Data is not valuable in a vacuum. Data is only valuable once information, insight or in other words knowledge is extracted from it and is used to make decisions, shape policies, and change behaviors.
Data scientists, analysts, and researchers spend a significant amount of time and effort extracting knowledge from data and communicating it. Because extracting knowledge from data can be expensive, it is important to find ways to reduce its cost. A robust and well-designed data infrastructure can contribute to this cost reduction by smoothing the frictions involved with data analytics projects: storing, searching, accessing, understanding, cleaning, transforming, analyzing, and visualizing data. Lowering that cost can go a long way toward increasing data use and knowledge production.