DAMA-DMBOK

1. Data Management is Foundational for Organizational Success

Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.

Data as a vital asset. Organizations increasingly recognize data as a vital enterprise asset, crucial for gaining insights into customers, products, and services. Effective data management is not accidental; it requires intention, planning, coordination, and commitment. It's about actively managing data as an asset to derive ongoing value, similar to how financial and physical assets are managed.

Wide-ranging activities. Data management activities are wide-ranging, from strategic decision-making about data value to the technical deployment and performance of databases. This requires a blend of technical and non-technical skills, with shared responsibility between business and IT roles. Collaboration is key to ensuring high-quality data that meets strategic needs.

DAMA's role. To support data management professionals, DAMA International provides resources like the DAMA Guide to the Data Management Body of Knowledge (DMBOK2). This guide builds upon foundational knowledge, addressing advancements in the profession and offering a framework for managing data effectively. It emphasizes the importance of data management principles and the DAMA Data Management Framework.

2. Ethical Data Handling is a Social Responsibility and Competitive Advantage

An ethical approach to data use is increasingly being recognized as a competitive business advantage.

Ethical principles. Data handling ethics are concerned with how to procure, store, manage, use, and dispose of data in ways that align with ethical principles such as fairness, respect, responsibility, integrity, quality, reliability, transparency, and trust. Ethical data handling is not only a legal requirement but also a matter of social responsibility. It's necessary for the long-term success of any organization that wants to get value from its data.

Core concepts. The ethics of data handling center on the impact on people, the potential for misuse, and the economic value of data. Organizations must recognize their ethical obligation to protect data entrusted to them, fostering a culture that values the ethical handling of information. This includes managing data quality to reduce the risk of misrepresentation, misuse, or misunderstanding.

Competitive advantage. An ethical approach to data use is increasingly being recognized as a competitive business advantage, enhancing trustworthiness and improving relationships with stakeholders. Reducing the risk of data misuse and securing data from criminals are primary reasons for cultivating ethical principles. The emerging roles of Chief Data Officer, Chief Risk Officer, and Chief Privacy Officer are focused on controlling risk by establishing acceptable data handling practices.

3. Data Governance Provides Oversight and Direction

Data Governance (DG) is defined as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.

Defining data governance. Data Governance (DG) is the exercise of authority and control over the management of data assets. It ensures that data is managed properly, according to policies and best practices. The scope of a data governance program includes strategy, policy, standards, oversight, compliance, issue management, data management projects, and data asset valuation.

Activities. To accomplish its goals, a Data Governance program develops policies and procedures, cultivates data stewardship practices, and engages in organizational change management. This requires strong leadership and the support of organizational change management. Data governance is not an end in itself; it needs to align directly with organizational strategy.

Principles. Implementing a DG program requires commitment to change and should be sustainable, embedded, and measured. Guiding principles include leadership and strategy, being business-driven, shared responsibility, multi-layered, framework-based, and principle-based. Data Governance is separate from IT governance, focusing exclusively on the management of data assets.

4. Data Architecture Blueprints Data Asset Management

Architecture refers to the art and science of building things (especially habitable structures) and to the results of the process of building – the buildings themselves.

Defining data architecture. Data Architecture involves the organized arrangement of component elements intended to optimize the function, performance, feasibility, cost, and aesthetics of data assets. It includes Data Architecture outcomes (artifacts), activities, and behavior. Data Architecture is fundamental to data management, providing a blueprint for managing data assets.

Enterprise architecture domains. Data Architecture operates in the context of other architecture domains, including business, application, and technical architecture. Architects from different domains must address development directions and requirements collaboratively, as each domain influences and puts constraints on the other domains. Enterprise Architecture frameworks, such as the Zachman Framework, provide ways of thinking about and understanding architecture.

Data architecture outcomes. Primary Data Architecture outcomes include data storage and processing requirements and designs of structures and plans that meet the current and long-term data requirements of the enterprise. Architects define and maintain specifications that define the current state of data, provide a standard business vocabulary, align with enterprise strategy, express strategic data requirements, outline high-level integrated designs, and integrate with the overall enterprise architecture roadmap.

5. Data Modeling Translates Business Needs into Precise Data Structures

Data modeling is the process of discovering, analyzing, representing, and communicating data requirements in a precise form called the data model.

Defining data modeling. Data modeling is the process of discovering, analyzing, and scoping data requirements, and then representing and communicating these data requirements in a precise form called the data model. It is a critical component of data management, requiring organizations to discover and document how their data fits together. Data models depict and enable an organization to understand its data assets.

Data model components. Data models typically include entities, relationships, attributes, and domains. Entities are things about which an organization collects information, while relationships capture the associations between entities. Attributes are properties that identify, describe, or measure an entity, and domains define the complete set of possible values for an attribute.

Data modeling schemes. Common schemes used to represent data include Relational, Dimensional, Object-Oriented, Fact-Based, Time-Based, and NoSQL. Each scheme uses specific diagramming notations and is suited to particular technologies. Data models exist at three levels of detail: conceptual, logical, and physical.

6. Data Storage and Operations Maximize Data Value Through its Lifecycle

Data Storage and Operations includes the design, implementation, and support of stored data to maximize its value throughout its lifecycle, from creation/acquisition to disposal.

Defining data storage and operations. Data Storage and Operations includes the design, implementation, and support of stored data, to maximize its value throughout its lifecycle. It encompasses managing database technology and managing databases. Database administrators (DBAs) play key roles in both aspects.

Database architecture types. Databases can be classified as centralized or distributed, with distributed systems further categorized as federated (autonomous) or non-federated (non-autonomous). Virtualization and cloud platforms offer additional options for data storage and operations. Database processing types include ACID (Atomicity, Consistency, Isolation, Durability) and BASE (Basically Available, Soft State, Eventual Consistency), with the CAP Theorem defining the trade-offs between consistency, availability, and partition tolerance.

Data lifecycle management. DBAs maintain and assure the accuracy and consistency of data over its entire lifecycle, from initial implementation through obtaining, backing up, and purging data. This includes implementing policies and procedures for acquisition, migration, retention, expiration, and disposition of data.

7. Data Security Protects Data Assets from Misuse and Breaches

Data Security includes the planning, development, and execution of security policies and procedures to provide proper authentication, authorization, access, and auditing of data and information assets.

Defining data security. Data Security includes the planning, development, and execution of security policies and procedures to provide proper authentication, authorization, access, and auditing of data and information assets. The goal is to protect information assets in alignment with privacy and confidentiality regulations, contractual agreements, and business requirements.

Essential concepts. Key concepts in data security include vulnerability, threat, and risk. Vulnerabilities are weaknesses in a system, while threats are potential offensive actions. Risk is the possibility of loss and the condition that poses the potential loss. Data security measures include access controls, data masking/encryption, and network security measures like firewalls and intrusion detection systems.

Data security activities. Data security activities involve identifying data security requirements, defining data security policy, defining data security standards, and implementing controls and procedures. These activities are guided by principles such as collaboration, an enterprise approach, proactive management, clear accountability, and a Metadata-driven approach.

8. Data Integration and Interoperability Enables Data Sharing and Consolidation

Data Integration and Interoperability (DII) describes processes related to the movement and consolidation of data within and between data stores, applications and organizations.

Defining data integration and interoperability. Data Integration and Interoperability (DII) describes processes related to the movement and consolidation of data within and between data stores, applications, and organizations. Integration consolidates data into consistent forms, either physical or virtual. Data Interoperability is the ability for multiple systems to communicate.

Essential concepts. Key concepts in DII include Extract, Transform, and Load (ETL), latency, application coupling, and enterprise service bus (ESB). ETL involves extracting data from sources, transforming it to meet target requirements, and loading it into the target system. Latency refers to the time difference between data generation and availability.

DII architecture concepts. Application coupling describes the degree to which two systems are entwined, with loose coupling being preferred. An Enterprise Service Bus (ESB) acts as an intermediary between systems, passing messages between them. Service-Oriented Architecture (SOA) provides independent services with well-defined interface calls.

9. Document and Content Management Governs Unstructured Information

Document and Content Management entails controlling the capture, storage, access, and use of data and information found in a range of unstructured media, especially documents needed to support legal and regulatory compliance requirements.

Defining document and content management. Document and Content Management entails controlling the capture, storage, access, and use of data and information stored outside relational databases. Its focus is on maintaining the integrity of and enabling access to documents and other unstructured or semi-structured information. It also has strategic drivers, such as regulatory compliance, the ability to respond to litigation and e-discovery requests, and business continuity requirements.

Essential concepts. Key concepts include content, content management, content Metadata, content modeling, and content delivery methods. Content refers to the data and information inside the file, document, or website. Controlled vocabularies, such as taxonomies and ontologies, are used to index, categorize, tag, sort, and retrieve content.

Activities. Activities in document and content management include planning for lifecycle management, managing the lifecycle, and publishing and delivering content. This involves capturing records and content, managing versioning and control, backing up and recovering data, and managing retention and disposal.

10. Reference and Master Data Ensures Consistency and Accuracy

Reference and Master Data includes 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.

Defining reference and master data. Reference and Master Data includes 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. Master Data is data about the business entities that provide context for business transactions and analysis, while Reference Data is data used to characterize or classify other data.

Essential concepts. Key concepts include system of record, system of reference, trusted source, and golden record. A System of Record is an authoritative system where data is created/captured, while a System of Reference is an authoritative system where data consumers can obtain reliable data. A Trusted Source is recognized as the 'best version of the truth,' and a Golden Record represents the most accurate data about entity instances.

Activities. Activities in Reference and Master Data Management include defining drivers and requirements, assessing data sources, defining architectural approach, modeling data, defining stewardship and maintenance processes, and establishing governance policies.

11. Data Warehousing and Business Intelligence Drives Insight and Decision-Making

Data Warehousing and Business Intelligence 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.

Defining data warehousing and business intelligence. Data Warehousing and Business Intelligence 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. The primary driver is to support operational functions, compliance requirements, and Business Intelligence (BI) activities.

Essential concepts. Key concepts include Business Intelligence (BI), Data Warehouse (DW), and Data Warehousing. BI refers to a type of data analysis aimed at understanding organizational activities and opportunities, as well as the technologies that support this analysis. A Data Warehouse is an integrated decision support database, and Data Warehousing describes the operational processes that maintain the data in a data warehouse.

Activities. Activities in Data Warehousing and Business Intelligence include understanding requirements, defining and maintaining the DW/BI architecture, developing the data warehouse and data marts, populating the data warehouse, implementing the business intelligence portfolio, and maintaining data products.

12. Data Quality Ensures Fitness for Use

Data Quality includes the planning and implementation of quality management techniques to measure, assess, and improve the fitness of data for use within an organization.

Defining data quality. Data Quality includes the planning and implementation of quality management techniques to measure, assess, and improve the fitness of data for use within an organization. It involves defining high-quality data, defining a data quality strategy, identifying critical data and business rules, performing an initial data quality assessment, identifying and prioritizing potential improvements, defining goals for data quality improvement, and developing and deploying data quality operations.

Essential concepts. Key concepts include data quality dimensions, critical data, and business rules. Data quality dimensions are measurable characteristics of data, such as completeness, validity, accuracy, consistency, and timeliness. Critical data is the data most important to the organization, and business rules define or constrain aspects of business processing.

Activities. Activities in Data Quality Management include defining high-quality data, defining a data quality strategy, identifying critical data and business rules, performing an initial data quality assessment, identifying and prioritizing potential improvements, defining goals for data quality improvement, and developing and deploying data quality operations.

13. Big Data and Data Science Uncover New Opportunities

Big Data and Data Science describes the technologies and business processes that emerge as our ability to collect and analyze large and diverse data sets increases.

Defining big data and data science. Big Data refers not only to the volume of data but also to its variety and velocity. Data Science merges data mining, statistical analysis, and machine learning with data integration and data modeling capabilities to build predictive models that explore data content patterns.

Essential concepts. Key concepts include the Data Science process, Big Data architecture components, and analytic modeling. The Data Science process involves defining strategy and business needs, choosing data sources, acquiring and ingesting data, developing hypotheses and methods, integrating and aligning data, exploring data using models, and deploying and monitoring.

Activities. Activities in Big Data and Data Science include defining strategy and business needs, choosing data sources, acquiring and ingesting data, developing hypotheses and methods, integrating and aligning data, exploring data using models, and deploying and monitoring.

14. Maturity Assessments Guide Data Management Improvement

Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.

Defining data management maturity assessment. Data Management Maturity Assessment (DMMA) is an approach to process improvement based on a framework that describes how characteristics of a process evolve from ad hoc to optimal. It helps organizations evaluate their level of maturity and put in place a plan to improve their capabilities.

Essential concepts. Key concepts include assessment levels and characteristics, assessment criteria, and existing DMMA frameworks. Assessment levels typically range from initial/ad hoc to optimized, with characteristics defined for each level. Assessment criteria are used to evaluate processes, and existing frameworks provide guidance and structure for the assessment.

Activities. Activities in Data Management Maturity Assessment include planning assessment activities, performing the maturity assessment, interpreting results, creating a targeted program for improvements, and re-assessing maturity.

15. Data Management Organization and Roles Enable Effective Practices

Data management activities are wide-ranging. They include everything from the ability to make consistent decisions about how to get strategic value from data to the technical deployment and performance of databases.

Defining data management organization and roles. Data Management Organization and Role Expectations provide best practices and considerations for organizing data management teams and enabling successful data management practices. It involves understanding existing organization and cultural norms, defining data management organizational constructs, identifying critical success factors, building the data management organization, and defining interactions between the DMO and other data-oriented bodies.

Data management organizational constructs. Common organizational constructs include decentralized, network, centralized, hybrid, and federated operating models. Each model has its own benefits and drawbacks, and the best model for an organization depends on its specific needs and culture.

Critical success factors. Critical success factors for building a Data Management Organization include executive sponsorship, a clear vision, proactive change management, leadership alignment, communication, stakeholder engagement, orientation and training, adoption measurement, adherence to guiding principles, and evolution not revolution.

16. Change Management Embeds Data Management into Organizational Culture

Data Management is cross-functional; it requires a range of skills and expertise: A single team cannot manage all of an organization’s data.

Defining data management and organizational change management. Data Management and Organizational Change Management describes how to plan for and successfully move through the cultural changes that are necessary to embed effective data management practices within an organization. It involves understanding the laws of change, managing a transition, addressing Kotter’s eight errors of change management, following Kotter’s eight-stage process for major change, understanding the formula for change, and diffusing innovations and sustaining change.

Kotter's eight-stage process for major change:

1. Establishing a sense of urgency
2. Forming a powerful guiding coalition
3. Creating a vision
4. Communicating the vision
5. Empowering others to act on the vision
6. Planning for and creating short-term wins
7. Consolidating improvements and producing still more change
8. Institutionalizing new approaches

Communicating data management value. Communicating data management value involves following communications principles, conducting audience evaluation and preparation, accounting for the human element, developing a communication plan, and keeping communicating.

Last updated: February 23, 2025