Data Science Part 2

With all the data we have now-a-day and continue to gain by the minute, where is this trend leading us?  Unlike over population which could lead to world hunger, this unprecedented data growth offers an entire field of study called Data Science.

Wikipedia defines data science as the extraction of knowledge from data and to produce data products.  Thanks to computer which has contributed to this data growth as well as to data management, it has enabled our ability to cross exam massive amount of data and to increase the utility of data science.

Two products from data science are data analytic and business analytic.  Both try to generate better outcomes by leveraging the data we collected.  Two practical examples of data analytic and business analytic are Autofill and Product Suggestion.

Autofill is a predictive feature based on what you have typed.  The computer or smartphone can automatically fill in your words.  It can be annoying if the autofill does not offer the right words.  But over time, the predictive function gets better.  It is an application of data analytic.

Product suggestion as the name implies offers item(s) you may be interested, how much and how many of the product(s) are available. This feature similar to the autofil is the attempt to provide relevant information based on your past decisions.  It is an application of business analytic.

The goal of data science, as in any science, is to provide relevant insight and meaningful outcome.  As this field gains more popularity (and more data), the promise of smarter decision making is within everyone’s reach.

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