Data Democratization: Stories about data, AI and privacy

• 50. How to Democratize Data and Bridge AI's Tech/Business-Gap with Erste Group's Anna Lishchenko

  For the 50th episode of the Data Democratization Podcast, I sat down with Anna Lishchenko, Data & Analytics for Business Platform Lead at Erste Group. As a true champion of data democratization, Anna shares invaluable insights into how to make data widely accessible, actionable, and impactful across a large, decentralized organization.💡 What you'll learn:The essential elements of building a data-driven culture, including data literacy, governance, and community building.Laying the foundation for success: How to align data and AI initiatives with business goals and secure buy-in from key stakeholders.Breaking barriers to experimentation: Using synthetic data to democratize access and accelerate innovation.Bridging silos: Connecting business and technical teams to drive collaboration and create real impact.Scaling AI and data initiatives: Turning one-off projects into sustainable enterprise-wide strategies.Whether you're just starting on your AI journey or scaling data initiatives, this episode is packed with actionable insights and practical advice. Don’t miss this engaging conversation about the status-quo and the future of data democratization!

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• 49. "Forget copying others — every organization needs to build their own AI muscle" with Microsoft's Daragh Morrissey

  In episode 2 of the new season, Alexandra Ebert chats with Daragh Morrissey, Microsoft’s Director of AI for Worldwide Financial Services, recorded live at Money2020 US. Daragh offers a global perspective on AI adoption in financial services, highlighting unique strategies and challenges faced by institutions worldwide. He explains why Canada and Australia stand out in AI progress and why succeeding with Gen AI requires financial institutions to shift from over-strategizing to focusing on tangible outcomes.Daragh also shares insights on impactful AI applications in financial services, from automating contact centers to upselling, enhancing customer relationships, and modernizing legacy code. Lastly, Alexandra and Daragh take a look at the future of work and the role autonomous agents might play. They also debate whether AI will free up advisors time and whether, as a result, banks will have many more human advisors, or if the future of personalized banking will be more automated, yet transformative in enhancing individual financial health and fostering greater inclusivity. Daragh’s compelling anecdotes and strategic insights make this episode a must-listen for anyone interested in AI’s future in financial services.Table of Contents:0:00 - 2:39 Introduction to the Episode and Guest2:39 - 4:42 Daragh's Role and Microsoft's AI Approach in Financial Services4:42 - 6:37 Patterns in Successful AI Adoption Globally6:37 - 8:20 Gen AI Adoption: Early Success Stories and Lessons Learned8:20 - 10:59 Overcoming Challenges: Moving Beyond Perfectionism in AI Projects10:59 - 12:14 The Shift in Financial Services AI Use Cases12:14 - 14:36 Regional Differences in AI Approaches in Financial Services14:36 - 17:55 AI Use Cases in Financial Services: From Advisors to Autonomous Agents17:55 - 20:00 Ethical Challenges and Responsible AI in Financial Services20:00 - 23:25 The Future of Financial Services AI: Customer Relationship Innovations23:25 - 27:32 Expanding Financial Inclusion with AI and Responsible AI Practices27:32 - 29:41 Closing Thoughts on AI’s Impact on Financial Services

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• 48. Driving Impact with (Gen) AI for Financial Services with NVIDIA's Malcolm deMayo

  Welcome back to Season 4 of the Data Democratization Podcast! In the 1st episode of the new season, Alexandra Ebert sat down with Malcolm DeMayo, NVIDIA's VP of Global Financial Services, live at Money2020 US to dive into what it takes for financial services organizations to succeed with data and AI at scale. Malcolm shares insights on using data as a true differentiator, why Responsible AI practices are more important than ever, and how modern privacy-enhancing technologies - like federated learning and synthetic data - help tackle common data challenges. He also sheds light on how Gen AI is impacting talent and the surprising ways AI assistants might help organizations counteract the negative effects of employee churn. And, of course, Alexandra asks Malcolm for his vision for the future of AI in financial services. Check out other episodes of the Data Democratization Podcast for more stories about data, privacy, responsible AI and how to do data democratization well.

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• 47. The secrets of unlocking business value with your data strategy

  In the 47th episode of the Data Democratization Podcast, host Alexandra Ebert talks to Maritza Curry, Head of Data at RCS South Africa, to explore practical insights into developing effective data strategies. The discussion delves into the importance of solid data management and data governance. It also covers the topic of whether an AI strategy is necessary and the critical need for integrating business literacy into AI and data initiatives. Maritza offers valuable tips from her extensive experience, emphasizing the importance of good communication. She highlights the necessity of talking not only to executives but also to those on the frontlines in order to develop effective data strategies and tailor them to best fit your organizational culture.Check out the other episodes of the Data Democratization Podcast for more stories about data, privacy, responsible AI and how to do data democratization well. 

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• 46. How to (finally) move your AI products to production

  In the 46th episode of the Data Democratization Podcast, host Alexandra Ebert is talking to Wolfgang Weidinger, AI, Data Science, and Analytics Coordinator at Generali Insurance, Austria, and Chairman Of The Board at the Vienna Data Science Group. Wolfgang is a seasoned data scientist with tons of experience managing AI, data science, and analytics projects. The episode covers a wide range of topics, offering actionable tips for those looking to deploy AI models in large organizations:selecting the right tools for AI projects,the adoption of AI in various industries,the significance of soft skills, collaboration, and domain knowledge,the organizational role of the data scientist,how to bridge the gap between business and technology.If you would like to learn more about data science and AI in practice, we recommend The Handbook of Data Science and AI - Generate Value from Data with Machine Learning and Data Analytics, co-authored by Wolfgang. If you are in Vienna, Austria, follow the Vienna Data Science Group for great meetups and opportunities to connect with the local data science community!

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