How to Balance the Five Analytic Dimensions

So many data scientists select an analytic technique in hopes of achieving a magical solution, but in the end, the solution simply may not even be possible due to other limiting factors. It is important for organizations working with analytic capabilities to understand the various constraints of implementation most real-world applications will encounter. When developing a solution one has to consider: data complexity, speed, analytic complexity, accuracy & precision, and data size. Data Scientists, nor the organizations they work for, will be able to be the best in each category simultaneously; however, it will prove necessary to understand the trade-offs of each.  Data Complexity It is important to know as much as possible about the data. Practically, this looks like understanding the data type, formal complexity measures, tab measures of…

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