A major challenge facing data scientists or those in similar roles, is that business analytics is understood by few. Many of those who do understand are the younger lot. And many of these further only understand the mathematical concepts, but not the predictive aspects. This renders analytics as incomplete as the human element goes missing. The top Machine Learning tools such as Google’s Deep Mind or IBM’s Watson are powered by humongous streams of data warehousing. Yet, their iteration only when customer psychologies are well understood. A lot of stakeholders, even internal ones are skeptical on using analytics. Thus, the data models to be chosen are important. Cognitive biases and knee-jerk reactions imperil the proper usage. The presence of silos ensures that vital data cannot quickly travel across spaces.


Uploaded Date:09 August 2018

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