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Data Science is Changing, Data Scientists must Adapt – Here’s Why and How

Summary:  Deep changes are underway in how data science is practiced and successfully deployed to solve business problems and create strategic advantage.  These same changes point to major changes in how data scientists will do their work.  Here’s why and how.

What’s Happening Now

Advanced analytic platforms are undergoing several evolutionary steps at once.  This is the final buildout in the current competitive strategy being used by advanced analytic platforms to capture as many data science users as possible.  These last steps include:

  1. Full integration from data blending, through prep, modeling, deployment, and maintenance.
  2. Cloud based so they can expand and contract their MPP resources as required.
  3. Expanding capabilities to include deep learning for text, speech, and image analysis.
  4. Adopting higher and higher levels of automation in both modeling and prep reducing data science labor and increasing speed to solution. Gartner says that within two years 40% of our tasks will be automated.

Here are a few examples I’m sure you’ll recognize. 

  • Alteryx with roots in data blending is continuously upgrading its on-board analytic tools and expanding access to third party GIS and consumer data such as Experian.

Click here to read the full article written by Bill Vorhies.

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