| 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 ● Policies on data and metadata management, access, usage, security, and quality ● Data quality and data architecture standards ● Data stewardship to provide hands-on observation, audit, and improvement of data practices ● Data-related regulatory compliance requirements ● Issue management ● 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 ● 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:
● Conceptual data model ● Physical data model ● Logical data model | ●Provides a common vocabulary around data ● Documents corporate memory about data assets ● Serves as a primary communications tool during projects ● 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:
● Defining database tech characteristics ● Setting storage, usage, resiliency, and access requirements ● Developing storage containers ● Managing database access controls, database performance, and data migration | ●Enables data availability throughout the lifecycle
● 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 ● 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:
● Integration consolidating data into consistent physical or virtual forms ● Data interoperability enabling multiple systems to communicate | ● Makes data available as and when needed by data users ● Consolidates data into data hubs and marts ● Lowers cost and complexity of managing solutions by developing shared models and interfaces ● Identifies meaningful events (opportunities and threats) and automatically triggers alerts and actions ● 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 ● Ensures integration capabilities between structured and unstructured data ● 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 ● Reduces risk of data inconsistencies, quality issues, and gaps ● Lowers the costs of integrating new data sources into an already complex environment ● 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:
● Integrated decision support database ● Related software programs | ●
Supports operational functions ● Ensures regulatory compliance ● Enables BI activities |
| 10. Metadata Management | Control activities to enable access to high quality, integrated metadata that describes: ● Data itself (databases, data elements, data models) ● Concepts represented by data (business processes, application systems, software code, tech infrastructure) ● Connections between the data and concepts | ● Increases confidence in data by enabling data quality measurement ● Increases the value of master data by enabling multiple uses ● Improves operational efficiency by identifying redundant data and processes ● Prevents the use of out-of-date or incorrect data ● Reduces data-oriented research time ● Improves communication between data users and IT people ● Improves time-to-market by reducing the system development time ● Reduces training costs through thorough documentation of data context, history, and origin ● Supports regulatory compliance |
