Whole to part panorama

 Any new initiative brings with it several bytes of data to start with. When we start with the goal to derive insights, evaluating the available raw data becomes the only activity for days. Fields stop making sense with relation to another if at all when we challenge it enough but often enables modeling with precision with regard to the context.

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 It all began with a few spreadsheets with lots of related data from respective areas, the discovery activity started with all the enthusiasm and energy similar to past activities. When we dived deeper to build the whole picture we started to struggle in putting the individual parts together. It seemed more complex than we anticipated, after adding moving and rolling aspects to current key measures. The more we focused on deriving the key metrics, the more the data fought back. 

It was then a transformative journey where narrowing down from the goal backward for a change proved helpful. Adding in the variables and factors to account for precision along the way, moving ahead and backward in timelines, until it looked well enough to be stable. Few metrics that apparently did not look convincing enough proved useful in enhancing the accuracy of our insights. 

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A data discovery journey is not without its fair share of hurdles, but it gets more exciting when we are able to create something more than what actually existed before and what we hoped to achieve. Overturning the conventional part to whole relationships and stereotypes it was an incredible satisfaction on being able to finish painting the final picture. 

Author: Rahul Bhattacharya

Rahul is a journalist with expertise in researching a variety of topics and writing engaging contents. He is also a data analyst and an expert in visualizing business scenarios using data science. Rahul is skilled in a number of programming languages and data analysis tools. When he is not busy writing, Rahul can be found somewhere in the Appalachian trails or in an ethnic restaurant in Chicago.

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