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6. Data-driven product development with Ryan McCabe, Senior Data Scientist at Spotify, Stockholm

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Manage episode 290329933 series 2895967
Content provided by Alexandra Ebert (MOSTLY AI). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alexandra Ebert (MOSTLY AI) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

Ryan is a Senior Data Scientist at Spotify with extensive experience in developing great
customer-centric products with the power of data. He is a data consultant and university
lecturer, passionate about data science and understanding the business side of things.
In this episode you will hear about:

  • How to start building data infrastructure in your organization, no matter the size.
  • How to embed data best practices in your team and how to get the most value out of data science.
  • Actionable advice on how to scale machine learning in your organization through making data accessible and automating data access processes.
  • Data protection is a true differentiator in today’s market. You don’t need to use
    personal data for data science and fake users are better than real ones for testing your
    ideas.
  continue reading

52 episodes

Artwork
iconShare
 
Manage episode 290329933 series 2895967
Content provided by Alexandra Ebert (MOSTLY AI). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alexandra Ebert (MOSTLY AI) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

Ryan is a Senior Data Scientist at Spotify with extensive experience in developing great
customer-centric products with the power of data. He is a data consultant and university
lecturer, passionate about data science and understanding the business side of things.
In this episode you will hear about:

  • How to start building data infrastructure in your organization, no matter the size.
  • How to embed data best practices in your team and how to get the most value out of data science.
  • Actionable advice on how to scale machine learning in your organization through making data accessible and automating data access processes.
  • Data protection is a true differentiator in today’s market. You don’t need to use
    personal data for data science and fake users are better than real ones for testing your
    ideas.
  continue reading

52 episodes

All episodes

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Welcome back to season 5 of the Data Democratization Podcast - and our first-ever live studio recording. In this episode, Alexandra Ebert sits down with Faris Haddad, AWS' Global AI Technical Strategy Lead. Together, they delve into the transformative role of synthetic data in modern enterprises. Faris shares his journey into the world of synthetic data, highlighting its evolution from a niche solution to a cornerstone of enterprise data strategy. He discusses the challenges organizations face with data silos, legacy systems, and privacy concerns, and how synthetic data offers a pathway to overcome these hurdles. Whether you're grappling with data accessibility issues or exploring innovative ways to leverage your organization's data assets, this episode offers valuable perspectives on integrating synthetic data into your enterprise data strategy.…
 
AI democratization sounds great in theory, but why do so many enterprises struggle to make it work? In this episode, Ari Kaplan, Head of Tech Evangelism at Databricks , breaks down the biggest roadblocks to scaling AI—and what’s needed to overcome them. From data intelligence and synthetic data to why leading enterprises rely on unified data and analytics Platforms to cut costs, reduce governance complexity, and streamline AI adoption—we explore how organizations can move beyond AI pilot purgatory and use AI to drive tangible impact at scale. Ari dives into the hard truths about AI implementation, including why companies still face governance hurdles, siloed data, and inefficient infrastructure. Ari and I discuss how organizations can get more value from their data, deploy AI responsibly, and use synthetic data to scale securely. Plus, Ari shares his personal approach to balancing a global career, thought leadership, and content creation—including how he manages to make time for writing books while advising enterprises worldwide. We also discuss his take on social media strategy, the skills he’s encouraging his kids to develop for an AI-driven world, and the future of work in an era of intelligent automation. If you care about AI democratization, enterprise AI strategy, and staying ahead of the curve, this is an episode you won’t want to miss!…
 
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!…
 
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 Guest 2:39 - 4:42 Daragh's Role and Microsoft's AI Approach in Financial Services 4:42 - 6:37 Patterns in Successful AI Adoption Globally 6:37 - 8:20 Gen AI Adoption: Early Success Stories and Lessons Learned 8:20 - 10:59 Overcoming Challenges: Moving Beyond Perfectionism in AI Projects 10:59 - 12:14 The Shift in Financial Services AI Use Cases 12:14 - 14:36 Regional Differences in AI Approaches in Financial Services 14:36 - 17:55 AI Use Cases in Financial Services: From Advisors to Autonomous Agents 17:55 - 20:00 Ethical Challenges and Responsible AI in Financial Services 20:00 - 23:25 The Future of Financial Services AI: Customer Relationship Innovations 23:25 - 27:32 Expanding Financial Inclusion with AI and Responsible AI Practices 27:32 - 29:41 Closing Thoughts on AI’s Impact on Financial Services…
 
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.…
 
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.…
 
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!…
 
In this episode of the Data Democratization Podcast, host Alexandra Ebert, Chief Trust Officer at MOSTLY AI , sits down with Caroline Louveaux, Chief Privacy & Data Responsibility Officer at Mastercard, to explore the evolving landscape of data privacy and AI governance. Caroline shares her insights on topics ranging from privacy-enhancing technologies (PETs) to data for social impact. Here is what you'll learn: How to be successful in today's data and AI ecosystem, What is the role of the Chief Data Responsibility Officer, How to set up your organization for compliance with the 'alphabet soup of EU regulations', How to make compliance loveable, What's needed on the regulatory side, How to enable AI innovation, What is an AI governance framework, and how to make it work? How can privacy pros prepare for AI? How can privacy-enhancing technologies facilitate AI innovation? Why is automation so important? Can data and AI positively impact society? Dive into this conversation to better understand the importance of data governance and responsible data usage in the digital age.…
 
What is data and AI literacy, and why is it central to DataCamp's mission? In this episode, our host, Alexandra Ebert, MOSTLY AI's Chief Trust Officer, had the chance to talk to truly like-minded people. DataCamp's CEO and co-founder, Jo Cornelissen, and Maggie Remynse, VP of Curriculum, have both seen firsthand how transformative knowledge and access to data is. If you are interested in Data and AI literacy, make sure you check out the vast resources DataCamp is going to share throughout September 2023 during their annual Data and AI Literacy Month. There are top-notch experts sharing their knowledge during webinars, podcast episodes, and even a virtual conference on September 28th. And the best of all, it’s completely free of charge. Sign up here: https://bit.ly/3sMu8pJ…
 
Recorded live at the Money2020 Europe conference, Alexandra Ebert, MOSTLY AI 's Chief Trust Officer talks to Sulabh Agarwal, Accenture's Global Head of Payments. Sulabh shares his insights on the changing landscape of the payments industry and the factors driving its transformation. He highlights the influence of technology and the role of payments as a catalyst for innovation, improved customer experiences, and personalization in the payment process. The episode concludes with a discussion on the future of payments and the actions payments executives should take to future-proof their strategies.…
 
In this episode, we are joined by our esteemed guest, Dr. Meshari Alwashmi, a prominent Digital Health Scientist whose expertise spans not only extensive research but also a successful track record as a serial entrepreneur and trusted advisor to digital health initiatives. Prepare to be enlightened as we uncover the latest trends and advancements propelling the digital health industry forward. Discover the remarkable potential for progress and the direction in which this dynamic industry is heading. Moreover, we will delve into the invaluable contributions that synthetic data in healthcare can make to this ongoing revolution. This episode offers much more than just a glimpse into the future of digital health. Tune in for actionable advice that will guide your organization toward embracing innovation and achieving success in the realm of digital health.…
 
In this episode of the Data Democratization Podcast, host Alexandra Ebert interviews Daniela Pak-Graf, the managing director of Merkur Innovation Lab — the innovation arm of Merkur Insurance. Daniela shares her tips and best practices for innovating with data in one of the most conservative and sensitive industries, health insurance. Tune in to find out how to accelerate innovation through effective data management, forward thinking organizational decisions and enabling technologies, like AI-powered synthetic data generation .…
 
The 40th episode is a special one. We invited MOSTLY AI's Chief Product Officer, Mario Scriminaci, to quiz him about synthetic data technology and how the filed is progressing beyond the data privacy use case. Mario will share how MOSTLY AI is developing its synthetic data platform to provide users with easy and fast data augmentation tools. The frontiers of generative synthetic data is exciting - tune in to learn what's already a reality and what the future holds.…
 
In episode 39 of the Data Democratization Podcast, host Alexandra Ebert, Chief Trust Officer at MOSTLY AI , is joined by Rania Wasir, co-founder and CTO of leiwand.ai , to discuss AI transparency, the misconceptions surrounding AI transparency and fairness, and why having a standard for transparency is important. The episode also explores the concepts of fairness and explainability in AI, and how they differ from transparency. The challenges of detecting biases in large language models such as ChatGPT are also explored.…
 
Karin Schöfegger is a seasoned ML product manager who knows how to create successful AI/ML products and mitigate the risks involved. In this episode, she shares her insights about challenges in building AI products. Tune in to learn about: How to align the business side and the data science side of product development What's the difference between traditional software development and machine learning development How to bring customer understanding into data science and engineering What are the most common traps in data science Why it's essential to work with realistic data instead of picture-perfect datasets How to get buy-in from stakeholders for ethical AI…
 
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