Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, most architecture laid out to enable data scientist miss two key challenges:
- Data scientists spent most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientist often stay out of the reach of regular data analysts and business users
Data virtualization offers a new alternative to address these issues in a simple and elegant way.