Our guest today holds a PhD in organizational psychology and has been working on data products in the health and wellness space for over a decade. We cover a lot of ground in this interview: how to create data products that work, how to avoid the unexpected consequences of poorly designed data interventions, and the importance of ethnographic thinking in data science.We'll also talk about reducing friction in data collection, the coaching data product model, and surprising things we can learn when people's routine's are broken. From today's episode, you'll come away with a better understanding of how to build contextually relevant data products that make a difference in people's lives.

Building Data Products that Work in the Health and Wellness Industry

If you want to learn how data science, artificial intelligence, machine learning, and deep learning are being used to change our world for the better, you’ve subscribed to the right podcast. We talk to entrepreneurs and experts about their experiences employing new technology—their approach, their successes, their failures, and the outcomes of their work. We make these difficult concepts accessible to a wide audience.

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Welcome to another exciting episode of Data Crunch! In this episode, we dive deep into the world of data analytics and manufacturing with our special guests, Alex Reid and Jay Minutrie. Both Alex and Jay bring unique entrepreneurial and data-focused experiences to the table, making them the perfect duo for an engaging conversation about the future of manufacturing.

We start with the origin story of their innovative technology, which began as an idea in Alex's father's laboratory at Tulane University. Alex, a young entrepreneur, teamed up with Jay, a seasoned industry veteran, to bring this patented technology to the market. Their complementary backgrounds served as the foundation for their successful partnership, leading to significant advancements in the manufacturing industry.

As we continue the conversation, Alex and Jay discuss the challenges and opportunities they faced while introducing their hard tech into the industrial space, and how they managed to successfully navigate these obstacles with the help of a strategic acquisition by Yokogawa. The pair also share insights on how they plan to leverage their new partnership to drive even more innovation and growth in their industry.

In this episode, we also explore the concept of on-demand designer materials at commodity scale. Alex shares his vision of a future where bespoke materials can be produced for specific customers at an affordable cost, leading to greater efficiency, reduced waste, and the ability to create customized products tailored to individual needs. This groundbreaking concept has the potential to revolutionize the manufacturing industry, and we dive into what it could mean for industry players and end-users alike.

Alongside this fascinating discussion, we also touch on the importance of mentorship for young entrepreneurs and the value of having experienced individuals to guide and support you through the challenges of starting a business. Alex and Jay's unique partnership serves as an inspiring example of how collaboration and shared knowledge can lead to incredible success.

Furthermore, our guests delve into the impact of their technology on the biopharma space, highlighting the potential for advancements in life-saving drugs and personalized medicine. With opportunities for growth in both the polymer and biopharma sectors, Alex and Jay's enthusiasm for the future is infectious, and their determination to make a difference is truly inspiring.

So, join us as we explore the world of data science in manufacturing, the potential for on-demand designer materials, and the inspiring journey of two entrepreneurs who are pushing the boundaries of innovation. Don't miss this enlightening episode of Data Crunch, filled with valuable insights, engaging conversations, and a glimpse into the future of manufacturing.

Data Science in Manufacturing: On-Demand Designer Materials at Commodity Scale

Working with structured and semistructured data can be hard, but it's currently much easier than working with unstructured data, like images, video, audio, and text. We talk with Davit Buniatyan, CEO of Activeloop, who chats with us about how he works to make unstructured data for machine learning easier and faster to work with. 

Creating a Database for AI with Activeloop

What's it like to build an AI product in an industry that still uses outdated project management technology and has no clear conceptual model? After receiving his PhD at Stanford, René Morkos speaks to this very situation. He describes his journey building an AI product in the construction industry.

Streamlining Construction with AI

After graduating from the University of Pennsylvania with a master’s degree in artificial intelligence/robotics, Edward Cui  was one of the first Uber self-driving car engineers. He’s had a lot of experience working with unstructured data and shares how we can increase the efficiency of modeling unstructured data by an order of magnitude.

The Future of Unstructured Data with Graviti

What is the secret culprit behind overworked teachers and administrators in much of the educational system? We're joined by one of Data Crunch's finest, James Thomas, who tells from both a technological and personal standpoint the real difficulties faced by our teachers and students, and how the right approach to data can solve many of their problems.

Building Data Products that Work in the Health and Wellness Industry

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