Introduction to Data Management

This is the overview of what will be studied in Data Management Course

Data management encompasses a structured approach for developing and implementing detailed policies to handle data within an organization. It includes frameworks, standards, projects, teams, and maturity models that ensure the proper handling, security, and utilization of data. Since information is a core component of any IT system, understanding how to manage data effectively is crucial for organizational success.

Nature of Data

• Data is the fuel for applications, enabling them to function.
• Applications exist primarily to process data supplied by users, devices, or other systems.
• Infrastructure (hardware and software) is responsible for storing and managing data.
• Data is a key organizational asset. Its value lies in its ability to provide insights, enable decision-making, and drive business processes.
• Organizations have significant responsibilities to manage and protect their data.

Data, Information, & Knowledge

Data, Information, Knowledge

Data

• Data represents raw facts in various forms, such as text, numbers, graphics, images, sound, or video.
• It is the raw material for creating information but lacks inherent meaning without context.

Information

• Information is the interpretation of data in context, making it understandable and useful.
• By organizing data around context, we give it meaning.

Knowledge

• Knowledge is derived from information that has been integrated into a broader perspective, recognizing patterns, trends, and relationships.
• Knowledge enables understanding, which in turn leads to more effective actions and decisions.

Managing the Data Paradigm

• Between 1950 and 2002, only 5 billion gigabytes of digital data were created.
• By 2011, this same volume of data was created every two days, and by 2013, it was being generated every ten minutes.
• The exponential growth of data has made traditional tools and techniques inadequate for managing modern data volumes, requiring new approaches and technologies.

Rise of Data-Centric Organizations

In modern business, the use of data has become a core driver of success. Organizations are increasingly data-centric, making data the foundation for strategic decision-making, much like how hardware and software have historically been the key elements of IT.

• The importance of data now rivals traditional physical assets.
• Data is crucial for operations, driving business decisions, and enabling automation and innovation.

Information as an Organizational Asset

• Organizations manage their tangible assets using inventory and asset management systems.
• Data, though less tangible, is equally valuable but is often not treated with the same level of rigor in terms of management and valuation.
• High-quality, accurate, and accessible information is essential for efficient operations.

Generalized Information Management Lifecycle

Generalized Management Information Lifecycle

To properly manage data, organizations follow a generalized lifecycle that encompasses several stages:

1. Enter, Create, Acquire, Derive, Update, Capture – This phase involves generating or capturing data from various sources.
2. Store, Manage, Replicate, Distribute – Data is stored, managed, and made available across the organization.
3. Protect and Recover – Safeguarding data from loss or breaches and ensuring the ability to recover data when needed.
4. Archive and Recall – Long-term storage of data, ensuring it can be retrieved when necessary.
5. Delete/Remove – Properly disposing of data when it is no longer needed or relevant.

Expanded Information Management Lifecycle

The lifecycle can also include phases for the planning, design, and implementation of the infrastructure required to support information management.

• This expanded lifecycle includes:
  • Plan, Design, Specify – Designing the infrastructure and processes required to manage the data lifecycle.
  • Implement Underlying Infrastructure – Executing the design by setting up the necessary hardware and software.

Data and Information Management

Data management is the business process that plans and executes policies, practices, and projects for handling data assets. This process includes:

• Acquisition – Gathering data from various sources.
• Control – Ensuring the integrity and security of data.
• Protection – Safeguarding data from unauthorized access and breaches.
• Delivery – Making the right data available to the right people at the right time.
• Enhancement – Continuously improving the quality and value of data.

Data Management Goals

Primary Goals

• Understand enterprise information needs: Know what information is needed by the organization and its stakeholders.
• Capture, store, and protect data: Ensure the integrity and security of data throughout its lifecycle.
• Improve data quality: Continuously enhance the accuracy, relevance, integration, and usefulness of data.
• Ensure privacy and confidentiality: Prevent unauthorized or inappropriate use of data and safeguard sensitive information.
• Maximize data value: Ensure that data is used effectively to drive decision-making and create business value.

Secondary Goals

• Control data management costs: Ensure that data management efforts are cost-effective.
• Promote the value of data assets: Help stakeholders recognize and appreciate the strategic importance of data assets.
• Manage data consistently: Ensure that data is handled in a consistent and standardized manner across the organization.
• Align data efforts with business needs: Ensure that data management initiatives support broader business objectives.

Data Management Principles

• Data and information are valuable enterprise assets: They must be treated with the same level of care as other important business resources.
• Shared responsibility: Data management is a joint responsibility between business data owners and IT professionals.
• Data management is a business function: It encompasses a set of disciplines that ensure the effective use, protection, and management of data assets.

Data Management Functions

The organizational data management function involves the development, execution, and supervision of:

• Plans – Strategies for managing data.
• Policies – Guidelines for ensuring data integrity and security.
• Programs and Projects – Specific initiatives aimed at enhancing data management.
• Processes and Practices – Day-to-day activities that ensure the smooth operation of data management functions.

Data Management Challenges

Some common data management challenges include:

• Discovery: Difficulty in finding the right data.
• Integration: Challenges in combining data from different sources.
• Insight: Extracting value and insights from data.
• Dissemination: Making data available to those who need it.
• Management: Handling large volumes of data efficiently.

Data Management Frameworks

Several frameworks exist to guide organizations in managing data:

• TOGAF: Provides a high-level process for creating a data architecture within an overall enterprise architecture.
• DMBOK: A detailed framework focused on managing data throughout its lifecycle.
• COBIT: Provides guidelines for IT governance, including controls for data management processes.

DMBOK (Data Management Body of Knowledge)

Developed by the Data Management Association (DAMA), DMBOK is a comprehensive framework for managing data across its entire lifecycle. It includes:

• Goals and Principles: Fundamental concepts guiding data management functions.
• Activities: Specific actions taken within each data management function.
• Roles and Responsibilities: Who is responsible for performing each activity.
• Techniques and Tools: Common methods and software used to manage data effectively.

DMBOK Knowledge Areas

DMBOK defines 11 key Knowledge Areas that provide a comprehensive guide for managing data:

• Data Governance: Establishing the rules and policies that govern data management.
• Data Architecture: Defining the overall structure of data within the organization.
• Data Modeling & Design: Creating precise models that represent data requirements.
• Data Storage & Operations: Managing the storage, retrieval, and operation of data systems.
• Data Security: Protecting data from breaches and unauthorized access.
• Data Integration & Interoperability: Ensuring data flows smoothly across different systems.
• Data Quality: Continuously assessing and improving the quality of data.
• Reference and Master Data: Managing key organizational data sets like customer records.
• Data Warehousing & Business Intelligence: Enabling reporting and analysis.
• Document & Content Management: Handling unstructured data like documents and multimedia.
• Metadata Management: Organizing and providing context for data.

These knowledge areas help organizations structure their data management practices to ensure they are aligned with strategic goals and operational needs.