Data management component | What it is | Why it is needed |
1. Data Governance | A program that exercises control over the management of the organization's data assets and includes:
● A strategy defining the data governance approach
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Policies on data and metadata management, access, usage, security, and quality
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Data quality and data architecture standards
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Data stewardship to provide hands-on observation, audit, and improvement of data practices
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Data-related regulatory compliance requirements
● Issue management
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Standards for data asset valuation | ●Reduces risk by managing data security, privacy, and the general risk data poses to finances or reputation
●Improves response to regulatory requirements
●Enables data quality
metadata management, through business glossary establishment and metadata availability
●Enhances efficiency in data projects
●Establishes control of data-related contracts (cloud storage, external data acquisition, sales of data, etc.) |
2. Data Architecture | A structure of the company’s current data assets and data management resources that maps how data flows through systems. | ● Describes the current state of data assets
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Defines data requirements
● Guides data integration
●Enables easier evolution of products, services, and data for new business opportunities |
3. Data Modelling | A process to discover how the company’s data fits together and communicate the data structures and relationships in:
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Conceptual data model
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Physical data model
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Logical data model | ●Provides a common vocabulary around data
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Documents corporate memory about data assets
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Serves as a primary communications tool during projects
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Provides the starting point for application customization, integration, or replacement |
4. Data Storage & Operation | The design, implementation, and support of stored data to maximize its value. Key activities include:
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Defining database tech characteristics
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Setting storage, usage, resiliency, and access requirements
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Developing storage containers
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Managing database access controls, database performance, and data migration | ●Enables data availability throughout the lifecycle
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Ensures data integrity
● Improves efficiency of data transactions |
5. Data Security & Privacy | Security policies and procedures aimed at ensuring proper authentication, authorization, access, and auditing of data assets | ●Reduces risks by protecting data assets in line with privacy regulations, contractual agreements, and business requirements
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Drives business growth by preventing security issues that can impact operational success |
6. Data Integration & Operability | The movement and consolidation of data within and between data stores and applications, with:
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Integration consolidating data into consistent physical or virtual forms
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Data interoperability enabling multiple systems to communicate | ● Makes data available as and when needed by data users
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Consolidates data into data hubs and marts
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Lowers cost and complexity of managing solutions by developing shared models and interfaces
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Identifies meaningful events (opportunities and threats) and automatically triggers alerts and actions
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Supports BI, analytics, master data management |
7. Document Management | Controlling the capture, storage, access, and use of data stored outside relational databases | ● Ensures efficient retrieval and use of data in unstructured formats
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Ensures integration capabilities between structured and unstructured data
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Improves compliance with legal obligations and customer expectations |
8. Master Data Management | ● Controlling master data (key business data about customers, products, suppliers, locations, etc.) to:
● Enable availability of accurate and current values of master data
● Reduce risk of ambiguous master data identifiers that refer to more than one instance of an entity or to more than one entity | ● Provides users access to the same complete and current data sets, which are based on master data
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Reduces risk of data inconsistencies, quality issues, and gaps
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Lowers the costs of integrating new data sources into an already complex environment
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Simplifies data sharing architecture to reduce costs and risk associated with complex environments |
9. Data Warehousing and BI | Operational extract, cleansing, transformation, control, and load processes that maintain the data in a data warehouse consisting of:
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Integrated decision support database
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Related software programs | ●
Supports operational functions
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Ensures regulatory compliance
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Enables BI activities |
10. Metadata Management | Control activities to enable access to high quality, integrated metadata that describes:
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Data itself (databases, data elements, data models)
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Concepts represented by data (business processes, application systems, software code, tech infrastructure)
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Connections between the data and concepts | ● Increases confidence in data by enabling data quality measurement
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Increases the value of master data by enabling multiple uses
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Improves operational efficiency by identifying redundant data and processes
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Prevents the use of out-of-date or incorrect data
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Reduces data-oriented research time
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Improves communication between data users and IT people
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Improves time-to-market by reducing the system development time
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Reduces training costs through thorough documentation of data context, history, and origin
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Supports regulatory compliance |