The operations and maintenance monitoring of nuclear power plants (NPPs) in the United States is reliant on manual activities supplying information to a human decision-making process. Several manually collected labor-intensive processes generate information that is not typically used beyond the intended target for collecting that information. The industry has recognized the benefits of both reducing labor-intensive tasks by automating them and increasing the fidelity of the information collected to enable advanced remote monitoring of NPPs using data-driven decision making. This requires developing new means to acquire data from the various data sources of an NPP. While some sources already exist in a digital form, others are collected manually, summarized through conclusive statements, or not collected at all. This paper describes 15 sources of data at an NPP and methods to migrate the data collection from a manual and analog data form to an automated and digital data form that increases the data fidelity in time and space. Three states of data collection methods are described for each data source. The states describe a base state for how the data are currently being collected, a modern state for a more efficient method of collecting data that has not yet been implemented, and a state of the art for an advanced method of collecting data that is not yet ready for deployment.