Catalog
concept#Data#Platform#Best Practices#Data Architecture

Data Management Body of Knowledge (DAMA-DMBOK)

A comprehensive framework for data management, providing best practices, models, and standards.

The DAMA-DMBOK is a guide for data management that defines principles and processes to ensure effective data governance.
Established
Medium

Classification

  • Medium
  • Organizational
  • Architectural
  • Intermediate

Technical context

CRM SystemsDatabase Management SystemsBusiness Intelligence Tools

Principles & goals

Data quality must be monitored continuously.Data management should be consistent across the organization.Data security is of utmost priority.
Build
Enterprise

Use cases & scenarios

Compromises

  • Weak data integrity can jeopardize decisions.
  • Lack of acceptance by employees.
  • Technological dependencies could cause issues.
  • Conduct regular training for employees.
  • Clearly communicate data management policies.
  • Actively gather feedback from users.

I/O & resources

  • Business Goals
  • Available Data Resources
  • Technological Infrastructure
  • Establishment of a successful data management program
  • Successful integration of new technologies
  • Improved data availability

Description

The DAMA-DMBOK is a guide for data management that defines principles and processes to ensure effective data governance. It encompasses a range of disciplines, including data architecture, quality, and security.

✔

  • Improved data integrity.
  • Increased efficiency in data management.
  • Better decision-making through high-quality data.

✖

  • Requires extensive training resources.
  • Can be time-consuming in implementation.
  • Dependent on organizational culture.

  • Data Quality Index

    An index to measure data quality in real-time.

  • User Satisfaction with Data Services

    Assessment of user satisfaction with the provided data.

  • Data Integrity Rates

    Percentage of data that falls within the defined standards.

Example Bank for Data Management

A bank implements DAMA-DMBOK to improve its data quality standards.

Consulting Firm with Data-Driven Approach

A consulting firm utilizes DAMA-DMBOK resources to assist clients with data management.

In-House Training for Employees

A company offers in-house training based on DAMA-DMBOK principles.

1

Clarify goals and expectations.

2

Identify and engage stakeholders.

3

Develop strategies for data management.

⚠️ Technical debt & bottlenecks

  • Using outdated technologies.
  • Insufficient documentation of data management processes.
  • Lack of standards for data integrity.
Insufficient data expertise in the organization.Lack of integration between systems.Complexities due to outdated technologies.
  • Ignoring data quality issues.
  • Disregarding data protection regulations.
  • Lack of training for employees.
  • Using too many tools that are not integrated.
  • Considering data management as a one-time task.
  • Lack of a long-term strategy for data.
Analytical SkillsKnowledge in Data ArchitectureUnderstanding of regulatory requirements
Regulatory requirements for data management.Competitive pressure to improve data quality.Technological advancements in data processing.
  • • Limited budgets for data initiatives.
  • • Lack of management support.
  • • Data protection regulations may limit implementation.