Presentation on theme: "1 Components of A Successful Data Warehouse Chris Wheaton, Co-Founder, Client Advocate."— Presentation transcript:
1 Components of A Successful Data Warehouse Chris Wheaton, Co-Founder, Client Advocate
2 Presentation Information Presentation: Successful Components of a Data Warehouse –The purpose of this presentation is to provide attendees with the understanding of how to build a successful data warehouse/business intelligence solution.
3 Presentation Information Author: Chris Wheaton –Biography: Chris Wheaton is a Co-Founder of BASE Consulting Group, Inc. He initiated and has contributed to the development of the Business Intelligence and Data Warehousing Certificate Program at the University of California, Berkeley Extension and is a lecturer in the program. He has presented on Data Warehousing topics at conferences throughout the United States including the Business Objects and Oracle Applications User Conferences. –Contact Information: E-mail: email@example.com Phone: (510) 628-3300 Ext. 223
4 Agenda Data Warehouse ? What is it? Why do it? Why do they fail? How do you control business risk? How do you control technical risk?
5 Many names for the same thing Decision Support System (DSS) Executive Information System Management Information System Business Intelligence Solution Analytic Application Data Warehouse Six terms for the same thing: a system for helping companies get the information they want, when they want it…etc. Data Warehouse? What is it?
6 Data Warehouse Objectives - Business Access to specific high-value information on a timely basis. Analysis Makes the restructured data available to users via user- friendlier query and reporting tools. Reporting - Technical Gets the data off of the transaction system for analysis. Performance Restructures and integrates the data so that it is easier to use for reporting and analysis. Integration Data Warehouse? What is it?
7 Data Warehouse Risks Business Content –Does the solution answer the right questions? –Does the solution have enough data? User Acceptance –Is it too complex for the average user? –Is the data timely enough? Technical Performance –Is the performance of the user queries satisfactory? –Can data be loaded to the data warehouse within the allotted timeframe? Integration –How do we combine information from multiple systems? Why do they fail?
8 Controlling Business Risk We have found that the best way to address the business risks associated with a data warehouse project is to employ a methodology with the following components: –Enterprise Strategy –Phased Delivery –Iterative Prototyping How do you control the risks?
10 Enterprise Strategy Asses technical landscape Identify business drivers Define analytical processes to be supported Identify major facts, dimensions and attributes Map and gap to data sources Assess current architecture and tools Recommend subject area phasing and tool selection How do you control the risks?
11 Phased Subject Areas How do you control the risks? ____________________________ 123 7 Conceptual Architecture SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Phase 1 Prototype 1 Phase 1 Requirements Phase 2 Prototype 1 11 Transition 4 Phase 1 Prototype 2 5 Phase 1 Prototype 3 Phase 1 Deployed to Production 6 Deliverable # Milestone 8 Phase 2 Prototype 2 9 Phase 2 Prototype 3 Phase 2 Deployed to Production 10 Phase 2 Requirements
12 Subject Area Scope Subject Area Focus A related set of business entities as defined by specific user group. Integration between subject areas. Examples are: Bookings, Billings, Backlog Customer Support Calls Inventory Marketing G/L Transactions How do you control the risks?
13 Iterative Prototyping How do you control the risks?
14 Controlling Technical Risk We have found that the best way to address the technical risks associated with a data warehouse project is to employ an architecture with the following components: –Integrated Staging Area –Dimensional Data Store
15 Architectural Overview Data Warehouse – What do you deliver?
16 Integrated Staging Area Database tables holding production data before it is loaded into the dimensional data store tables. Tables usually resemble the source data tables, and have not been re-structured except to allow for some integration. Critical to source system reconciliation. Staging area may also include flat files in original format. Data Warehouse – What do you deliver?
17 Dimensional Data Store Sales by Product & Day Sales TimeGeo Product Aggregates Sales by Geo & Day Dimension Tables Fact Table Data Warehouse – What do you deliver?
18 Advantages of a Dimensional Model Standardization of dimensions helps standardize reporting across areas of the business. Dimension tables preserve the history of the dimensional information. Whole new dimensions can be introduced without major disruptions to the fact table. Data Warehouse – What do you deliver?
19 Extract, Transform and Load Data Warehouse – What do you deliver?