Subject Areas Flashcards
Data Management
Definition: Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.
Goals:
- 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
Data Ethics
Definition: Data handling ethics are concerned with how to procure, store, manage, interpret, analyze / apply and dispose of data in ways that are aligned with ethical principles, including community responsibility.
Goals:
- To define ethical handling of data in the organization
- To educate staff on the organization risks of improper data handling
- To change/instill preferred culture and behaviors on handling data.
- To monitor regulatory environment, measure, monitor, and adjust organization approaches for ethics in data.
Data Governance
Definition: The exercise of authority, control, and shared decision-making (planning, monitoring, and enforcement) over the management of data assets.
Goals:
- Enable an organization to manage its data as an asset.
- Define, approve, communicate, and implement principles, policies, procedures, metrics, tools, and responsibilities for data management.
- Monitor and guide policy compliance, data usage, and management activities.
Data Architecture
Definition: Identifying the data needs of the enterprise (regardless of structure), and designing and maintaining the master blueprints to meet those needs. Using master blueprints to guide data integration, control data assets, and align data investments with business strategy.
Goals:
- Identify data storage and processing requirements.
- Design structures and plans to meet the current and long-term data requirements of the enterprise.
- Strategically prepare organizations to quickly evolve their products, services, and data to take advantage of business opportunities inherent in emerging technologies.
Data Modeling and Design
Definition: Data modeling is the process of discovering, analyzing, and scoping data requirements, and then representing and communicating these data requirements in a precise form called the data model. This process is iterative and may include a conceptual, logical, and physical model.
Goals:
- To confirm and document an understanding of different perspectives, which leads to applications that more closely align with current and future business requirements, and creates a foundation to successfully complete broad-scoped initiatives such as master data management and data governance programs.
Data Storage and Operations
Definition: The design, implementation, and support of stored data to maximize its value.
Goals:
- Manage availability of data throughout the data lifecycle.
- Ensure the integrity of data assets.
- Manage performance of data transactions.
Data Security
Definition: Definition, planning, development, and execution of security policies and procedures to provide proper authentication, authorization, access, and auditing of data and information assets.
Goals:
- Enable appropriate, and prevent inappropriate, access to enterprise data assets.
- Understand and comply with all relevant regulations and policies for privacy, protection, andconfidentiality.
- Ensure that the privacy and confidentiality needs of all stakeholders are enforced and audited.
Data Integration and Interoperability
Definition: Managing the movement and consolidation of data within and between applications and organizations
Goals:
- Provide data securely, with regulatory compliance, in the format and timeframe needed.
- Lower cost and complexity of managing solutions by developing shared models and interfaces.
- Identify meaningful events and automatically trigger alerts and actions.
- Support business intelligence, analytics, master data management, and operational efficiency efforts.
Document and Content Management
Definition: Planning, implementation, and control activities for lifecycle management of data and information found in any form or medium.
Goals:
- To comply with legal obligations and customer expectations regarding Records management.
- To ensure effective and efficient storage, retrieval, and use of Documents and Content.
- To ensure integration capabilities between structured and unstructured Content.
Reference and Master Data
Definition: Managing shared data to meet organizational goals, reduce risks associated with data redundancy, ensure higher quality, and reduce the costs of data integration.
Goals:
- Enable sharing of information assets across business domains and applications within an organization.
- Provide authoritative source of reconciled and quality-assessed master and reference data.
- Lower cost and complexity through use of standards, common data models, and integration patterns.
Data Warehousing and Business Intelligence
Definition: Planning, implementation, and control processes to provide decision support data and support knowledge workers engaged in reporting, query, and analysis.
Goals:
- To build and maintain the technical environment and technical and business processes needed to deliver integrated data in support of operational functions, compliance requirements, and business intelligence activities.
- To support and enable effective business analysis and decision making by knowledge workers.
Metadata Management
Definition: Planning, Implementation, and control activities to enable access to high quality, integrated metadata
Goals:
- Provide organizational understanding of business terms and usage.
- Collect and integrate metadata from diverse sources.
- Provide a standard way to access metadata.
- Ensure metadata quality and security.
Data Quality
Definition: The planning, implementation, and control of activities that apply quality management techniques to data, in order to assure it is fit for consumption and meets the needs of data consumers.
Goals:
- Develop a governed approach to make data fit for purpose based on data consumers’ requirements.
- Define standards, requirements, and specifications for data quality controls as part of the data lifecycle.
- Define and implement processes to measure, monitor, and report on data quality levels.
- Identify and advocate for opportunities to improve the quality of data, through process and system improvements.
Big Data and Data Science
Definition: The collection (Big Data) and analysis (Data Science, Analytics and Visualization) of many different types of data to find answers and insights for questions that are not known at the start of analysis.
Goals:
- Discover relationships between data and the business.
- Support the iterative integration of data source(s) into the enterprise.
- Discover and analyze new factors that might affect the business.
- Publish data using visualization techniques in an appropriate, trusted, and ethical manner.
