1. Manajemen Data (2)
PTI Pertemuan 6

2. Today’s Lessons Data Management Data Warehouse Definition Topics
Architecture
Benefit
Disadvantages

3. Data Management Definition
The official definition provided by Data Management Association (DAMA) : "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise." This definition is fairly broad and encompasses a number of professions which may not have direct technical contact with lower-level aspects of data management, such as relational database management.

4. Alternatively, the definition provided in the DAMA Data Management Body of Knowledge (DAMA-DMBOK) is: "Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets."

5. Topics Data Governance Data Architecture, Analysis and Design
Data asset
Data governance
Data steward
Data Architecture, Analysis and Design
Data analysis
Data architecture
Data modeling

6. Data Security Management
Database Management
Data maintenance
Database administration
Database management system
Data Security Management
Data access
Data erasure
Data privacy
Data security

7. Data Quality Management
Data cleansing
Data integrity
Data quality
Data quality assurance
Reference and Master Data Management
Data integration
Master Data Management
Reference data

8. Data Warehousing and Business Intelligence Management
Data mart
Data mining
Data movement (extract, transform and load)
Data warehousing
Document, Record and Content Management
Document management system
Records management

9. Meta Data Management Meta-data management Metadata Metadata discovery
Metadata publishing
Metadata registry

10. Contact Data Management
Business continuity planning
Marketing operations
Customer data integration
Identity management
Identity theft
Data theft
ERP software
CRM software
Address (geography)
Postal code
address
Telephone number

11. Data Warehouse Definition
A data warehouse is a repository (collection of resources that can be accessed to retrieve information) of an organization's electronically stored data, designed to facilitate reporting and analysis. More simply, a data warehouse is a collection of a large amount of data.

12. Architecture
Architecture, in the context of an organization's data warehousing efforts, is a conceptualization of how the data warehouse is built. There is no right or wrong architecture, but rather there are multiple architectures that exist to support various environments and situations. The worthiness of the architecture can be judged from how the conceptualization aids in the building, maintenance, and usage of the data warehouse.

13. Operational database layer
One possible simple conceptualization of a data warehouse architecture consists of the following interconnected layers:
Operational database layer
The source data for the data warehouse — An organization's Enterprise Resource Planning systems fall into this layer.
Data access layer
The interface between the operational and informational access layer — Tools to extract, transform, load data into the warehouse fall into this layer.

14. Informational access layer
Metadata layer
The data directory — This is usually more detailed than an operational system data directory. There are dictionaries for the entire warehouse and sometimes dictionaries for the data that can be accessed by a particular reporting and analysis tool.
Informational access layer
The data accessed for reporting and analyzing and the tools for reporting and analyzing data — Business intelligence tools fall into this layer. The Inmon-Kimball differences about design methodology, discussed later in this article, have to do with this layer

15. Benefit
A data warehouse provides a common data model for all data of interest regardless of the data's source. This makes it easier to report and analyze information than it would be if multiple data models were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.
Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.
Information in the data warehouse is under the control of data warehouse users so that, even if the source system data are purged over time, the information in the warehouse can be stored safely for extended periods of time.

16. Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.
Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.
Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.

17. Disadvantages
Data warehouses are not the optimal environment for unstructured data.
Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency in data warehouse data.
Over their life, data warehouses can have high costs.

18. Data warehouses can get outdated relatively quickly
Data warehouses can get outdated relatively quickly. There is a cost of delivering suboptimal information to the organization.
There is often a fine line between data warehouses and operational systems. Duplicate, expensive functionality may be developed. Or, functionality may be developed in the data warehouse that, in retrospect, should have been developed in the operational systems.

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