The Data Management Body of Knowledge (DAMA-DMBOK2) presents a comprehensive view of the challenges, complexities, and value of effective data management.

Today's organizations recognize that managing data is central to their success. They recognize data has value and they want to leverage that value. As our ability and desire to create and exploit data has increased, so too has the need for reliable data management practices.

The second edition of DAMA International's Guide to the Data Management Body of Knowledge (DAMA-DMBOK2) updates and augments the highly successful DMBOK1. An accessible, authoritative reference book written by leading thinkers in the field and extensively reviewed by DAMA members, DMBOK2 brings together materials that comprehensively describe the challenges of data management and how to meet them by:

Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas.
Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics.
Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals.

DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles:

Data is an asset with unique properties
The value of data can be and should be expressed in economic terms
Managing data means managing the quality of data
It takes metadata to manage data
It takes planning to manage data
Data management is cross-functional and requires a range of skills and expertise
Data management requires an enterprise perspective
Data management must account for a range of perspectives
Data management is data lifecycle management
Different types of data have different lifecycle requirements
Managing data includes managing risks associated with data
Data management requirements must drive information technology decisions
Effective data management requires leadership commitment

Chapters include:

Data Management
Data Handling Ethics
Data Governance
Data Architecture
Data Modeling and Design
Data Storage and Operations
Data Security
Data Integration and Interoperability
Document and Content Management
Reference and Master Data
Data Warehousing and Business Intelligence
Metadata Management
Data Quality Management
Big Data and Data Science
Data Management Maturity Assessment
Data Management Organization and Role Expectations
Data Management and Organizational Change Management

Standardization of data management disciplines will help data management professionals perform more effectively and consistently. It will also enable organizational leaders to recognize the value and contributions of data management activities.

Rese�a del editor

Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas.
Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics.
Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals.

Data is an asset with unique properties
The value of data can be and should be expressed in economic terms
Managing data means managing the quality of data
It takes metadata to manage data
It takes planning to manage data
Data management is cross-functional and requires a range of skills and expertise
Data management requires an enterprise perspective
Data management must account for a range of perspectives
Data management is data lifecycle management
Different types of data have different lifecycle requirements
Managing data includes managing risks associated with data
Data management requirements must drive information technology decisions
Effective data management requires leadership commitment

Chapters include:

Data Management
Data Handling Ethics
Data Governance
Data Architecture
Data Modeling and Design
Data Storage and Operations
Data Security
Data Integration and Interoperability
Document and Content Management
Reference and Master Data
Data Warehousing and Business Intelligence
Metadata Management
Data Quality Management
Big Data and Data Science
Data Management Maturity Assessment
Data Management Organization and Role Expectations
Data Management and Organizational Change Management

"Sobre este t�tulo" puede pertenecer a otra edici�n de este libro.

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