Data Warehouse | Advantages | Components | Uses
Data warehouse is a relational or multidimensional database management system (DBMS) designed to support the management in decision making process.
• Data warehouse is a repository of an organization’s electronically stored data and are designed to facilitate reporting and analysis
• It also emphasizes on the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary.
• An expanded definition for Data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata (data about data).
• In contrast to data, warehouses are operational systems that perform day-to-day transaction processing.
• A data warehouse is a collection of computer-based information that is critical to successful execution of enterprise initiatives
• It provides a tool to satisfy the information needs of the employees at all organizational levels-not just for complex data queries but as a general facility for getting quick, accurate and often insightful information.
• It is designed so that its users can recognize the information they want and access that information using simple tools.
• One of the principal reasons for developing a Data Warehouse is to integrate operational data from various sources into a single and consistent architecture that supports analysis and decision making with the enterprise.
• The data in the ”warehouse” are stored in a single, agreed upon format even when underlying operational applications store the data differently.
• Some of the applications Data warehousing can be used for are:
- Credit card churn analysis
- Insurance fraud analysis
- Call record analysis
- Logistics management : It is the part of Supply Chain Management (SCM) that plans, implements, and controls the efficient, effective, forward, and reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption in order to meet customers requirements.
Advantages of Data Warehouse:
Some of the advantages of Data Warehouse are:• Data warehouse helps in cost effective decision making. A data warehouse allows reduction of staff and computer resources required to support queries and reports against operational and production database. This typically offers significant savings.
• Data warehouse enhance Better enterprise intelligence: Increased quality and flexibility of enterprise analysis arises from the multi-level data structure which guarantees data accuracy and reliability ensuring that a Data Warehouse contains only trusted data.
• Enhanced customer service: An enterprise can maintain better customer relationships by correlating all customer data via a single Data Warehouse Architecture.
• Business reengineering: Allowing unlimited analysis of enterprise information often provides insights to enterprise processes that may yield breakthrough ideas for engineering those processes. Knowing what information is important to an enterprise will provide direction and priority for reengineering efforts.
• 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 is purged (washed out) 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 facilitate decision support system applications such as trend reports, exception reports,and reports that show actual performance versus goals. ?
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Components of Data Warehouse:
A. Summarized Data:
• Classified into two categories:
- lightly summarized
- highly summarized
• Lightly summarized data are the hallmark (trademark) of data warehouse as all enterprise elements do not have the same information requirement. They include less data than the total data stored in current detail.
• Highly summarized data are primarily for enterprise executives. They come from either the lightly summarized data used by enterprise elements or from current detail. Data volume at this level is much less than other levels.
B. Current Detail:
It is the heart of the data warehouse where the whole bulk of data resides. It comes directly from operational systems of records and may be stored as raw data or as aggregations of raw data.
C. Operational System of Record:
It is a source of the data that feeds the Data Warehouse.
D. Integration/Transformation Programs:
As operational data items pass from their systems of record to a data warehouse, integration and transformation programs convert them from application-specific data into enterprise data.
E. Archives:
It Contains old data (normally over two years old) of significant, continuing interest and value to the enterprise.
F. Metadata:
Metadata is ”data about other data”, of any sort in any media. It is definitional data that provides information about or documentation of other data managed within an application or environment.
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Uses of Data Warehouse:
A. Standard Reports and Queries:Many users of the data warehouse need to access a set of standard reports and queries and hence it is desirable to periodically produce a set of standard reports that are required by many different users. When these users need a particular report, they can just view the report that has already run the data warehouse system rather than running it themselves. This facility can be particularly useful for reports that take a long time to run.
B. Queries Against Summarized Data:
The summary views in the data warehouse can be object of a large majority of analysis in a data warehouse. These views contain predefined standard business analysis.
C. Data Mining Data mining is the process of extracting hidden patterns from data. As more data are gathered, with the amount of data doubling every year, data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, fraud detection and scientific discovery. Data mining can be applied to data sets of any size.
Data mining has five main functions:
• Classification : infers the defining characteristics of a certain group.
• Clustering: identifies groups of items that share a particular characteristics.
• Association: identifies relationships between events that occur at one time.
• Sequencing: similar to association, except that the relationship exists over a period of time.
• Forecasting: estimates future values based on patterns within large sets of data.
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