Data management is a sub-set of information management that governs organization and control of the structure and design, storage, movement, security and quality of information.

What comprises a data management strategy?

• Structure and design focuses primarily on data modeling and artifacts that flow from data modeling activities.  The goal is to provide direction on modeling practices, the use of conceptual, logical, and physical models, naming standards, and modeling tools;

• Data storage focuses on the management of persistent data stores throughout the organization. It covers such topics as database selection guidelines, backup & recovery procedures, data retention/archiving policies, and disaster recovery planning;

• Data movement focuses on the movement of data between systems.  In this context, “systems” include external data sources, operational systems, and analytic data stores;

• Metadata is a key enabler for realizing the goals of information management. It has touch points throughout the vertical disciplines e.g.
  

• The structure and design discipline will populate the metadata repository with entity and attribute definitions; 
• The technical metadata will include information about where (in which databases) specific tables are stored; 
• Analytic data access tools (OLAP tools) will make use of the business metadata to provide entity and attribute definitions to the end user; and
• Data movement processes will access business metadata to retrieve validation and transformation rules to be applied during the ETL processes.

• Data Quality is not a stand-alone discipline. It is a result of adherence to a sound data management methodology. Data quality is concerned with: 
• Data validations in operational (OLTP) systems;
• Validation of purchased data sets;
• Quality checks on data extracts;
• Audit controls to verify integrity of bulk data movements;
• Use of common field validations (both technical and business) across systems including the ETL environment.

• Data management practice is a roadmap of tasks, artifacts, standards, guidelines, and best practices. It provides a structured framework for delivery of data-related projects.

The following topics should be addressed as part of the information management strategy:

Are there data model standards and best practices for structure and design?
Does it include naming standards and best practices for:

• Conceptual data model?
• Enterprise data model?
• Logical data model?
• Entity relationship diagrams?
• Physical data model?
• Class list names?

Is a database management policy available to address data storage?
Does the database management policy specify how database operations will function within the organization and does it address:

• How frequently data is “backed up”?  
• If off-site storage utilized? 
• Where backup data is located?

Are data warehouse best practices established?
Are data movement best practices documented and communicated to project teams?
Are master data management best practices documented?
Does a data security policy address electronic and physical security concerns?
Does the information security policy specify how data and information will be protected from authorized access?
Is there a meta data management policy that specifies internal requirements for gathering, maintaining and providing metadata?
Is a data quality standards document available?
Do IT best practices include data management best practices?

Summary...

Information management is a discipline that governs accountability for the structure and design, storage, movement, security, quality, delivery and usage of information required for management and business intelligence purposes.

The data management strategy is comprised of disciplines involved with specific segments of data management and these segments are governed by standards and best practices.