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Industry experience dictates a successful strategy is to use a bottom-up methodology that builds the data warehouse incrementally, one business unit at a time The bottom-up development methodology may be used to build a data mart for a specified business area, such as finance, sales, marketing, and so on, within a 90day time box The bottom-up approach uses Rapid Application Development (RAD) techniques, rather than top-down Information Engineering techniques Although the first phase of the development effort is focused on building a single data mart, the data mart is embedded with long-term enterprise data warehousing architecture that is specified in an early phase of the development methodology The assumption is that the long-term DW architecture will be implemented incrementally, one business unit at a time The development of more complex components of the architecture, such as a central data warehouse and an ODS, are deferred until later stages of the development effort The incremental development effort is kept under control through use of logical data-modeling techniques (Entity Relationship diagrams (ERD) that gradually expand to an enterprise model), and integration of all components of the architecture with central metadata, generated and maintained by the ETL tool

41'4

Data Integration Platform Warehouse Admin Tools Extract, Transform and Load End-user Tools Managed Query Tools RDBMS Desktop OLAP Tools ROLAP Tools Central Metadata External Metadata Exchange Data Modeling Tool Data Extraction, Transformation and Load Architected Data Marts Data Access and Analysis

$35,000 575% = $201250 $55,000 475% = $261250 $55,000 575% = $316250 $316250 $261250 = $550 savings

The next step is to implement additional architected data marts to the existing Phase 1 data mart Each data mart reads metadata from the central metadata repository, for example: Multiple source databases: DB2, RDBMS, VSM, and so on Multiple target databases: RDBMS and/or multidimensional databases Multiple data marts supporting individual business areas Each architected data mart integrates with the central repository Data from multiple sources that has been extracted, transformed, and loaded into multiple target databases

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The architecture utilizes a more complex, integrated data warehousing framework, including a data extraction tool, a data modeling tool, a metadata repository, and a data warehouse administration tool

This supports a realistic pilot project with important learning experiences, especially if taken into account that not many vendors of data marts or data access tools provide a mechanism to integrate metadata with central metadata, and this opens the door for building stovepipe data marts

Mutual fund families also offer rights of accumulation to those who invest in Class A shares Rights of accumulation come into play when you have already purchased some Class A shares and want to make a subsequent purchase Going back to our previous example, you have purchased $35,000 of XYZ Growth Fund Class A and wish to make another purchase of $35,000 Rather than be charged 575 percent on the second purchase of $35,000, you would be charged 575 percent on the first $15,000 and then 475 percent on the remaining $20,000 Your total sales load for the second purchase of $35,000 would be $181250, rather than $201250 (saving you $200) This occurs every time an investor makes subsequent purchases of his Class A shares

Data Integration Platform Warehouse Admin Tools Extract, Transform and Load End-user Tools Managed Query Tools RDBMS Desktop OLAP Tools ROLAP Tools MOLAP Tools Data Cleansing Tool Source Databases and Files Data Modeling Tool Data Extraction, Transformation and Load Data Mining Tools Data Access and Analysis

At this point your organization has adapted to the capabilities of the BI environment and is demanding more and more The power of information is now driving requirements and pushing the DW to move into the next phase This type of environment is commonly referred to as hub-and-spoke enterprise DW architecture Extensions of the DW to enterprise architecture require implementation of a central data warehouse that stores detailed (atomic) data, that is, individual, lowlevel transactions The hub-and-spoke mode consists of a large central DW driving multiple data marts and often has multiple sources The central DW also supports organizational, consolidated analysis and reporting information Note that at this point in your DW implementation, the complexity of the environment will be driving higher development cost and risk The end users are getting smarter and will be driving demands for access to the detail (atomic) data to review detail transactions, rather than summary data

5/22/62 6/2/62 7/12/62 7/24/62 7/24/62 7/19/62 9/28/62 1/15/63

Original sales load $35,000 575% = $201250 $15,000 575% = $86250 20,000 475% = $950 Total sales load for second $35,000 investment = $181250 $201250 $181250 = $200 savings

Appl Package Central Metadata External Local Metadata Data Modeling Tool Data Extraction, Transformation and Load Local Metadata Central Data Warehouse

Following are the 12 steps for developing a data warehouse: 1 Identify business drivers, sponsorship, risks, and return on investment (ROI) 2 Survey user needs, and identify desired functionality 3 Design long-term, enterprise data warehousing architecture on paper 4 Define functional requirements for initial subject area 5 Research and select decision support system (DSS) components and tools 6 Design target database 7 Build data mappings for extractions, transformations, and loading, with scrubbing rules and error handling included 8 Build aggregation, summarization, partition, and distribution as per the requirements

9 Build an initial architected data mart, using an exact subset of enterprise DW architecture 10 Build additional architected data marts 11 Expand to enterprise architecture, including central data warehouse and optional ODS 12 Maintain and administer the data warehouse

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