Data Localization: Laws or regulations requiring data about a nation's citizens or residents to be collected, processed, and/or stored inside the country before being transferred internationally, often as a condition for conducting business in a country. These measures are often enforced to protect data privacy, maintain data security, or enable law enforcement access to data.

Data Loss Prevention (DLP): A set of tools or processes used to identify, monitor, and protect data in use, data at rest, and data in motion through deep content inspection and with a focus on sensitive data. The goal is to prevent sensitive data from being lost, misused, or accessed by unauthorized users, thereby maintaining the data's confidentiality and integrity.

Data Mapping: The process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including data transformation or data mediation between a data source and a destination. It helps to define how individual data fields are matched from one database to another, ensuring that information is transferred accurately and correctly during system migrations, data consolidation, or ETL (Extract, Transform, Load) processes. It is fundamental in data management and integration activities.

Data Mining: The analytical process designed to explore large amounts of data in search of consistent patterns and systematic relationships between variables and then validate the findings by applying the detected patterns to new subsets of data. It is a multidisciplinary approach to discovering useful information from large data sets and is used for various purposes, including market analysis, fraud detection, customer retention, and more. Data mining often involves the use of machine learning and statistical techniques to discover these patterns.

Data Modeling: The process of developing data structures and specifications. It involves creating a conceptual representation of data objects, the associations between different data objects, and the rules governing these associations. This is instrumental in managing data as a resource, promoting data sharing, increasing data consistency, and designing databases.

Data Normalization: A systematic approach of decomposing tables to eliminate data redundancy and undesirable characteristics like insertion, update, and deletion anomalies. The goal of this technique is to reduce and even eliminate data redundancy, an issue that can lead to data anomalies and inconsistencies within databases or data storage systems.