# Physical Perception of Big Data: Enhencer's Multivariate Analysis

The qualitative perspective of the big data discussed a lot for the last
few decades. In fact, the stress was being directed onto the qualitative
side rather than the quantitative side. If I know something even a little
bit about the latest paradigm, it is that *nothing is one-sided.* Big
data is no exception. However, the qualitative side of it has more
importance than the quantitative side in more than one situation. Anyways,
we must, in order to have a consistent analysis, grasp both sides. All and
all, the definition comes from the quantitative side, or it seems so. In
fact, the correct way to put **big data** into the analysis field is to
differentiate both sides and to get the full perception of it. Hence, only
a **multivariable analysis** may achieve that without hesitation.

While we are analyzing the sample, we only see coefficients, variables and
other numbers that are affecting the dependent variable. However, all
those numbers in the sample are just the simplified versions of human
behavior. Thus, what we call big data, as quantitatively, is just
**marginal propensity of human beings’ deviation from different
actions.**
That is, with every agent’s deviation from its actions, a mapping can be
drawn depicting the movements of the behavior in a minor scale. When we
expand the image and started to see larger-scale i.e. marginal deviation
of female students who are taking the bus to school rather than taking the
metro is a physical form of the data. What we are defining here is not
whether sample points are behaving rational, but we are trying to define
the main trends in the data set so as to
*predict the future behavior of the same sample group (or current
behavior of different sample groups).*

Having analyzed the data with multiple variables, we can conclude on a
verdict. However, the big data analysis is not about reaching cut-edge
verdicts. In fact, the most definite reason of big data analysis is to get
help to construct a **prediction model.** Although, it sounds easy,
constructing a prediction model is not an easy task. However, the
Enhencer’s ability to use multiple variables at the same time blazes the
way for a better understanding of the physical perception of big data.

The discussion on data sometimes gets complex. In fact, taking something physical and turning it into a something numerical is hard, while again turning numerical examination into something tangible is more than complex. Although, at first sight it looks nonsense, the second evolution of tangibility eases the way for prediction models to be constructed. Thus, elaborating a human behavior, at first sight, is almost impossible, turning that behavioral example into a numerical model cleans the sharp edges of behavior i.e. unrelated parts that will cause biases in the model. Furthermore, obtaining all the information into a pool and clustering them will assist model creators to predict with more significant results.

To sum up, understanding big data is no easy task; however, using multiple
variables to erase biases and residual effects would definitely help model
constructor. Thinking multiple-sided rather than single-sided will help
one to understand that big data is more than just bigger in the
quantitative or qualitative means. The correct physical perception of it
comes from the infinite number of alternatives that every sample point
holds. Therefore, whether one tries to comprehend big data for practical
purposes or not, the main objective here will be to understand
*which side of the big data would help them to solve the ongoing
problem.*

In conclusion, although complex models are mathematically superior to
simple ones, the market naturally wants **simpler** and
**result-oriented**
models whether they are theoretical or atheoretical as long as they
suggest solutions to initial and further problems.