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Data Management Plan

Need a data management plan and want practical standards and best practices for information management and data governance?

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

What is Data Storage?

The data storage discipline focuses on the management of persistent data stores throughout the organization.  A data management plan needs to address roles, responsibilities, tasks and deliverables for topics such as database selection guidelines, backup & recovery procedures, data retention/archiving policies, and disaster recovery planning.



What are data management principles?

Data management is based on the following principles:
  • Data is a valuable corporate asset e.g. it is collected, organized, stored and used to support business objectives therefore making accurate and timely data an important asset for the organization;
  • Data is defined separately from the technology used to collect and store it e.g. data requirements are clearly recorded prior to designing automated data collection and storage methods so that business needs are clearly understood;
  • Accurate information, or metadata, is essential i.e.effective management of data collected from functional areas requires that accurate metadata be kept;
  • Common data management standards, best practices and guidelines are used e.g. a common approach to defining, modeling, designing and documenting data will improve quality and make it easier to share data among systems and organizational departments.
What is included in a data management plan?

A data management plan needs to address roles, responsibilities, tasks and deliverables for the following:
  • Planning and Requirements analysis;
  • Architecture and design;
  • Development;
  • Quality assurance testing;
  • Transition to production; and
  • Operations, maintenance and data disposition phases.
Data management checklist

Requirements analysis

Roles, responsibilities, tasks and deliverables should be defined for:
  • Business systems analyst;
  • Data analyst;
  • Data modeler;
  • Data architect; and
  • Database administrator.
Data requirements guidelines should address documentation guidelines for:
  • Entity list;
  • Entity definitions;
  • Entity identifiers;
  • Conceptual data model;
  • Likely sources of data;
  • Data model validation;
  • Data distribution plans; and
  • Data collection burden for all stakeholders.
Data documentation responsibilities should be defined.
Lifecycle methodology and data modeling/data management tools should be defined.
Data requirements should be defined in the form of a logical data model.
Data movement and data flow requirements should be defined in the form of a data flow diagram.
Data security requirements should be specified
Data quality assurance plans should be completed.
High level data conversion and history data requirements should
Requirements management plans should be defined.

Architecture and design specifications should include:

Physical data model.
Database design document.
Source to target mappings.
Operational support specifications including:
  • Data backup and restore specifications; and
  • Security logging and recovery plans.
Data movement design specifications.
Data conversion specifications.
Business intelligence and reporting architecture and design specifications.
Data model repositories;
Data movement code repositories;
Metadata repositories;
Business intelligence code repositories; and
Configuration management databases .

Development and build phase should include documentation for:

Data structures for production support.
User and production support training plans.


Quality assurance testing should include documentation for:

Test data acquisition plan;
Test plan;
Data management testing support roles and responsibilities.

Transition to production should define:

Production data dictionary.
Production metadata.
Data transition plans.


Operations, maintenance and data disposition phase should include data management plans for:

Database and metadata management.
Configuration management support.
Audit and evaluation support.
Data definition language (DDL) disposition.
Data disposition.
Cut over plans.

Summary...

An enterprise data warehouse is a key component of an information management framework.

A data management plan and best practices are required to ensure rapid project delivery and ongoing data warehouse management.


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