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Apr 15, 2016 · post

Assessing Data Science Maturity

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We’re getting excited for our Data Leadership Conference, which is set for April 28 in New York City! The conference will feature an expert panel where Haile Owusu (Mashable), Claudia Perlich (Dstillery), and Kirk Borne (Booz Allen Hamilton) will share practical insights on how to build data capabilities within complex organizations. 

We’ll discuss questions like: 

  • What skills should organizations combine in data science teams?
  • Why should organizations invest in experiments that may fail?
  • How can organizations operationalize a rigorous, scientific method when answering questions with data?
  • How should proofs of concept be structured to ensure they provide value?
  • How do organizations know they are asking the right questions with data?
  • What are the first steps to breaking down data silos and democratizing access across the organization?
  • What can data scientists do to spread the mindset and practices of data science to other departments resistant to these practices?
  • And many more!

To get started thinking about these questions, have a look at Ten Signs of Data Science Maturity, a new (free!) O’Reilly Book by Kirk Borne and Peter Guerra. The book provides a great introduction to criteria organizations can use to evaluate their “data capabilities,” or cultural qualities that “support and make the most of data science.” Using clear, accessible (and often witty) prose, the authors cover issues like data democratization, agile data product development, executing on the scientific method, and valuing the importance of visualizations and storytelling. 

And come meet your peers across industries to discuss how to put these principles into practice April 28!

- Kathryn

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Ongoing posts and code documenting the process of building a question answering model.
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Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

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