RESEARCH & RESOURCES

Enabling the Citizen Data Scientist: How to Build a Bigger Business Impact with Analytics

TDWI Speaker: David Stodder, TDWI Research Director

Date: Wednesday, January 20, 2016

Time: 9:00 a.m. PT, 12:00 p.m. ET

Webinar Abstract

Data science is becoming essential to organizations seeking to gain greater business value from data. Yet, finding and keeping dedicated, high-pedigree data scientists is not easy; some even say it’s like “chasing unicorns.” A better strategy is to develop data science teams and empower business users – executives, marketing decision-makers, line of business (LOB) managers, and more – to engage in data exploration, experimentation, and development of insights that they can apply to improving business outcomes. This requires not just technology but training, attending to people, process, and governance issues, and helping personnel to define the right questions so that they can apply the most relevant analytic methods and technologies.

This TDWI Webinar will discuss how organizations can move faster with analytics and broaden the impact by enabling “citizen” data scientists – that is, business users and subject matter experts who are not highly trained in data science – to do more themselves. The Webinar will be aimed at helping organizations think clearly about what they want and expect from data science. It will talk about how to enable business users to apply data science to their organization’s most important questions. It will discuss best practices for improving the speed and relevance of data science projects. Joining TDWI Research Director of BI, David Stodder, will be Cisco’s Senior Manager of Data Science, Robert John Lake, who currently leads a team of data scientists at Cisco

Topics to be covered in this Webinar:

  • Defining data science; key trends in data science practices
  • How to focus on business questions before choosing methods and tools
  • Why communication skills are critical to realizing data science’s value
  • Best practices for making data science teams successful

David Stodder

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