This Chapter describes principles of information management for health systems and the need to focus on key data items required to improve individual and population health. It discusses the collection and analysis of relevant, high-quality data and the importance of agreeing on health programme aims before defining the minimum data set. We review the derivation of health indicators, focusing on WHO indicators. Many indicators rely on linking data from different sources, which requires accurate personal identifiers. Data is useless unless reports based on it can be shared and understood, so data analysts should use different visualization techniques to facilitate and support user decisions such as self-service dashboards. We also review the many high quality, open source, free to use data capture, analysis and data sharing tools that can support health systems, concluding that it is rarely necessary to develop an information system from scratch. Finally, while big data analytics, artificial intelligence and machine learning capture many headlines, health system can achieve much using simple tools to capture relevant, high-quality data and turn it into actionable knowledge to support their decision makers.