TAG Data Talk

-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 Hua Ai:

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• What are the types of data science work able to get automated, managed with AI?
• How can AI help, what steps are reduced 
• How does this change the necessary skill set?
• Are we writing ourselves out of a job?
• Final piece of advice for leveraging AI to reduce data science barriers 

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Giang Do:

• What is a Data Science career?
• Value of Experience, Exposure and Knowledge.
• What are the tricks to a successful career?
• Tips you wish you had known.

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Deepac Jose:

• what are some of the capabilities
• importance of challenging status quo
• breaking silos
• how to know you’ve won
• final piece of advice

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?

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.

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Dinakar Deshmukh:

• what does it mean for a solution to be scalable
• how have we lacked in this area as an industry 
• why it’s important to make solutions that last
• the future of data science products and scalability

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Chad Harness:

• describe automl to the business person
• how do these types of tools work
• what are the right ways to leverage automl
• expectations for the future of automl tools

In this episode of TAG Data Talk, host Dr. Beverly Wright talks with Justin Grieshop:

• Describe the backgrounds and disciplines in data science and analytics
• How to show the importance of diversity of thought
• Advice for understanding the right education and how to extract value from those resources

In this episode of TAG Data Talk, we talk with Patrick Halpin about Building and Managing the Data Science Product Development Process

In this episode of TAG Data Talk, we talk with Lyndsey Padden about the Evolution of Data Scientists: The Transitioning Role of Data Scientist

In this episode of TAG Data Talk, we talk with Nan Li about Dispelling the AI Hype

In this episode of TAG Data Talk, we talk with Kevin Enyekwe and Chanyong Yoo about Developing and Delivering a Quick Data Science project

In this episode of TAG Data Talk, we talk with Stephanie Dean about Enabling an Analytics Culture through Transformative Processes

In this episode of TAG Data Talk, we talk with Ken Viciana about Leading Data Scientists

In this episode of TAG Data Talk, we talk with Johnny Howze on how to Power Up with Data

In this episode of TAG Data Talk, we talk with Eric Friedman about Leveraging Data Science for Actionable Insights

In this episode of TAG Data Talk, we talk with Anqi Zou about connecting and leveraging academic partnerships in data science 

In this episode of TAG Data Talk, we talk with Frank Borovsky about Unlocking Data along the Value Chain in Manufacturing

In this episode of TAG Data Talk, we talk with Selma Dogic about strategies to understand true problems and questions for data science projects

In this episode of TAG Data Talk, we talk with Christina Stathopoulos about building and advancing a data science career.

In this episode of TAG Data Talk, we talk with Scott Taylor about Aligning IT data function with data science efforts.

In this episode of TAG Data Talk, we talk with Manuj Aggarwal about Data Science and Artificial Intelligence from a CTO's perspective.

In this episode of TAG Data Talk, we talk with Peggy Tsai about Enabling AI Efforts by Improving Data Governance. 

In this episode of TAG Data Talk, we talk with Jerry DiMaso and Ryan Gertenbach about enterprise data literacy to scale analytics solutions.

In this episode of TAG Data Talk, we talk with Monica Kedzierski about Making Analytics a Business as Usual Function.

In this episode of TAG Data Talk, we talk with Arvind Prabhakar, Chief Technology Officer at StreamSets, about Data Ops for Dynamic Business Needs.

In this episode of TAG Data Talk, we talk with Jason Poovey, Director of Data Science and Analytics at Greenlight Financial Technology, about the Importance of Involving Analytics Stakeholders Early.

In this episode of TAG Data Talk, we talk with Jim Haller about advanced analytics for financial functions.

In this episode of TAG Data Talk, we talk with LaRae Bennett, Sr. Director, Data & Analytics at Premier Research, about the lost art of listening for improving analytics.

In this episode of TAG Data Talk, we talk with a panel of experts about leveraging workforce analytics for strategic advantage.

In this episode of TAG Data Talk, we talk with Doug Laney about the stimulating data science innovation and getting beyond the process.

In this episode of TAG Data Talk, we talk with Shelly Kunkle about the challenges and imperfections encountered during the model building process, and how to derive value despite the imperfections within the models.

In this episode of TAG Data Talk, we talk with Jason Schlachter about landing a career in the AI field.

• Describe the AI field
• What are some of the roles within the AI field
• Skills and strategies for gaining access to a career in AI

In this episode of TAG Data Talk, we talk with Winn Martin, Chief Data Officer at Steady about The Criticality of Understanding Business Context in Analytics.

In this episode of TAG Data Talk, we talk with T. Scott Clendaniel, Lead Data Scientist at Franklin Templeton Strategic Artificial Intelligence Labs, about Encouraging Model Adoption among the Executive Suite and Beyond.

In this episode of TAG Data Talk, we talk with Juan Gorricho, Executive Data Science Leader in Financial Services, about Data Management in a Global Marketplace.