Data Stewardship Glossary

Large Language Models (LLMs) are advanced artificial intelligence systems that understand, process, and generate human-like content. This understanding sheds light on their implications for data management, particularly in areas like Data Stewardship and Master Data Management. Additionally, as we delve into the workings of LLMs, we can explore how they relate to concepts such as a Data Dictionary and the use of a Graph Database, considering their types and potential future developments.

Data integration combines information from multiple sources into unified, reliable data that drives business operations and decision-making.  Multiple components, types, and emerging technologies formulate data integration basics. Learn more in this “What is” piece.

Data lineage, the tracking and documentation of data flows, can seem complex. This piece breaks down data lineage components, types, processes and intersections in an easy-to-understand way. 

A knowledge graph is a structured data representation that reveals patterns and connections in the real world. This tool is essential to ensuring good data quality when processing data in real-time. Learn more in this "What Is" piece.

Data stewardship (DS) is the practice of overseeing an organization’s data assets, ensuring they are accessible, reliable, and secure throughout their lifecycles. This includes maintaining a comprehensive data dictionary that defines the various data elements and their relationships. Learn about the different types of data stewardship, their importance, and the challenges they face, especially in the context of emerging technologies like graph databases and large language models. Additionally, explore how effective master data management plays a crucial role in enhancing data stewardship.