CC6 - Chapter 1 = ENUMERATION Flashcards
Data Managemet Principle
- Data is an asset with unique properties
- The value of data can 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; it 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 lifecycle management
- Different types of data have different lifecycle characteristics
- Managing data includes managing the risks associated with data
- Data management requirements must drive Information Technology decisions
- Effective data management requires leadership commitment
Data Management Challenges
- Data differs from other assets
- Data Valuation
- Data Quality
- Planning for Better Data
- Metadata & Data Management
- Data Management is Cross-funtional
- Establishing an Enterprise Perspective
- Accounting for Other Perspective
- The Data Lifecycle
- Different Types of Data
- Data & Risk
- Data Management & Technology
- Effective Data Management Requires Leadership and Commitment
The Data Lifecycle
- Plan
- Design & Enable
- Create / Obtain
- Store / Maintain
- Use
- Enhance
- Dispose of
KNOWLEDGE AREAS
- Data Governance
- Data Architecture
- Data Modeling and Design
- Data Storage and
- Data
- Data Integration and Interoperability
- Document and Content Management
- Reference and Master Data
- Data Warehousing and Business
- Metadata
- Data Quality
Data Management Frameworks
- Strategic Alignment Model
- The Amsterdam Information Model
- The DAMA-DMBOK Framework
- DMBOK Pyramid (Aiken)
- DAMA Data Management Framework Evolved
DMBOK Pyramid (Aiken)
Phase 1:
* Data Integration & Interoperability
* Data Storage & Operations
* Data Security
* Data Modeling & Design
The organization purchases an application that includes database capabilities. This means the organization has a starting point for data modeling / design, data storage, and data security
Phase 2:
* Data Quality
* Metadata
* Data Architecture
Once they start using the application, they will find challenges with the quality of their data. But getting to higher quality data depends on reliable Metadata and consistent Data Architecture.
Phase 3:
* Documents & Content
* Reference & Master Data
* Data Warehousing / Business Intelligence
* Data Governance
Disciplined practices for managing Data Quality, Metadata, and architecture require Data Governance that provides structural support for data management activities.
Phase 4:
* Advanced Practices: Mining, Analytics, Big Data
The organization leverages the benefits of well-managed data and advances its analytic capabilities.
The focus of data management on the data lifecycle has several important implications:
5.9.1. Creation and usage are the most critical points in the data lifecycle -Data management must be executed with an understanding of how data is produced, or obtained, as well a show data is used.
5.9.2. Data Quality must be managed throughout the data lifecycle - Data Quality Management is central to data management. Low quality data represents cost and risk, rather than value.
5.9.3. Metadata Quality must be managed through the data lifecycle - Because Metadata is a form of data, and because organizations rely on it to manage other data, Metadata quality must be managed in the same way as the quality of other data
5.9.4. Data Security must be managed throughout the data lifecycle - Data management also includes ensuring that data is secure and that risks associated with data are mitigated.
5.9.5. Data Security must be managed throughout the data lifecycle - Organizations produce a lot of data, a large portion of which is never actually used.
Data management – goals
- Within an organization, data management goals include:
- Understanding and supporting the information needs of the enterprise and its stakeholders, including customers, employees, and business partners
- Capturing, storing, protecting, and ensuring the integrity of data assets
- Ensuring the quality of data and information
- Ensuring the privacy and confidentiality of stakeholder data
- Preventing unauthorized or inappropriate access, manipulation, or use of data and information
- Ensuring data can be used effectively to add value to the enterprise
Deliverables from strategic planning for data management include:
- A Data Management Charter
- A Data Management Scope Statement:
- A Data Management Implementation Roadmap:
Structure of the DMBOK
The DMBOK is organized around eleven Knowledge Areas, also known as the DAMA Wheel:
- 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 Management
- Data Warehousing and Business Intelligence
- Metadata Management
- Data Quality
DAMA-DMBOK contains chapters on the following topics:
- Data Handling Ethics describes the central role that data ethics plays in making informed, socially responsible decisions about data and its uses. Awareness of the ethics of data collection, analysis, and use should guide all data management professionals.
- Big Data and Data Science describes the technologies and business processes that emerge as our ability to collect and analyze large and diverse data sets increases.
- Data Management Maturity Assessment outlines an approach to evaluating and improving an organization’s data management capabilities.
- Data Management Organization and Role Expectations provide best practices and considerations for organizing data management teams and enabling successful data management practices.
- Data Management and Organizational Change Management describes how to plan for and successfully move through the cultural changes that are necessary to embed effective data management practices within an organization.
The six most commonly used schemes are:
- Relational
- Dimensional
- Object-Oriented
- Fact-Based
- Time-Based
- NoSQL.
Examples of components:
- entities,
- relationships,
- facts,
- keys, and attributes
Models of these schemes exist at three levels of detail:
- conceptual
- logical
- physical
SLDC Methodologies Examples
- Waterfall Model
- Agile Methodology
- Rapid Application Development
1.3.2 Types of Data that are Modeled
- category information
- resource information
- business event information
- detail transaction information
High quality data definitions exhibit three essential characteristics:
Clarity - The definition should be easy to read and grasp.
Accuracy - The definition is a precise and correct description of the entity.
Completeness - All of the parts of the definition are present.
Construction-type keys
- simple key
- surrogate key
- compound key
- composite key
Function-type keys
- super key
- candidate key
- business key
- primary key
- alternate key
Domains can be defined in different ways.
- data type
- data format
- list
- range
- rule-based
The three main types of change are sometimes known by ORC.
-
Overwrite (Type 1): The
new value overwrites the old valuein place. -
New Row (Type 2): The n
ew values are written in a new row, and the old row is marked as not current. -
New Column (Type 3): Multiple instances of a
value are listed in columnson the same row, and a new value means writing the values in the series one spot down to make space at the front for the new value. The last value is discarded.
DATA MODEL SCHEMES
- relational
- dimensional
- object-oriented
- fact-based modeling
- time-based
- no sql
Anchor Modeling Basic Concepts
- anchors
- attributes
- ties
- knots
4 types of NoSQL
- document
- key-value
- column-oriented
- graph
3 components of Data Model
- conceptual
- external
- internal
Normalization levels include:
- First normal form (1NF)
- Second normal form (2NF)
- Third normal form (3NF)
- Boyce/Codd normal form (BCNF)
- Fourth normal form (4NF)
- Fifth normal form (5NF)
