When: February 26, 2026 | 9:00 AM PT | 12:00 PM ET Duration: 45 minutes Format: Live, virtual session. Participate hands-on or just watch. Maximizing returns from existing assets is one of the fastest ways to improve ROI without taking on additional operational risk. In this live, hands-on session, we’ll use a real-world oil and gas example to demonstrate how teams can build a data product that supports smarter investment and capital planning decisions. You’ll see how trusted production and financial data power a practical, business-facing data product designed to help teams answer one critical question:
Which assets should we invest in next to maximize return?
What You’ll Build:
In 45 minutes, we’ll walk step by step through how to build a capital-focused data product that helps teams:
Identify the highest-return assets using trusted production and financial data
Avoid value-destroying investments
Optimize asset performance without increasing operating costs
Direct capital and resources toward the most profitable opportunities
No slides. Just a live, practical build. You can participate hands-on, ask questions, or simply observe.
What You’ll Need:
A computer with internet access (Chrome or Edge recommended)
You’ll receive a link during the session to activate a lab account and participate hands-on. No setup required.
Who Should Join:
Designed for: Analytics, Data, AI Product Managers, Finance, and Operational leaders in energy and manufacturing who support investment and capital planning decisions.
Space is limited to keep the session interactive. Reserve your spot!
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Whitepaper
Organizations have spent years cataloging data—yet many still struggle to turn it into business value. The answer? Data products.

Whitepaper
Struggling to extract real value from your data investments? You’re not alone. Many organizations are drowning in data—yet starving for insights.

Blog
AI readiness isn’t about building better models—it’s about delivering data in a form AI can actually trust and reuse. Data products provide that foundation by packaging data with clear ownership, governance, lineage, and quality signals, turning raw datasets into reliable, consumable building blocks for enterprise AI. Organizations that operationalize data products don’t just experiment with AI; they scale it confidently across teams, use cases, and decisions.