The explosion of content in market research has created a paradox - more information but less time to consume it. Companies are now turning to AI chatbots to solve this problem, transforming how professionals interact with research data. Instead of expecting teams to read everything, these tools allow users to extract precisely what they need when they need it. This approach is proving not just more efficient but actually increases engagement with underlying content. How might your organization benefit from more targeted access to insights? What valuable information might be buried in your existing research that AI could help surface? With over 30 years of experience in marketing, media, and technology, Dan Coates is the President and co-founder of YPulse, the leading authority on Gen Z and Millennials. YPulse helps brands like Apple, Netflix, and Xbox understand and communicate with consumers aged 13–39, using data and insights from over 400,000 interviews conducted annually across seven countries. Prior to founding YPulse, Dan co-founded SurveyU, an online community and insights platform targeting youth, which merged with YPulse in 2009. He also led the introduction of Globalpark’s SAAS platform into the North American market, until its acquisition by QuestBack in 2011. In addition, Dan has held senior roles at Polimetrix, SPSS, PlanetFeedback, and Burke, where he developed cutting-edge practices and products for online marketing insights and transitioned several ventures from early stages to high-value acquisitions. In the episode, Richie and Dan explore the creation of an AI chatbot for market research, addressing customer engagement challenges, the integration of AI in content consumption, the impact of AI on business strategies, and the future of AI in market research, and much more. Links Mentioned in the Show: YPulseConnect with DanHaystack by DeepsetUnmanaged: Master the Magic of Creating Empowered and Happy Organizations by Jack SkeelsSkill Track: AI FundamentalsRelated Episode: Can You Use AI-Driven Pricing Ethically? with Jose Mendoza, Academic Director & Clinical Associate Professor at NYURewatch sessions from RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The integration of speech AI into everyday business operations is reshaping how we communicate and process information. With applications ranging from customer service to quality control, understanding the nuances of speech AI is crucial for professionals. How do you tackle the complexities of different languages and accents? What are the best practices for implementing speech AI in your organization? Explore the transformative power of speech AI and learn how to overcome the challenges it presents in your professional landscape. Alon Peleg serves as the Chief Operating Officer (COO) at aiOla, a position he assumed in May 2024. With over two decades of leadership experience at renowned companies like Wix, Cisco, and Intel, he is widely recognized in the tech industry for his expertise, dynamic leadership, and unwavering dedication. At aiOla, Alon plays a key role in driving innovation and strategic growth, contributing to the company’s mission of developing cutting-edge solutions in the tech space. His appointment is regarded as a pivotal step in aiOla’s expansion and continued success. Gill Hetz is the VP of AI at aiOla where he leverages his expertise in data integration and modeling. Gill was previously active in the oil and gas industry since 2009, holding roles in engineering, research, and data science. From 2018 to 2021, Gill held key positions at QRI, including Project Manager and SaaS Product Manager. In the episode, Richie, Alon, and Gill explore the intricacies of speech AI, its components like ASR, NLU, and TTS, real-world applications in industries such as retail and pharmaceuticals, challenges like accents and background noise, and the future of voice interfaces in technology, and much more. Links Mentioned in the Show: aiOlaConnect with Alon and GillCourse: Spoken Language Processing in PythonRelated Episode: Building Multi-Modal AI Applications with Russ d'Sa, CEO & Co-founder of LiveKitSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The rise of A-B testing has transformed decision-making in tech, yet its application isn't without challenges. As professionals, how do you navigate the balance between short-term gains and long-term sustainability? What strategies can you employ to ensure your testing methods enhance rather than hinder user experience? And how do you effectively communicate the insights gained from testing to drive meaningful change within your organization? Vanessa Larco is a former partner at NEA where she led Series A and Series B investment rounds and worked with major consumer companies like DTC jewelry giant Mejuri, menopause symptom relief treatment Evernow, and home-swapping platform Kindred as well as major enterprise SaaS companies like Assembled, Orby AI, Granica AI, EvidentID, Rocket.Chat, Forethought AI. She is also a board observer at Forethought, SafeBase, Orby AI, Granica, Modyfi, and HEAVY.AI. She was a board observer at Robinhood until its IPO in 2021. Before she became an investor, she built consumer and enterprise tech herself at Microsoft, Disney, Twilio, and Box as a product leader. In the episode, Richie and Vanessa explore the evolution of A-B testing in gaming, the balance between data-driven decisions and user experience, the challenges of scaling experimentation, the pitfalls of misaligned metrics, the importance of understanding user behavior, and much more. Links Mentioned in the Show: New Enterprise AssociatesConnect with VanessaCourse: Customer Analytics and A/B Testing in PythonRelated Episode: Make Your A/B Testing More Effective and EfficientSign up to attend RADAR: Skills Edition - Vanessa will be speaking! New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. What makes a database modern, and why does it matter? In a world where we face countless choices, how do you build systems that not only scale but also make life easier for your teams? And with AI reshaping industries and workflows, how do businesses bridge the gap between legacy systems and cutting-edge applications? Sahir Azam is the Chief Product Officer at MongoDB. He has been with MongoDB since 2016, where he launched the industry’s first developer data platform, MongoDB Atlas, and scaled the company’s thriving cloud business from the ground up. He also serves on the boards of Temporal and Observe, Inc, a cloud data observability startup. Sahir joined MongoDB from Sumo Logic, where he managed platform, pricing, packaging, and technology partnerships. Before Sumo Logic, he launched VMware's first organically developed SaaS management product and grew their management tools business to $1B+ in revenue. Earlier in his career, Sahir also held technical and sales-focused roles at DynamicOps, BMC Software, and BladeLogic. In the episode, Richie and Sahir Azam explore the evolution of databases beyond NoSQL, enhancing developer productivity, integrating AI capabilities, modernizing legacy systems, and much more. Links Mentioned in the Show: MongoDBConnect with SahirCourse: Introduction to MongoDB in PythonRelated Episode: Not Only Vector Databases: Putting Databases at the Heart of AI, with Andi Gutmans, VP and GM of Databases at GoogleRewatch sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI has rapidly emerged as an incredibly transformative technology, and nowhere has its impact been felt more unexpectedly than in the creative arts. Just a decade ago, few would have predicted that AI would evolve from automating routine tasks to generating paintings, music, and even poetry. Yet today, the role of AI in the arts has entered mainstream conversations, even contributing to the debates seen in last year’s Hollywood strikes.  Kent Kersey is a creative technologist who has served as a Product and Business leader in startups across B2B, B2C, and Enterprise SaaS. He is the founder and CEO of Invoke, an open-source Enterprise platform built to empower creatives to co-create with custom/fine-tuned AI products. In the episode, Adel and Kent explore intellectual property and AI, the legal landscape surrounding AI models, open vs closed-source models, the future of creative teams and GenAI, innovations in GenAI, the role of artists in an AI-world, GenAI’s impact on the future of entertainment and much more.  Links Mentioned in the Show: InvokeHow to Use Midjourney: A Comprehensive Guide to AI-Generated Artwork CreationCourse: Generative AI ConceptsRelated Episode: Seeing the Data Layer Through Spatial Computing with Cathy Hackl and Irena CroninRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

One of the big use cases of generative AI is having small applications to solve specific tasks. These are known as AI agents or AI assistants. Since they’re small and narrow in scope, you probably want to create and use lots of them, which means you need to be able to create them cheaply and easily. I’m curious as to how you go about doing this from an organizational point of view. Who needs to be involved? What’s the workflow and what technology do you need? Dmitry Shapiro is the CEO of MindStudio. He was previously the CTO at MySpace and a product manager at Google. Dmitry is also a serial entrepreneur, having founded the web-app development platform Koji, acquired by Linktree, and Veoh Networks, an early YouTube competitor. He has extensive experience in building and managing engineering, product, and AI teams. In the episode, Richie and Dmitry explore generative AI applications, AI in SaaS, approaches to AI implementation, selecting processes for automation, changes in sales and marketing roles, MindStudio, AI governance and privacy concerns, cost management, the limitations and future of AI assistants, and much more. Links Mentioned in the Show: MindStudioConnect with Dmitry[Webinar] Dmitry at RADAR: From Learning to Earning: Navigating the AI Job LandscapeRelated Episode: Designing AI Applications with Robb Wilson, Co-Founder & CEO at Onereach.aiRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

Effective data management has become a cornerstone of success in our digital era. It involves not just collecting and storing information but also organizing, securing, and leveraging data to drive progress and innovation. Many organizations turn to tools like Snowflake for advanced data warehousing capabilities. However, while Snowflake enhances data storage and access, it's not a complete solution for all data management challenges. To address this, tools like Capital One’s Slingshot can be used alongside Snowflake, helping to optimize costs and refine data management strategies. Salim Syed is a VP, Head of engineering for Capital One Slingshot product. He led Capital One’s data warehouse migration to AWS and is a specialist in deploying Snowflake to a large enterprise. Salim’s expertise lies in developing Big Data (Lake) and Data Warehouse strategy on the public cloud. He leads an organization of more than 100 data engineers, support engineers, DBAs and full stack developers in driving enterprise data lake, data warehouse, data management and visualization platform services. Salim has more than 25 years of experience in the data ecosystem. His career started in data engineering where he built data pipelines and then moved into maintenance and administration of large database servers using multi-tier replication architecture in various remote locations. He then worked at CodeRye as a database architect and at 3M Health Information Systems as an enterprise data architect. Salim has been at Capital One for the past six years. In this episode, Adel and Salim explore cloud data management and the evolution of Slingshot into a major multi-tenant SaaS platform, the shift from on-premise to cloud-based data governance, the role of centralized tooling, strategies for effective cloud data management, including data governance, cost optimization, and waste reduction as well as insights into navigating the complexities of data infrastructure, security, and scalability in the modern digital era. Links Mentioned in the Show: Capital One SlingshotSnowflakeCourse: Introduction to Data WarehousingCourse: Introduction to Snowflake

Building machine learning systems with high predictive accuracy is inherently hard, and embedding these systems into great product experiences is doubly so. To build truly great machine learning products that reach millions of users, organizations need to marry great data science expertise, with strong attention to user experience, design thinking, and a deep consideration for the impacts of your prediction on users and stakeholders. So how do you do that? Today’s guest is Sam Stone, Director of Product Management, Pricing & Data at Opendoor, a real-estate technology company that leverages machine learning to streamline the home buying and selling process. Sam played an integral part in developing AI/ML products related to home pricing including the Opendoor Valuation Model (OVM), market liquidity forecasting, portfolio optimization, and resale decision tooling. Prior to Opendoor, he was a co-founder and product manager at Ansaro, a SaaS startup using data science and machine learning to help companies improve hiring decisions. Sam holds degrees in Math and International Relations from Stanford and an MBA from Harvard. Throughout the episode, we spoke about his principles for great ML product design, how to think about data collection for these types of products, how to package outputs from a model within a slick user interface, what interpretability means in the eyes of customers, how to be proactive about monitoring failure points, and much more.