CC6 - Chapter 1 = NOTES Flashcards
manages computer databases. The role may include capacity planning, installation, configuration, database design, migration, performance monitoring, security, troubleshooting, as well as backup and data recovery.
Database administrator (DBA)
is responsible for maintaining an organization’s computer networks, including hardware and software. They ensure that networks are secure, efficient, and reliable.
network administrator
are data governance employees who collect and maintain data for the organizations they work for while also protecting their data assets.
Data stewards
is a leader who uses data to help a company make strategic decisions. They are responsible for integrating data from various sources to create a unified view.
data strategist
is a senior executive who manages a company’s data strategy and use. They are responsible for ensuring that data is used effectively to support business decisions.
Chief Data Officer (CDO)
- Ensure that data is accurate and of good quality
Data quality
- Protect data from unauthorized access, theft, or corruption
Data security
- Manage data governance strategies, practices, and requirements
Data governance
- Lead the development of a data strategy that aligns with business objectives
Data strategy
- Implement data analytics into business processes
Data analytics
- Promote data literacy and a data-driven culture
Data literacy
- Ensure compliance with data protection and privacy regulations
Regulatory compliance
Some examples of basic metadata are:
- author
- date created
- date modified
- file size.
is also used for unstructured data such as images, video, web pages, spreadsheets, etc. Web pages often include metadata in the form of meta tags.
Metadata
: Identify key business goals that data can support and prioritize data needs aligned with strategic initiatives.
Define Business Objectives
: Conduct a comprehensive data audit to identify all data sources, their formats, locations, quality, and usage across the organization.
Data Inventory and Mapping
: Assess data accuracy, completeness, consistency, and relevance to identify areas for improvement.
Data Quality Analysis
: Identify key stakeholders, their data requirements, and establish communication channels.
Stakeholder Engagement
: Develop clear guidelines for data ownership, access control, data quality standards, retention policies, and privacy compliance.
Establish Data Governance Policies
: Assign data stewards, data owners, and data custodians with defined accountability for data management.
Data Governance Roles and Responsibilities
: Implement processes to monitor and improve data quality through data cleansing, validation, and standardization.
Data Quality Management Plan
: Determine which data sources are critical for integration and prioritize based on business needs.
Data Source Selection
: Map data elements from different sources to a unified schema and transform data to ensure consistency.
Data Mapping and Transformation
: Select appropriate data integration tools to extract, transform, and load (ETL) data from disparate sources.
Data Integration Tools
: Choose appropriate data storage architecture (relational, dimensional, cloud-based) to facilitate analysis and reporting.
Data Warehouse/Lake Design
: Implement robust data security controls including encryption, access controls, and data masking to protect sensitive information.
Data Security Measures
: Establish a reliable data backup and disaster recovery plan to mitigate data loss risks.
Data Backup and Recovery Strategy
: Choose appropriate BI tools to visualize and analyze data for decision-making.
Business Intelligence (BI) Tool Selection
: Create customized dashboards and reports aligned with key business metrics to provide actionable insights.
Dashboard Development
: Develop data models to enable efficient querying and analysis of data across different dimensions.
Data Modeling and Analysis
: Regularly monitor data quality metrics to identify and address data quality issues proactively.
Data Quality Monitoring
: Track key performance indicators (KPIs) related to data management to assess the effectiveness of implemented strategies.
Performance Evaluation
: Review and update the data management strategy as business needs evolve and new technologies emerge.
Adapting to Change
: Ensure strong support from leadership and involve key stakeholders throughout the implementation process.
Organizational Alignment
: Communicate changes effectively and provide training to users to facilitate adoption of new data management practices.
Change Management
: Adhere to relevant data privacy regulations (e.g., GDPR, CCPA) when managing sensitive data.
Compliance Requirements
describe the purpose the Knowledge Area and the fundamental principles that guide performance of activities within each Knowledge Area.
Goals
are the actions and tasks required to meet the goals of the Knowledge Area. Some activities are described in terms of sub-activities, tasks, and steps.
Activities
- set the strategic and tactical course for meeting data management goals. it s occur on a recurring basis.
(P)Planning Activities
- are organized around the system development lifecycle (SDLC) (analysis, design, build, test, preparation, and deployment).
(D)Development Activities
- ensure the ongoing quality of data and the integrity, reliability, and security of systems through which data is accessed and used.
(C) Control Activities
- support the use, maintenance, and enhancement of systems and processes through which data is accessed and used.
(O)Operational Activities
are the tangible things that each Knowledge Area requires to initiate its activities. Many activities require the same inputs. For example, many require knowledge of the Business Strategy as input.
Inputs
are the outputs of the activities within the Knowledge Area, the tangible things that each function is responsible for producing. Deliverables may be ends in themselves or inputs into other activities. Several primary deliverables are created by multiple functions.
Deliverables
describe how individuals and teams contribute to activities within the Knowledge Area. Roles are described conceptually, with a focus on groups of roles required in most organizations. Roles for individuals are defined in terms of skills and qualification requirements. Skills Framework for the Information Age (SFIA) was used to help align role titles. Many roles will be cross-functional.
Roles and Responsibilities
are the people responsible for providing or enabling access to inputs for the activities.
Suppliers
those that directly benefit from the primary deliverables created by the data management activities.
Consumers
are the people that perform, manage the performance of, or approve the activities in the Knowledge Area.
Participants
are the applications and other technologies that enable the goals of the Knowledge Area.
Tools
are the methods and procedures used to perform activities and produce deliverables within a Knowledge Area. Techniques include common conventions, best practice recommendations, standards and protocols, and, where applicable, emerging alternative approaches.
Techniques
are standards for measurement or evaluation of performance, progress, quality, efficiency, or other effect. The metrics sections identify measurable facets of the work that is done within each Knowledge Area. Metrics may also measure more abstract characteristics, like improvement or value
Metrics
