The Elusive Paradox

Curiosity to derive a metric however simple or complex it may be requires clear objective and precision right from the initial stages. Adding to it the modern examples of flashy dashboards and infinite interactivity, and we have all the ingredients of a recipe that can go extreme either way. All that dazzles and sparkles need not help visualize data in the best possible way.

In our efforts to derive insights from an ocean of data, all that we initially managed in the first few weeks was plain text, and then some more fields of plain text, and more such fields of plain text. It is only when we were halfway through our intense data prepping process that we gradually started to realize the opportunities ahead of us, and how much more calculations and fields we needed to implement the aesthetics in our insights. It was interesting to learn from some of the pros about how the landscape changed in exactly these same phases of data processing over the years, and how we approach similar scenarios differently today.

Some of the easily derivable figures helped us get the straightforward metrics, but then it was all up to the questions that were posed before us. Answers that were impossible with what was available to us slowly started to take shape and led to more intuitive insights which were not evident. To our delight, it was then time to choose our visuals and once the effectiveness of each variety and classification were assessed, we were able to narrow down to a few that best suits over our pages of topics.

Some of the most decisive areas of study are often on where we are headed, contrary to the usual focus of comparisons and forecasting magnitudes of change. Where we can specify and identify values of combinations that we can focus on for further investigation. The elusive areas of interest get tougher to detect amidst the noise, and that is when modern concepts of data processing help us step up the game.

Published by Rahul

Rahul is a data analyst and expert in visualizing business scenarios using data science. He has performed extensive research across varied business scenarios and datasets to come up with insightful results. Rahul is skilled in a number of programming languages and data analysis tools. When he is not busy refining business data, Rahul can be found somewhere in the Appalachian trails or in an ethnic restaurant in Chicago. All contents here are copyrighted and belongs to Rahul.

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