In such an environment, managers should use analytics not as a telescope that, if only it were accurate enough, would let them see the future perfectly. Instead, they should use analytics as a flashlight that, as they scan their surroundings, can help them find their way through a murky, fast-changing environment. Agile analytics uses advanced AI to continually assess the currency, relevancy and accuracy of data and analytic models, updating both to reflect changing real-world conditions and how useful given data or models were in the past. In agile analytics, advanced AI also continually assesses for model perishability -- developing and testing new models, improving them over time, and giving decision makers an estimated confidence level for each prediction. Achieving agile analytics begins with a deep understanding of the lifecycle of data -- which datasets should be used to train models and make business-critical decisions in uncertain times.