655: AI ROI: How to get a profitable return on an AI-project investment

Transparent data science, profitable AI, and what’s missing from a data science education: Pandata’s Data Scientist in Residence Keith McCormick and Jon Krohn discuss how “insights” can never be the end product of a data science project, how to ensure you have a specific goal at the start of a project that is related to revenue, and why there is so much miscommunication between data scientists and their clients. Exclude the C-suite at your peril! This episode is brought to you by Glean (https://glean.io), the platform for data insights, fast. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • What an Executive Data Scientist in Residence is [05:27] • What A.I. transparency is and how it relates to the field of Explainable A.I. (XAI) [17:34] • How companies can ensure they profit from AI projects [36:47] • Possible organization structures for data science teams to be profitable [1:02:41] • The current gaps in data science education [1:09:58] Additional materials: www.superdatascience.com/655

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.