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Applying a Data-Informed Approach to Influence Leaders and Business Decision-Makers

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Manage episode 385569433 series 2293348
Content provided by TAG Data Science & Analytics, TAG Data Science, and Amp; Analytics. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by TAG Data Science & Analytics, TAG Data Science, and Amp; Analytics or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Ned Caron:

  • What do we mean by data-informed?
  • How does this differ from data-driven or other philosophies?
  • What are the challenges and benefits for adopting data-informed decision making?
  • What advice would you offer to someone trying to move in the data-informed direction?
  continue reading

65 episodes

Artwork
iconShare
 
Manage episode 385569433 series 2293348
Content provided by TAG Data Science & Analytics, TAG Data Science, and Amp; Analytics. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by TAG Data Science & Analytics, TAG Data Science, and Amp; Analytics or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Ned Caron:

  • What do we mean by data-informed?
  • How does this differ from data-driven or other philosophies?
  • What are the challenges and benefits for adopting data-informed decision making?
  • What advice would you offer to someone trying to move in the data-informed direction?
  continue reading

65 episodes

All episodes

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-Describe the marketing / data science dance - do we need data science for successful marketing? -What are the types of jobs for data scientists in marketing? -What are the key skills for a marketing data scientist? -What about other types of non-technical skills? Like domain knowledge or personal attributes? -How should data science talent vary based on Marketing context? -Describe your final piece of advice for anyone getting into the marketing data science space?…
 
In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Jorge Figueroa: AI Monopoly - Join or fight. Some developers are scared about AI being monopolized instead of thinking about the possibilities and opportunities new AI platforms represent in their projects. AI - A new danger to some professions or a new tool? Will use reporting as an example. Reports were created by IT specialists and were fixed and timely to create. These were mostly replaced by BI tools. Surely there were report-experts how thought BI was a danger to their work at some point in time that later migrated to using BI tools and found them a great evolution from basic reporting. Even when integrating ML or Data Science projects is faster and easier with newly available platforms and resources, experts are still needed to work out these integrations. You should not consider your expertise replaced but empowered by these new tools. Millions of research hours and dollars in investment are now at your disposal to expedite your work. I recently had a talk with some friends from the art community and they feel threatened by tools like Dall-e, Stable Diffusion, Craiyon, etc. I explained new tools often look threatening to some professions but are an opportunity. Those who learn to use them faster and better have an advantage over others. As an example, Prompt Engineering is now an important competence.…
 
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