The Data Day Texas 2025 Sessions

Escape the Data & AI Death Cycle, Enter the Data & AI Product Mindset

Anne-Claire Baschet - Mirakl
Yoann Benoit - Hymaïa

On the horizon, there's a transformation underway where every digital product will encompass Data & AI capabilities.

However, we must recognize that Data and Product teams have distinct cultures and origins. Data teams possess an array of tools and technical expertise, yet they often struggle with quantifying the value they deliver. They frequently miss the mark in addressing the right problems that align with customer needs or in collaborating with Business-Product-Engineering teams.

This is where adopting a Product Mindset becomes paramount. Closing the divide between the Data and Product communities is imperative, as both groups must collaborate on a daily basis to create value for users and support businesses in reaching their goals.
In this talk, you will get insights into : identifying and overcoming the most common traps that Data Teams fall into when delivering Data & AI initiatives
• Crafting impactful Data & AI Products that solve the right problems
• Scaling a Data & AI Product Culture throughout the whole organization and define a Data & AI Product Strategy.

Optimisation Platforms for Energy Trading

Adam Sroka - Hypercube

As the energy sector transitions to new technologies and hardware, the data requirements are undergoing significant changes. At the same time, the markets in which energy systems operate are also evolving - giving traders and energy teams a vastly more complex set of options against which they need to make decisions.
The move to real-time data for BESS system operation and the addition of multiple markets makes optimisation of revenue for storage assets intractable for human operation alone.
In this talk, Adam Sroka will walk through one solution deployed at a leading BESS trading company in the UK that aligned probabilistic forecasting and stochastic methodologies with a linear optimisation engine to determine the best markets, prices, and trades for any given portfolio of mixed energy and storage assets.
Adam will walk through an architecture diagram for a system that integrates real-time, near real-time, and slow-moving data with AI-driven forecasts and the complexities of optimisation management.

The Future of Data Education and Publishing in the Era of AI

Jess Haberman - Anaconda
Michelle Yi - Women in Data
Hala Nelson - James Madison University

In an era where generative AI and low/no-code tools are democratizing access to expert knowledge, the landscapes of technical education and publishing alike are undergoing a seismic shift. Are these advancements propelling us toward educational utopia or posing unprecedented threats to industry and academia? Join us as we unravel the future of data and tech education with a panel of distinguished experts.
The surge of generative AI content sparks debates: Does it herald a new era of learning or threaten academic integrity? Will AI augment or overshadow human-generated educational materials? What implications does the proliferation of AI-generated content hold for authors and the discoverability of their work? This panel delves into the ramifications of AI tools on teaching and writing and on student learning, exploring the opportunities they present for knowledge dissemination and the concerns they raise in academic and industry circles.
Our esteemed panelists bring diverse perspectives:
Hala Nelson: Associate Professor of Mathematics at James Madison University and author of Essential Math for AI.
Michelle Yi: Senior Director, Applied Artificial Intelligence at RelationalAI and advocate for STEM education among underrepresented minorities.
Jess Haberman (panelist and moderator): Head of Education & Developer Relations at Anaconda, leveraging 14 years of publishing experience, including as an acquisitions editor at O’Reilly Media.
Join us for a dynamic discussion that illuminates the future of data education and its transformative impact on the realms of technology, academia, and publishing.