Data management is the process of collecting, organizing, protecting, storing and sharing data. It is an essential part of any organization that understands the importance of data for decision-making. Data management includes a wide range of activities, from data modeling to data governance and data quality. It is important to have a well-defined process in place to ensure that data is accurate, available and accessible.
Data modeling is the process of discovering, analyzing, representing and communicating data requirements in a precise form called a data model. Data governance involves planning, creating, implementing, and enforcing policies that describe how an organization manages its data. Its ultimate goal is to ensure the widespread availability of high-quality data information that is standardized, secure, compatible and up to date. Data management systems and processes should be documented in a central knowledge management system, such as Confluence or Notion.
It is also important to define procedures to keep information up to date, as people and their knowledge may leave the organization, and data management needs will certainly evolve. Training and teaching people to manage data according to their data governance policies and processes is also essential. An architecture provides a blueprint for the databases and other data platforms to be implemented, including specific technologies to adapt to individual applications. Master data management is related to governance and data quality, although it has not been adopted as widely as the other two data management functions. Data quality takes care of the information it captures, stores and distributes to ensure that it is complete and up to date.
A well-executed data management strategy can help companies gain potential competitive advantages over their business rivals, both by improving operational efficiency and by enabling better decision-making. When there is no clear use case for the foreseeable future, consider deleting information, moving it to a data lake, or not capturing it in the first place. With its acquisition of Cerner, Oracle intends to create a national and anonymous patient database, a path full of potential opportunities for organizations that understand how to manage their data effectively.