Large-scale linear systems, Ax=b, frequently arise in data science and scientific computing at massive scales, thus demanding effective iterative methods to solve them. Often, these systems are noisy due to operational errors or faulty data-collection processes. In the past decade, the randomized Kaczmarz algorithm (RK) was studied extensively as an efficient iterative solver for such systems. However, the convergence study of RK in the noisy regime is limited and considers measurement noise in the right-hand side vector, b. Unfortunately, that is not always the case, and the coefficient matrix A can also be noisy. In this talk, we motivate and discuss doubly noise linear systems and the performance of the Kaczmarz algorithm applied to such systems.