Manajemen Data (2) PTI Pertemuan 6

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

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.

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."

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

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

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

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

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

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 Email address Telephone number

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.

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.

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.

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

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.

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.

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.

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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