Data Collection Data Quality Experimentation Predictive Analytics 
O'Reilly on Data
MARCH 18, 2020
It seems as if the experimental AI projects of 2019 have borne fruit. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models.
datapine
JANUARY 31, 2022
It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.
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