DMBOK2::DM Foundation, DG, DQ

Data Management Foundation, Data Governance, Data Quality

What is Data Management?

The development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data.

Why is Data Management important?

It ensures data is reliable, secure, and usable for decision-making.

What is a Data Asset?

Data that has value to an organization.

What is Metadata?

Data that describes other data.

What are examples of Metadata?

Table names, column definitions, lineage, transformation rules.

What is Data Governance?

The exercise of authority and control over the management of data assets.

What is the goal of Data Governance?

Ensure data is managed as a strategic asset.

What is a Data Governance Framework?

A structure defining roles, policies, standards, and decision rights for managing data.

What is a Data Governance Council?

A cross-functional group responsible for governance oversight.

What is Data Stewardship?

The management and oversight of data assets by designated stewards.

What is a Data Steward responsible for?

Data definition, quality monitoring, and issue resolution.

What is a Data Owner?

A business leader accountable for a data domain.

Difference between Data Owner and Data Steward?

Owner = accountability, Steward = operational management.

What is a Data Policy?

A high-level statement of management intent regarding data.

What is a Data Standard?

A rule that ensures consistency in how data is defined or used.

What is Data Quality?

The degree to which data is fit for its intended purpose.

What are the main Data Quality dimensions?

Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness.

What is Data Accuracy?

Data correctly represents the real-world object or event.

What is Data Completeness?

All required data values are present.

What is Data Consistency?

Data values are the same across systems.

What is Data Timeliness?

Data is available when needed.

What is Data Validity?

Data conforms to defined formats and rules.

What is Data Uniqueness?

Each entity is recorded once without duplication.

What is Data Profiling?

The analysis of data to understand structure, content, and quality.

What is Data Cleansing?

The process of correcting or removing inaccurate data.

What is Data Monitoring?

Ongoing tracking of data quality metrics.

What is Root Cause Analysis in Data Quality?

Identifying the underlying cause of data issues.

What is a Data Issue?

A problem affecting data quality or usability.

What is Data Issue Management?

The process of identifying, tracking, and resolving data problems.

What is Data Control?

A mechanism used to ensure data accuracy and integrity.

What is a Data Quality Rule?

A rule used to validate data correctness.

What is Data Quality Score?

A metric measuring the quality level of data.

What is Preventive Data Quality Control?

Processes preventing data issues from occurring.

What is Detective Data Quality Control?

Processes detecting data errors after they occur.

What is Corrective Data Quality Control?

Processes fixing data errors.

What is Data Standardization?

The process of ensuring data follows consistent formats.

What is Data Validation?

The process of ensuring data conforms to rules.

What is Data Observability?

The ability to understand the health of data through monitoring.

What is Master Data?

Core business entities such as customers, products, employees.

What is Reference Data?

Permissible values used to categorize data.

What is Transactional Data?

Data describing business events.

What is a Data Domain?

A logical grouping of data related to a business area.

What is a Data Asset Inventory?

A catalog of data assets within an organization.

What is Data Lifecycle?

The stages data goes through from creation to deletion.

Typical Data Lifecycle stages?

Create, Store, Use, Share, Archive, Destroy.

What is Data Lifecycle Management?

The management of data from creation through archival or deletion.

What is Data Architecture?

The structure and organization of data assets.

What is Data Modeling?

The process of designing data structures and relationships.

Three levels of Data Modeling?

Conceptual, Logical, Physical.

What is Data Integration?

Combining data from multiple sources.

What is ETL?

Extract, Transform, Load process used to move and transform data.

What is Data Lineage?

The lifecycle and transformation history of data.

What is Data Catalog?

A centralized inventory of data assets.

What is Data Classification?

Organizing data into categories based on sensitivity or use.

What is Data Security?

Protection of data from unauthorized access.

What is Data Privacy?

Protection of personal information.

What is Data Risk?

Potential negative impact due to poor data management.

What is Data Compliance?

Adherence to regulatory requirements regarding data.

What is Data Value?

The measurable business benefit derived from data usage.

What is Data Strategy?

A long-term plan for managing data as a strategic asset.

What is a Data Strategy Roadmap?

A plan outlining data management initiatives.

What is Data Culture?

An organizational mindset valuing data-driven decision making.

What is Data Trust?

The confidence users have in data accuracy and reliability.

What is Data Transparency?

The ability for stakeholders to understand data origin and usage.

What is Data Accountability?

The assignment of responsibility for data management.

What is a Data Governance Policy?

A formal rule defining how data must be managed.

What is a Data Governance Operating Model?

The structure defining governance roles and processes.

What is a Data Stewardship Program?

A program that assigns responsibility for data management.

What is a Data Governance Maturity Model?

A framework used to assess governance capability.

What is a Data Escalation Process?

A process for resolving unresolved data issues.

What is Data Management Capability?

The organization's ability to manage data effectively.

What is DAMA-DMBOK?

A framework describing best practices in data management.

How many Knowledge Areas are in DMBOK2?

11 Knowledge Areas.

What is the purpose of DMBOK?

Provide a standard framework for data management.