Bridging Data Science and Product Thinking: Creating High-Impact Data Products

Music City Center 

Statisticians and data scientists develop powerful models and analyses, yet there's always a struggle to operationalize them into scalable, impactful solutions. A data product mindset bridges this gap-combining statistical rigor, data science, and product thinking to create solutions that are usable, maintainable, and designed for long-term adoption.
In this speed session, we'll break down the key principles of building high-impact data products, including identifying the right use cases, designing for great experiences, and ensuring repeatability. We'll explore how organizations move beyond one-off analyses to create production-ready data assets, such as automated forecasting models, intelligent recommendation systems, and self-service analytics tools that drive measurable business value and create delight for users. Attendees will get practical takeaways on how to apply product thinking to data science, helping data science teams deliver valuable insights across an organization.
Through a few real-world examples, attendees will also learn how to structure data products for maximum impact and drive their adoption.

Data Products

Data Science and Analytics

Scalable Machine Learning Models

Self-service analytics

Product thinking

Adoption and impact of Data Science solutions 

Section on Statistical Computing