Data-scarce

Data-scarce describes a situation, field, or dataset where there is a limited or insufficient amount of available data for analysis, research, or decision-making. This scarcity can stem from various factors including the cost of data collection, the difficulty in obtaining the data, the sensitive nature of the data, the recent emergence of a phenomenon, or limitations in data storage or processing capabilities. Consequently, data-scarce environments often require the application of specialized analytical techniques, the use of data augmentation strategies, or reliance on expert knowledge and domain expertise to compensate for the lack of comprehensive information.

Data-scarce meaning with examples

• The early research on rare diseases is often data-scarce, because only a few people are impacted by the condition. This makes it difficult to run comprehensive clinical trials or identify robust statistical patterns that suggest a promising treatment. Researchers, therefore, have to employ creative strategies, such as combining data across different countries or using computer modeling.
• The field of climate modeling can be data-scarce when trying to evaluate the effects of localized emissions on pollution, or the specific effects of certain weather patterns on certain events. Without dense, long-term monitoring stations in the location, modeling the weather's effects or the emissions' dispersion becomes exceedingly challenging, as the models require an extraordinary degree of data for calibration.
• Startups in emerging markets frequently operate in a data-scarce environment. They may struggle to understand consumer behavior due to limited market research, restricted access to sales data, and low rates of customer feedback. Therefore, they might rely on pilot programs, qualitative studies, and partner collaborations to overcome this, making decisions based on less definitive information.
• When attempting to predict the behavior of sophisticated adversaries in cybersecurity, the information is extremely data-scarce. Access to live attack data is almost always restricted to the security vendor who has been attacked or discovered the attack, making comprehensive predictive modeling exceedingly complex. This leads to reliance on theoretical attack vectors, simulations, and expert opinion, along with a high degree of uncertainty.