Many asset-intensive organisations are DRIP: Data Rich, Information Poor. Across the asset lifecycle — design, commissioning, operation, condition monitoring, maintenance, and renewal — data is abundant and increasingly inexpensive to collect. As a result, “more data” has quietly become the default objective. Yet cost volatility, reactive work, unstable planning, and investment misalignment persist. Data accumulation is not value creation.

The core issue is architectural. Asset data is rarely structured around the economic outcomes it is meant to support. Instead, organisations move forward from data and hope insight emerges.

This session introduces a reverse, value-driven logic for fixing the DRIP. Rather than starting with data, we begin by defining the value desired: cost stability, uptime reliability, capital efficiency, and controlled risk exposure. From there, we work backward through the chain: the actions required to deliver that value; the decisions required to enable those actions; the insights required to support those decisions; the information required to generate those insights; and finally, the data required to produce that information.

This disciplined inversion — the foundation of the Data to Dollars framework — shifts the objective from data expansion to economic alignment. It clarifies which data matters, which KPIs predict value, and which analytics roles must exist to convert asset performance into financial predictability.

• How to diagnose Data Rich, Information Poor conditions within their asset systems
• How to apply reverse value logic to define economically relevant asset data
• How to connect asset performance metrics to Total Cost of Ownership and risk
• How to rationalise KPI overload into decision-enabling insight
• How to design governance and analytical structures that convert data into predictable enterprise outcomes