How do you keep your data team focused and build something that people will derive value from? In this episode, we cover team engagement, how to evaluate tech, and the influence of consumer analytics on innovation. Listen to our conversation with Raheem Daya, Sr. Software Development Manager, Envision Engineering at Amazon Web Services: How to set a clear vision and keep your data team engaged How to decide what tech to invest in and build How consumer tech is democratizing data

What are the top three mega trends for data leaders this year (and beyond)? In this episode, we tackle cloud data platforms, the five sub disciplines of observability, and real-time machine learning. Listen to our conversation with Kevin Petrie, Vice President of Research at Eckerson Group. Hear the Answers to these Questions: Why a cloud data platform is a common destination with many routes? Which tools to standardize the different classes of observability? How the interrelationship between model observability and ML works?

Jimmy McDermott, Founder and CTO of Transeo, talks about use cases and expected value they will get from SQL analytics and search on the same, unified data set with the ChaosSearch Data Lake Platform.

When it comes to building new products, there’s a fine line between which pieces of the puzzle should be owned by humans with deep domain knowledge, and which aspects can or should be automated through AI. How far can the boundary be pushed? We speak with Jeremy Foran, Chief Technology Officer at Purple Cow Internet, about his new role as CTO at a fast-growing internet service provider.

Historically, knowledge has been relatively siloed by language. But with advancements in AI, there are now more opportunities than ever to capture broader and deeper insights across the written and spoken word by breaking down language and distance barriers globally. Listen to our conversation with Amr Awadallah, Founder and CEO at ZIR AI and former technology exec at Cloudera, Google, and Yahoo.

When providing the data to support marketing, it's important to frame and validate its quality based on whether it meets "the six C’s." Is the data clean, complete, correct, comprehensive, chosen well, and calculable? In our conversation with Christopher Penn, Co-Founder and Chief Data Scientist at TrustInsights.ai, we discuss numerous questions many in the industry are asking today.