This paper delves into the pursuit to introduce data centralization methods to small-scale firms and startups. This is done by teaching the founders skills and methods in Excel while tailoring the outputs to their specific industry and customer segments. Throughout this consulting-like process, I recorded each firm’s ability to understand my methods and the relative likelihood for adoption post-consulting. This study has found that the likelihood for adoption lies not in the complexity of the model, but the stage of the startup. By understanding this key difference, this paper aims to provide a blueprint for firms to follow when implementing data-centric practices, but giving specific recommendations based on two key life cycle points: inflection and continuation. The significance of these two terms will be discussed in the body of this thesis.