DAMA Framework
In this post I’ll cover what dama framework is, what are the different pillars of it and how it can be used to implement a data strategy.
The term Data Management refers to the development, implementation, and supervision of policies, programs, and practices that deliver, control, protect, and improve the value of data and information assets.
According to DAMA framework, there are 11 knowledge areas or pillars of data management. we’ll look at each of them below
1. Data Governance
- This pillar provides direction and oversight for data management by establishing a system of decision rights over data that accounts for the needs of the enterprise.
- This pillar focuses on vision, strtegy and target operating model which enables other 10 areas.
- Think it like a base of a building, poor data governance leads to failed/weak data management projects.
2. Data Architecture
- This pillar defines the blueprint for managing data assets by aligning with organizational strategy to establish strategic data requirements and designs to meet these requirements.
- This pillar focuses on Enterprise data models, tool standards, and system naming conventions
3: Data Modeling and Design
- This is the process of discovering, analyzing, representing, and communicating data requirements in a precise form called the data model
- This pillar focuses on data model management procedures, data modeling naming conventions, definition standards, standard domains, and standard abbreviations
4. Data Storage and Operations
- This pillar includes the design,implementation,and support of stored data to maximize its value. Operations provide support throughout the data lifecycle from planning for to disposal of data.
- Tool standards, standards for database recovery and business continuity, database performance, data retention, and external data acquisition
5. Data Security
- This pillar ensures that data privacy and confidentiality are maintained, that data is not breached, and that data is accessed appropriately.
- Data access security standards, monitoring and audit procedures, storage security standards, and training requirements
6. Data Integration and Interoperability
- This pillar includes processes related to the movement and consolidation of data within and between data stores, applications, and organizations
- Standard methods and tools used for data integration and interoperability
7. Document and Content Management
- This pillar covers planning, implementation, and control activities used to manage the lifecycle of data and information found in a range of unstructured media, especially documents needed to support legal and regulatory compliance requirements
- this focuses on content management standards and procedures, including use of enterprise taxonomies, support for legal discovery, document and email retention periods, electronic signatures, and report distribution approaches
8. Reference and Master Data
- This knowledge area covers ongoing reconciliation and maintenance of core critical shared data to enable consistent use across systems of the most accurate, timely, and relevant version of truth about essential business entities.
- Reference Data Management control procedures, systems of data record, assertions establishing and mandating use, standards for entity resolution
9. Data Warehousing and Business Intelligence
- This includes the planning, implementation, and control processes to manage decision support data and to enable knowledge workers to get value from data via analysis and reporting.
- Tool standard, processing standards and procedures, report and visualization formatting standards, standards for Big Data handling
10. Metadata
- This pillar includes planning, implementation,andc ontrol activities to enable access to high quality, integrated Metadata, including definitions, models, data flows, and other information critical to understanding data and the systems through which it is created, maintained, and accessed.
- Standard business and technical Metadata to be captured, Metadata integration procedures and usage
11. Data Quality
- This pillar covers the planning and implementation of quality management techniques to measure, assess, and improve the fitness of data for use within an organization.
- Data quality rules, standard measurement methodologies, data remediation standards and procedures
I’ll try to cover each of this pillar in more detail in my coming posts.