become information. They define entities in our environment. We then take these entities and put them in situations where we can learn about them, about an intervention, or an action for which we will change our own behavior. It is only when we apply a structure to both data and information to produce an actionable result that we have knowledge.4-6
%U http://ascopubs.org/doi/full/10.1200/EDBK_209527