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 governance?

• 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.  

• Data movement encompasses the extract-transform-load (ETL) facilities used for bulk data movement.  It also includes mechanisms to support continuous movement of discrete records, rows, or messages between systems.

• 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 data governance:
Data model standards and best practices should be established for structure and design.
They should include naming standards and best practices for:

• Conceptual data model;
• Enterprise data model;
• Logical data model;
• Entity relationship diagrams;
• Physical data model; and
  
• Class list names.

A database management policy should be available to address data storage.
The database management policy should specify how database operations will function within the organization and should also address:

• The frequency of frequently data back-up;
  
• Use of off-site storage; and
  
• Off-site storage location.
  

Data warehouse best practices should be established.
Data movement best practices should be documented and communicated to project teams.
Master data management best practices should be documented.
A data security policy should address electronic and physical security concerns?.
The information security policy should specify how data and information will be protected from authorized access.
A meta data management policy should specify internal requirements for gathering, maintaining and providing metadata.
A data quality standards document should be available.
IT best practices should include data management best practices.
A change management strategy should be defined.
Data governance should be included in corporate governance.
Corporate information should be considered a data asset.
A data management plan should be established.
Data governance should address data management strategy and data warehouse strategy.
Enterprise architecture framework and enterprise architecture strategy should be defined.
Information governance should specify an enterprise information management governance framework.
Information management architecture should be defined.
Information governance should address project management fundamentals
An information management steering committee should be established comprised of IT and business data guardians.
Information governance should be included in the enterprise strategic planning process.

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.