When you combine these experimental analytics with the knowledge of some-
one who understands the underlying processes generating the data, you have
a good chance of achieving results that can reasonably be relied upon. The
general goal is to make business decisions that are equal to or better than
those that can be made by humans.
Data Visualization
Data visualization is the last and often overlooked part of the process and
involves examining outputs from the prior stages. The best results often involve
equal parts of computer science, art and neuroscience, to understand the
intricacies of how we perceive visual input. The old adage “a picture is worth a
thousand words” certainly comes into play here.
We need to point out two things. First, we mentioned outputs from prior states.
These techniques and technologies are not only valuable for the final output of
your analytics, but can also be incredibly useful when going through the diffi-
cult process of data munging. Remember, we are talking about Big Data here,
and that generally entails more columns and rows of data than we can get our
Figure 1: This data visualization algorithm example identifies two distinct groups, just by analyzing
users’ activities. A surprising characteristic distinguishes yellow and blue dots: females and males.
Source: KDnuggets
58 | THE DOPPLER | WINTER 2018