Taming the Beast: Extracting Value from Hadoop

After deploying a data lake, organizations often reflect “I bet the farm on Hadoop; now what?” They have broken down the silos, mashed up structured and multi-structured data, and set up the Hadoop clusters. However, the investment has yet to pay off. The CIO wants answers. The CMO wants actionable information. The CEO wants results. Organizations need information on how to deliver on the promise of big data analytics. Where are the pitfalls to avoid? How are other organizations succeeding? What are best practices for implementing advanced/modern analytics?
















 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 







 

 

 

 

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Duration: 60 minutes

 

Join Ingo Mierswa, CTO at RapidMiner, John Myers, managing research director at leading IT analyst firm Enterprise Management Associates (EMA), and Lyndsay Wise, research director at EMA, for a discussion on how to close the loop between predictive insights and action using big data analytics.

Attendees will gain insight on:

  • How to give yourself a Hadoop reality check for those stuck in the hype of the data lake
  • Empowering analysts to anticipate the opportunities and risks of big data analytics
  • Guidance on monetizing insights buried in your multi-structured data
  • Building and deploying predictive models spanning cloud and on-premises environments
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Speakers
Lyndsay Wise, Research Director, BI & Data Warehousing, EMA

Lyndsay has over 10 years of experience in software research, BI consulting, and strategy development, specializing in software evaluation and best-fit solution selection. Her primary focus at EMA is on data integration, data governance, cloud technologies, data visualization, analytics, and collaboration.

 

Ingo Mierswa, Founder / CTO, RapidMiner

Ingo is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany.  He has authored numerous award-winning publications about predictive analytics and big data.

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