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Addressing Analytic Pain Points

Mark Hornick
Senior Director, Data Science and Machine Learning
If you’re an enterprise data scientist, data analyst, or statistician, and perform analytics using R or another third party analytics engine, you’ve likely encountered one or more of these pain points:
  • Pain Point #1: “It takes too long to get my data or to get the ‘right’ data”
  • Pain Point #2: “I can’t analyze or mine all of my data – it has to be sampled”
  • Pain Point #3: “Putting R (or other) models and results into production is ad hoc and complex”
  • Pain Point #4: “Recoding R (or other) models into SQL, C, or Java takes time and is error prone”
  • Pain Point #5: “Our company is concerned about data security, backup and recovery”
  • Pain Point #6: “We need to build 10s of thousands of models fast to meet business objectives”
Some pain points are related to the scale of data, yet others are felt regardless of data size. In this blog series, I’ll explore each of these pain points, how they affect analytics users and their organizations, and how Oracle Advanced Analytics addresses them.

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