Data warehouse legacy systems-data marts-marketing database

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    05-Dec-2014

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<ul><li> 1. DATA WAREHOUSE- LEGACY SYSTEMS-DATA MARTS-MARKETING DATABASE By Davin Abraham 1701310002 M.tech/DB/SRM </li> <li> 2. 12 rules of a Datawarehouse Data Warehouse and Operational Environments are Separated Data is integrated Contains historical data over a long period of time Data is a snapshot data captured at a given point in time Data is subject-oriented </li> <li> 3. 12 rules of a Datawarehouse Mainly read-only with periodic batch updates Development Life Cycle has a data driven approach versus the traditional process-driven approach Data contains several levels of detail Current, Old, Lightly Summarized, Highly Summarized </li> <li> 4. 12 rules of a Datawarehouse Environment is characterized by Read-only transactions to very large data sets System that traces data sources, transformations, and storage Metadata is a critical component Source, transformation, integration, storage, relationships, history, etc Contains a chargeback mechanism for resource usage that enforces optimal use of data by end users </li> <li> 5. Life cycle of the DW Warehouse Database First time load Refresh Refresh Refresh Purge or Archive </li> <li> 6. 1001 1007 1010 1020 Relational Database Model 31 42 22 32 F M M F Anderson Green Lee Ramos Attribute 1 Name Attribute 2 Age Attribute 3 Gender Row 1 Row 2 Row 3 Row 4 The table above illustrates the employee relation. Attribute 4 Emp No. </li> <li> 7. Multidimensional Database Model The data is found at the intersection of dimensions. Store GL_Line Time FINANCE Store Product Time SALES Customer </li> <li> 8. Two dimensions </li> <li> 9. Three dimensions </li> <li> 10. Data marts Small Data Stores More manageable data sets Targeted to meet the needs of small groups within the organization Small, Single-Subject data warehouse subset that provides decision support to a small group of people </li> <li> 11. Data Mart A subset of a data warehouse that supports the requirements of a particular department or business function. Characteristics include: Do not normally contain detailed operational data unlike data warehouses. May contain certain levels of aggregation </li> <li> 12. Independent Data Mart Sales or Marketing External Data Flat FilesOperational Systems </li> <li> 13. Reasons For Creating a Data Mart To give users more flexible access to the data they need to analyse most often. To provide data in a form that matches the collective view of a group of users To improve end-user response time. Potential users of a data mart are clearly defined and can be targeted for support </li> <li> 14. To provide appropriately structured data as dictated by the requirements of the end-user access tools. Building a data mart is simpler compared with establishing a corporate data warehouse. The cost of implementing data marts is far less than that required to establish a data warehouse. </li> <li> 15. Legacy Systems Older software systems that remain vital to an organisation The legacy Dilemma it is expensive and risky to replace the legacy system It is expensive to maintain the legacy system Businesses must weigh up the costs and risks and may choose to extend the system lifetime using techniques such as re-engineering. </li> <li> 16. The system may be file-based with incompatible files. The change required may be to move to a database-management system In legacy systems that use a DBMS the database management system may be obsolete and incompatible with other DBMSs used by the business </li> <li> 17. Legacy System Design Most legacy systems were designed before object-oriented development was used Rather than being organised as a set of interacting objects, these systems have been designed using a function-oriented design strategy Several methods and CASE tools are available to support function-oriented design and the approach is still used for many business applications </li> <li> 18. Legacy system categories Low quality, low business value These systems should be scrapped Low-quality, high-business value These make an important business contribution but are expensive to maintain. Should be re-engineered or replaced if a suitable system is available High-quality, low-business value Replace with COTS, scrap completely or maintain High-quality, high business value Continue in operation using normal system maintenance </li> <li> 19. Legacy System Evolution The structure of legacy business systems normally follows an input-process-output model The business value of a system and its quality should be used to choose an evolution strategy The business value reflects the systems effectiveness in supporting business goals System quality depends on business processes, the systems environment and the application software </li> <li> 20. Marketing Database is a systematic approach to the gathering, consolidation, and processing of consumer data (both for customers and potential customers) that is maintained in a company's databases. Although databases have been used for customer data in traditional marketing for a long time, the database marketing approach is differentiated by the fact that much more consumer data is maintained, and that the data is processed and used in new and more sophisticated ways. Among other things, marketers use the data to learn more about customers, select target markets for specific campaigns (through customer segmentation), compare customers' value to the company and provide more specialized offerings for customers. </li> <li> 21. Need for a Marketing Database Emails sent based on email response alone, not on overall purchases Gold customers are seldom recognized Long time customers treated as strangers Customers feel unappreciated You may lose your best supporters </li> <li> 22. What You Can Do with a Marketing DB? Store behavior and append demographic data Create customer segments, and develop a marketing plan for each segment. Personalize all your email communications to customers to build loyalty and sales Append demographic data Determine customer lifetime value. </li> <li> 23. Thank You </li> </ul>