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Yet, it is a little bit surprising to see that according to Gartner report estimated that: “ 60 percent of bigdata projects will fail to go beyond piloting and experimentation, and will be abandoned.” In this blog, I reflect on how the field of DataScience/analytics can meet expectations and fulfill the anticipated value.
Pedro Domingos, PhD Professor Emeritus, University Of Washington | Co-founder of the International MachineLearning Society Pedro Domingos is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in datascience and AI.
“We hear little about initiatives devoted to changing human attitudes and behaviors around data. Unless the focus shifts to these types of activities, we are likely to see the same problem areas in the future that we’ve observed year after year in this survey.” — BigData and AI Executive Survey 2019.
” Predictive Analytics (MachineLearning): This uses historical data to predict future outcomes. Prescriptive Analytics (DecisionScience): This goes beyond prediction, using data to recommend specific actions. Data Cleaning: Process of identifying and correcting errors or inconsistencies in datasets.
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