On-Demand Webinar

Making Open Source Analytics Work for Business

Join Teradata and SAS and learn how to unlock the power of open source for your business.

In partnership with:  

About the webinar

Companies want their analysts to be more innovative and productive with new open source technologies. They need a way to balance creativity with governance and control. This starts with accessing and manipulating data from many different sources, in various formats and stored in different places.

With the freedom to program in different languages, like Python, R and Lua, users can quickly select and self-serve their data. That’s when organizations unlock the power of open source to solve their business challenges.

Join us to learn how SAS and Teradata help global companies use open source data and analytics to support the business needs of today’s most ambitious analytics innovators.

Join us as we discuss:

  • Unlocking the power of open source to solve business challenges.
  • Selecting and self-serving data for use in the programming language of your choice.
  • Creating repeatable processes to operationalize analytics with transparency and lineage tracking.
  • Monitoring your model performance before, during and after models are deployed.

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About the Experts

Lou Galway, Senior Solutions Architect, SAS

Lou Galway supports the SAS Global Technology Practice, data management solutions and business analytics modernization assessments (BAMA). He produces sales enablement content and works directly with sales and customers. Galway specializes in the SAS and Teradata relationship, conducting BAMAs and data management assessments.

Before 2014, Galway supported the SAS® performance management portfolio. He has worked for SAS since 2001 in pre-sales and post-sales.

Paul Segal, Data Scientist, Teradata

Paul Segal has extensive knowledge on open and closed source tools. He first started using S+ back in the early 90's and has continued using those skills with open source R.

Segal has been using Python for about 18 years and following its adoption by the data science world as the language of choice.