Often, data used in on-line decision making (for example, in determining how to react to changes in process behavior, traffic flow control, etc.) is dynamic in nature and hence the timeliness of the data delivered to the decision making process becomes very important. The delivered data must conform to certain time or value based application specific consistency requirements. The design of mechanisms for such data delivery is challenging given that dynamic data changes rapidly and unpredictably, the latter making it very hard to use simple prediction techniques. To address these challenges we develop mechanisms to obtain timely and consistency-preserving updates for dynamic data by pulling data from the source at strategically chosen points in time, providing Quality of Service (QoS) Guarantees. Motivated by the need for practical system design, but using formal analytical techniques, we offer a systematic approach based on control-theoretic principles. We present a stochastic controller based on the Linear Quadratic Gaussian (LQG) technique as a means for deciding when to next refresh data from a source. A simple enhancement of the LQG algorithm allows us to provide QoS guarantees. Using real-world traces of real-time data we show the superior performance of our feedback-driven control-theoretic approach by comparing with a previously proposed adaptive refresh technique, a pattern matching technique, and a proportional controller with dynamically changing tuning criteria.