You have to have a lot of data to get AI to work. But the data folks are not jumping on it as fast as they should. So what happens when data teams aren’t up to speed, companies are hiring more data scientists than they are engineers, AND current data teams are focusing too much on biz reporting and not supporting AI?This week on Catalog & Cocktails, join hosts Tim Gasper and Juan Sequeda as they chat with special guest, Theresa Kushner, Head of North America Innovation Center at NTT Data Services to discuss how the AI train is leaving the station and data teams can only run so fast. Key Takeaways[00:10 - 02:25] Introduction & Cheers[02:28 - 04:12] What's your favorite way to travel and why?[04:15 - 07:01] Are data teams keeping up with AI teams?[07:01 - 08:50] Are data teams and AI teams helping each other or avoiding each other?[08:54 - 13:09] AI teams become a data set in themselves[13:10 - 14:45] Data ownership and control[14:51 - 17:20] Thoughts on purchasing data[17:20 - 20:53] Data products and observations[20:53 - 24:52] CDO versus the CDAO, definitions and comparisons[24:53 - 27:05] Should there be a CDO or a CDAO?[27:03 - 30:04] Data makes AI work[30:02 - 34:58] If you want results you have to collaborate[34:59 - 37:29] Creating culture tied to data quality[37:27 - 42:01] The skill sets for managing data products[42:02 - 46:09] Theresa's message of advice to data teams[46:12 - 52:36] Lightning Round[52:36 - 58:16] Takeaways[58:17 - 01:00:31] Three questions