RESEARCH & RESOURCES

Upcoming Webinars

International Broadcasts

TDWI On-Demand Webinars on Data Management, Analytics, & AI

TDWI Webinars deliver unbiased information on pertinent issues in the big data, business intelligence, data warehousing, and analytics industry. Each live Webinar is roughly one hour in length and includes an interactive question-and-answer session following the presentation.


On Demand

Growing Analytics Initiatives: Findings from TDWI Leadership Roundtables

Earlier this summer, TDWI conducted a series of roundtables with data and analytics leaders to better understand current challenges and how organizations are dealing with them.


Analytics for Everyone: 7 Tips for Analytics Adoption

This webinar will review the key details that will help you strengthen user adoption of advanced analytics in your company's environment.

Evan Levy


Increasing the Value of Business Analytics: Balance Self-Service and Governance and Address Diverse Data Needs

Join this webinar to hear about the latest TDWI Best Practices Report research about business analytics.

David Stodder


Five Steps for Accelerating Data Readiness to Improve Analytics and Governance

Join this TDWI Webinar to learn how you can update your technology strategy with modern solutions and methods to accelerate data readiness for better analytics and governance.

David Stodder


Why Automated Data Lineage Is a Must-Have for BI and Analytics

Learn about uses cases for data lineage, especially those in BI daily operations and analytics and how data lineage assists data exploration and discovery, solution development, auditing, data governance, and migrations.

Philip Russom, Ph.D.


What Do Modern Analysts Want?

Join this TDWI webinar to learn more about this emerging role and its importance to your organization. Utilizing results from a recent TDWI survey, Fern Halper, TDWI's VP of Research, and experts from ThoughtSpot will discuss this valuable role.


Six Critical Factors for Machine Learning Success

Machine learning requires the capacity to collect, manage, and access large amounts of accurate and diverse data, the ability to create new features and train models, and must be able to deploy, monitor, and update models in production. Learn about six factors to make machine learning a success.


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