Scoping the Information Management Practice
Filed under: Business Intelligence, Data Analysis, Data Governance, Data Integration, information strategy, Metadata
Even if in reality the dividing lines for data management are not always well-defined, it is possible to organize different aspects of information management within a virtual stack that suggests the interfaces and dependencies across different functional layers, which we will examine from the bottom – up.
The lowest level in this diagram, file management, forms the basis for all information management activities, and is often handled intrinsically within the operating system. However, with the growing interest in big data and its dependence on distributed file structures, file management reemerges as a critical component of the information management stack. The ability to differentiate between structured and unstructured data then becomes valuable in the context of determining the optimal methods for data storage and file management. Metadata management is used for managing data standards, reference data, and standard data models.
Decisions about data organization for both structured and unstructured data will influence the decision made for the data management, such as what might be deemed “legacy” data management frameworks (such as IMS or VSAM files), traditional relational database management systems (RDBMS) vs. newer NoSQL methods, and the decision as to whether these management schemes are to be implemented on top of big data platforms.
Above those levels we can begin to consider aspects of content. The first layer is master data management, used to provide shared access to a unified view of core data domains such as customer and product. Business processes rely on database management systems; transaction systems use transaction-oriented models for RDBMS systems, while reporting and analytics systems will use alternate data schemas in data warehouses that are optimized for rapid access. Data integration methods are used to facilitate the movement and integration of information into the target systems, while data quality and enrichment methods and tools will ensure observance of the quality expectations for the user community. Lastly, enabling business applications to access the various data resources requires data access and control methods.