It is the process of formulating, implementing, and supervising plans, systems, procedures, and practices throughout their life cycle in order to deliver, control, protect, and enhance the value of data and information assets.

Any person who works in various aspects of data management and uses their work to achieve the strategic goals of the organization

Data management requires both technical and non-technical skills

Responsibility for managing data must be shared between business and information technology roles, and these two areas need to collaborate to ensure that the organization has high-quality data that meets strategic needs.

The main driver of data management is the ability of organizations to derive value from their data assets

Understand and support the information needs of the enterprise and its stakeholders to be met

Acquire, store, protect data and ensure the integrity of data assets

Ensure the quality of data and information

Ensure data privacy and confidentiality of stakeholders

Protect data and information from unauthorized or improper access, operation and use

Ensure data can effectively serve corporate value-added goals

In information technology, data is also understood as information stored in digital form (although data is not limited to information that has been digitized, and the same principles of data management apply to data on paper as in databases)

Data is a representation that represents something other than itself

Data is both an explanation of what it represents and what must be explained

People need context or context to make data meaningful. A context can be thought of as a representation system for data that includes a common vocabulary and a set of relationships between components. If the conventions of such a system are known, the data within it can be interpreted.

Even within an organization, there are often multiple ways of representing the same concept. Therefore, data architecture, modeling, quality, management systems, metadata and data quality need to be managed, all of which are beneficial to people's understanding and use of data. When data spans multiple organizations, a variety of issues arise that add to the cost. Therefore, industry-level data standards are needed to improve data consistency

Data is understood as the "raw material" of information, and information becomes "data in context"

pyramid model

Both data and information need to be managed, and these terms are used interchangeably in this book

Today’s organizations rely on data assets to make more efficient decisions and have more efficient operations

Businesses that want to stay competitive must stop making decisions based on gut feelings or feelings and instead use event triggering and applied analytics to gain actionable insights

Being data-driven includes recognizing that data must be managed efficiently and professionally through a partnership of business leadership and technical expertise.

Like other management processes, data management must balance strategic and operational needs

(1) Data is an asset with unique attributes

(2) The value of data can be expressed in economic terms

(3) Managing data means managing the quality of data

(4) Metadata is required to manage data

(5) Data management requires planning

(6) Data management must drive information technology decisions

(7) Data management is a cross-functional job

(8) Data management requires an enterprise-level perspective

(9) Data management requires thinking from multiple perspectives

(10) Data management requires full life cycle management, and different types of data have different life cycle characteristics.

(11) Data management needs to incorporate data-related risks

(12) Effective data management requires leadership to take responsibility

The difference between data and other assets

data value

Data quality

Data optimization plan

Metadata and data management

Data management is a cross-functional effort

Establish a business perspective

Data management requires thinking from multiple perspectives

Data life cycle

different kinds of data

Data and Risk

Data management and technology

Effective data management requires leadership and commitment

Strategy is a set of choices and decisions that together constitute a high-level course of action to achieve high-level goals

Data strategy must come from an understanding of the data needs inherent in the business strategy: what data the organization needs, how to obtain it, how to manage the data and ensure its reliability, and how to leverage the data

The data management strategy is owned and maintained by the CDO and implemented by the data management team supported by the data governance committee

The CDO will draft a preliminary data strategy and data management strategy before forming the data governance committee to gain senior management support for establishing data management and governance

Data management strategy components

Data Management Strategic Planning Deliverables

At the center of the model are the relationships between data and information

The Amsterdam Information Model, like the Strategic Alignment Model, looks at the alignment of business and IT from a strategic perspective

DAMA wheel diagram

environmental factors hexagon

Knowledge domain context diagram

Summarize

The evolution of the DAMA data management framework

Provide guidance and supervision for data management by establishing a data decision-making system that can meet the needs of the enterprise

Defines a blueprint for managing data assets aligned strategically with the organization to establish strategic data needs and an overall design to meet them

Discover, analyze, present and communicate data needs in the precise form of data models

Aiming at maximizing data value, including the design, implementation and support activities of stored data as well as various operational activities throughout the data life cycle, from planning to disposal

Ensure data privacy and confidentiality is protected, data is not compromised, and data is appropriately accessed

Includes processes related to data movement and integration between data stores, applications, and organizations

The life cycle process for managing unstructured media data and information, including planning, implementation and control activities, and in particular the documentation required to support legal and regulatory compliance requirements

Includes ongoing coordination and maintenance of core shared data so that true information about critical business entities is used consistently across systems in an accurate, timely and relevant manner

Includes planning, implementation and control processes to manage decision support data and enable knowledge workers to derive value from data through analytical reporting

Contains planning, implementation, and control activities to enable access to high-quality integrated data, including definitions, models, data flows, and other critical information

Includes planning and implementation of quality management techniques to measure, evaluate and improve the applicability of data within the organization

Describes the core role of data ethics in promoting information transparency and socially responsible decision-making regarding data and its application. Ethical awareness in data collection, analysis and use should guide all data managers

Describes the technologies and business processes that have emerged to improve the ability to collect and analyze large, diverse data and information

Outlines methods for assessing and improving an organization's data management capabilities

Provides practical guidance and reference for building a data management team and achieving successful data management activities

Describes how to plan and successfully drive corporate culture change. Cultural change is the inevitable result of effectively embedding data management practices into the organization

How a particular organization manages its data depends on its goals, size, resources and complexity, as well as its understanding of how the data supports the overall strategy

Most organizations do not perform all the activities described in each knowledge area

However, a broader understanding of the context of data management will help organizations make better decisions about where to focus their efforts, thereby improving management practices within and between these functions.