Daily Links 04/11/2017

Demystifying data science

The key to a successful analytical model is having a robust set of variables against which to test for their predictive capabilities. And the key to having a robust set of variables from which to test is to get the business users engaged early in the process.

How machine learning is shaking up e-commerce and customer engagement

From a content perspective, [Sitecore] performs semantic analysis to:

  • Auto generate taxonomies and tagging
  • Help improve the tone of your content by analyzing for things like wordiness, slang, and other grammar-like faux pax

From a digital marketing perspective, ML can:

  • Help detect segments of your customers or audience
  • Improve the effectiveness of your testing and optimization processes
  • Provide content and product recommendations that increase the engagement time a customer spends on your website.

And from a backend perspective, it can help with fraud detection, something that every company with an e-commerce model needs to monitor actively.

Gartner 2017 magic quadrant for data science platforms: gainers and losers

Firms covered:

  • Leaders (4): IBM, SAS, RapidMiner, KNIME
  • Challengers (4): MathWorks (new), Quest (formerly Dell), Alteryx, Angoss
  • Visionaries (5): Microsoft, H2O.ai (new), Dataiku (new), Domino Data Lab (new), Alpine Data
  • Niche Players (3): FICO, SAP, Teradata (new)

Gartner notes that even the lowest-scoring vendors in MQ are still among the top 16 firms among over 100 vendors in the heated Data Science market.

Among those not on the quadrant, I’ve been impressed by DataRobot.

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