Friday, September 25, 2015

Data Nucleus

Data Nucleus

NOTE: This post is from one of our frequent contributors to this blog, "Birdseye59604.

For a moment, look at Data as a Nucleus where Data is the Atom and the Variables are Negative (electron) charges or Positive (proton) charges, creating Data Nucleus. The atom, or data, is constant, while electrons and protons are variables. If the number of protons and electrons are equal, that atom is electrically neutral, and variables in the processes are predictable within standard deviations.

If an atom, or data, has more or fewer electrons than protons, then it has an overall negative or positive charge, respectively. Data is affected by variety of variables, with environmental and human factors as major contributors. Environmental and human factors are reliable unpredictable with only a short time-span when outcome is predictable.

Environmental and human variables shapes data nucleus. 

When conducting a risk assessment based on collected data, and predicting the future based on that data, is like collecting locations of marbles when they are dropped, and then applying assumptions that these marbles will duplicate exact same location every time. The purpose of risk assessments are to predict the future based on past history. This is impossible, since data which are collected are data nucleus, with variable forces at that moment in time. These variable forces do no carry forward with past data collected. Without applying current data nucleus, risk assessments are just opinions of common sense without future reliability.

Incorrect risk-based wildfire management is not management but opinions.

It is widespread knowledge that forests around the world rejuvenate themselves by burning fires. These fires are damaging with an extremely high monetary cost. Wildfires burns not only the forest, but destroy lives, homes, businesses, equipment, recreational structures and everything else that are in their raging path. Precision dispatching of wildfire fighting resources to predicted regions becomes highly important.
These risk-based dispatching decisions, which are based on recorded data from previous years, become assumptions and opinions unless data nucleus is applied to each data collected.


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