【Abstract】We develop the operational data analytics (ODA) framework for the classical service design problem of G=G=c=k = G = c = k systems. The customer arrival rate is unknown. Instead, some historical data of interarrival times are collected. The data -integration model, specifying the mapping from the arrival data to the service rate, is formulated based on the time -scaling property of the stochastic service process. Validating the data -integration model against the long -run average service reward leads to a uniformly optimal service rate for any given sample size. We further derive the ODA-predicted reward function based on the data -integration model, which gives a consistent estimate of the underlying reward function. Our numerical experiments show that the ODA framework can lead to an efficient design of service rate and service capacity, which is insensitive to model specification. The ODA solution exhibits superior performance compared with the conventional estimation -and -then -optimization solutions in the small sample regime.