Analyzing User Query Needs,Overview,Project Management (Methodology, Maintaining Metadata),Defining DW Concepts & Terminology,Planning for a Successful Warehouse,Analyzing User Query Needs,Choosing a Computing Architecture,Modeling the Data Warehouse,Planning Warehouse Storage,ETT (Building the Warehouse),Meeting a Business Need,Supporting End User Access,Managing the Data Warehouse,Objectives,After completing this lesson, you should be able to do the following: Identify the warehouse users Identify how to gather user requirements Identify tasks involved with managing query access Identify the different database models that support OLAP query tools Describe query access architectures,Types of Users,Executives Managers Business analysts,User Access,Types of Users Executives Casual users or managers Business analysts or power users,Structured,Unstructured,Gathering User Requirements,Areas to focus: How users do business and what the business drivers are What attributes users need (required versus good to have) What the business hierarchies are What data users use and what they like to have What levels of detail or summary needed What type of front-end data access tool used How users expect to see the query results,Gathering User Requirements: Possible Obstacles,The following are some of the possible obstacles: Business objective of the data warehouse has not been specifically defined Scope of the data warehouse is too broad Misunderstanding about the purpose and function of a decision support systems and operational systems,Data Access Tool Requirements,Simple reports Complex trend analysis Regression analysis Multidimensional data analysis Exceptions reporting Forecasting Data manipulation Data mining Parameterized reports for batch execution Web-based or client-server-based (or both),Data Access Strategy,Define user requirements early Determine the choice of tools early Identify user roles and access requirements,User Query Progression,Starts simple Becomes more analytical Requires different techniques and flexible tools,What?,Why?,Why?,Why?,Training,Methods Informal: one-to-one or small class Formal: larger class Self-study Basic topics Logging on Accessing metadata Creating and submitting a query Interpreting results Saving queries and storing results Utilizing resources Learning warehouse fundamentals,ILT,IDL,CBT,Query Efficiency,User considerations Successful completion Faster query execution Less CPU used More opportunity for further analysis,Query Efficiency,Designer considerations Use indexes Select minimum data Employ resource governors Minimize bottlenecks Develop metrics Use prepared and tested queries Use quiet periods,Charge Models,Examples of charge models: Flat allocation model Transaction-based model Telephone service model Cable TV model Develop your own unique model Avoid a charge model that discourages users from using the warehouse,Query Scheduling and Monitoring,Query scheduling Manages information usage Directs queries Executes queries Sets job queue priorities Query monitoring Track resource-intensive queries Detect unused queries Catch queries that use summary data inefficiently Catch queries that perform regular summary calculations at the time of query execution Detect illegal access,Query Management and Monitoring Tools,Use tools, schedulers, Oracle Enterprise Manager Consider Automation levels Technology interfaces Cost,Security,Do not overlook Subject area sponsors: Review and authorize request for access rights Identify enhancements Transparent security Easy to implement, maintain, and manage,Security Plan,Define a strategy: Allocate business area owners Ensure invisibility Ensure easy management Consider auditing Manage passwords,Role-Based Security,Subject area access: Summary data for new users All data for experienced users Departmental access Limited object access Access during load,Application Context and Fine-Grained Access Control in Oracle8i,Application context,Access policy,Table,Who am I? Where am I?,Comparing OLAP and DSS,OLAP is used for multidimensional analysis. DSS provides a system enabling decision making. OLAP tools provide a DSS capability. OLAP for the warehouse provides analytical power. Other terms: EIS KBS,The Functionality of OLAP,Rotate and drill down to successive levels of detail. Create and examine calculated data interactively on large volumes of data. Determine comparative or relative differences. Perform exception and trend analysis. Perform advanced analytical functions for example forecasting, modeling, and regression analysis,Original OLAP Rules,1. Multidimensional conceptual view 2. Transparency 3. Accessibility 4. Consistent reporting performance 5. Client-server architecture,Original OLAP Rules,6. Generic dimensionality 7. Dynamic sparse matrix handling 8. Multiuser support 9. Unrestricted cross-dimensional operations 10. Intuitive data manipulation 11. Flexible reporting 12. Unlimited dimensions and aggregation levels,