Article by Kate Hodkinson: “Novel data sources can provide important proxies in data-limited contexts. When combined with traditional humanitarian data, such as needs assessments or displacement tracking, these sources can increase the resilience of the humanitarian data ecosystem. For example, in response to the March 2025 Myanmar earthquake, Microsoft AI for Good Lab provided data on building damage before access to affected areas was possible. In other cases, Meta’s high-resolution population density maps have been used to estimate the number of people living within a 30-metre grid and their demographics, helping organisations identify people in need.
Applications of this novel data sources have been explored through many pilots over the last decade. However, progress has not been linear. Seemingly promising technologies have fallen into obscurity, while others have carved out clear use cases. How should humanitarians use novel data sources to reinforce informational resilience, rather than create new dependencies?
Exploring this question required a structured rubric through which we could assess the integration of novel data sources to date, understand their technosocial context, and consider the factors that may define their future use in the humanitarian sector. We used two tools to do this: the S-Curve and the Technology Axis Model.
The S-Curve
The humanitarian sector’s adoption of novel data sources can be mapped against an S-Curve (adapted from Fisher, 1971), which measures maturity from nascent potential to normative practice. For example, the use of satellite data to create dynamic, high-resolution population estimates has moved from a ‘nascent’ opportunity towards a common practice, proving particularly valuable in contexts with limited or outdated census data. The S-Curve creates a way to plot examples of where novel data sources have been integrated into crisis response analysis or appear trapped in a perpetual pilot phase.
The Technology Axis Model (TAM) positions technology innovation cycles as happening in four inter-linked areas:
- The development of the underlying technology.
- The social norms and values associated with the innovation and its effects.
- The applications that are emerging as entrepreneurs seek to introduce the technology into markets.
- The infrastructure and systems that govern the technology area.
The Technology Axis Model was originally developed by Bill Sharpe as a way to help engineers think more widely about the contexts that surround technology innovation. Our definitions are drawn from a forthcoming paper on the Technology Axis Model, by Bill Sharpe and Andrew Curry…(More)”.
