Data science and machine learning are key technologies for enterprises that want to take advantage of the massive insights buried in their data marts, data warehouses, Apache Hadoop lakes, and spreadsheets.
But, despite the millions of dollars invested in analytics technologies, the majority of companies still struggle to establish an efficient and programmatic way to do analytics at scale. According to Gartner Inc., over 60% of models developed with the intention of operationalizing them were never actually operationalized.
Why are these investments failing to meet expectations? In this paper, we delve into today's most common data science and ML myths and offer potential solutions.