1. Data Warehouse. Group 5 Kacie Johnson Summer Bird Washington Farver Jonathan Wright Mike Muchane

2. Outline I. Data warehouse definition and integrated technologies I. Data warehouse definition and integrated technologies II. OLAP and OLTP II. OLAP and OLTP III. The concept of data warehousing III. The concept of data warehousing IV. How data warehouses are used by companies IV. How data warehouses are used by companies V. History of data warehousing V. History of data warehousing VI. Advantages and Disadvantages VI. Advantages and Disadvantages VII. Future applications

3. Definition A data warehouse is a logical collection of information gathered from many different operational databases used to create business intelligence that supports business analysis activities and decision- making tasks. A data warehouse is a logical collection of information gathered from many different operational databases used to create business intelligence that supports business analysis activities and decision- making tasks.

4. Business Intelligence Business intelligence usually refers to the information that is available for the enterprise to make decisions on. A data warehousing (or data mart) system is the backend, or the infrastructural, component for achieving business intelligence Business intelligence usually refers to the information that is available for the enterprise to make decisions on. A data warehousing (or data mart) system is the backend, or the infrastructural, component for achieving business intelligence

5. Data Mart A database that has the same characteristics as a data warehouse, but is usually smaller and is focused on the data for one division or one workgroup within an enterprise. A database that has the same characteristics as a data warehouse, but is usually smaller and is focused on the data for one division or one workgroup within an enterprise.

6. Data Mining Tools Data mining tools are Software tools used to query information in a data warehouse. Consist of: 1. Query-and-Reporting tools 2. Intelligent Agents 3. Multidimensional analysis tools (MDA) 4. Statistical tools

7. OLAP A data warehouse uses OLAP (On-Line Analytical Processing) to collect, organize, and make data available for the purpose of analysis - to give management the ability to access and analyze information about its business. This type of data can be called “informational data”. A data warehouse uses OLAP (On-Line Analytical Processing) to collect, organize, and make data available for the purpose of analysis - to give management the ability to access and analyze information about its business. This type of data can be called “informational data”.

8. OLTP Most data is collected to handle a company's on-going business. This type of data can be called "operational data". The systems used to collect operational data are referred to as OLTP (On-Line Transaction Processing). Most data is collected to handle a company's on-going business. This type of data can be called "operational data". The systems used to collect operational data are referred to as OLTP (On-Line Transaction Processing).

9. Data Warehouse Is… Subject Oriented Subject Oriented Integrated Integrated Time Variant Time Variant Nonvolatile Collection of Data for Management’s Decisions Nonvolatile Collection of Data for Management’s Decisions

10. Building Blocks Source Data Source Data Date Staging Date Staging Data Storage Data Storage Information Delivery Information Delivery Metadata Metadata Management and Control Management and Control

12. Design of DW Integration : facilitates an overview and analysis in the data warehouse Integration : facilitates an overview and analysis in the data warehouse Separation : operations used for reporting, decision support, analysis and controlling Separation : operations used for reporting, decision support, analysis and controlling

13. Dimensions and Measures Dimensions : categorizes each item in a data set in non-overlapping regions. Dimensions : categorizes each item in a data set in non-overlapping regions. Measures : a property that can be summed or averages using pre-computed aggregates. Measures : a property that can be summed or averages using pre-computed aggregates.

14. Types of Data Warehouse Financial Financial Insurance Insurance Human Resources Human Resources Global Global Data Mining/Data Mining and Exploration Data Mining/Data Mining and Exploration Telecommunications Telecommunications

15. Before DW Executives and decision makers could get critical information that already existed on the organization Executives and decision makers could get critical information that already existed on the organization The available data was exceedingly difficult to get (“data in jail”) The available data was exceedingly difficult to get (“data in jail”) Only a fraction of the data captured, processed and stored was actually available (“data poor”) Only a fraction of the data captured, processed and stored was actually available (“data poor”)

16. DW In Companies Validation: where users validate what they already believe to be true (45%) Validation: where users validate what they already believe to be true (45%) Tactical Reporting: where the user uses the data for tactical reasons (40%) Tactical Reporting: where the user uses the data for tactical reasons (40%) Exploration: where the user searches for knowledge not already known (15%) Exploration: where the user searches for knowledge not already known (15%)

17. Why the volume of data is exploding: DWs carry historical data DWs carry historical data DWs carry detailed data DWs carry detailed data DWs carry data for which there is no known need DWs carry data for which there is no known need DWs carry eCommerce data DWs carry eCommerce data

18. Advantages Cut costs Cut costs Boost revenues Boost revenues Saves time Saves time Better customer service Better customer service Avoids old data Avoids old data Queries or reports without impacting the performance of the operational systems Queries or reports without impacting the performance of the operational systems Combines related data from separate sources Combines related data from separate sources Increased data consistency Increased data consistency Improves access to a wide variety data Improves access to a wide variety data

19. Disadvantages Can complicate business processes. Can complicate business processes. Data warehousing can have a learning curve that may be too long for impatient firms. Data warehousing can have a learning curve that may be too long for impatient firms. Can require a great deal of "maintenance.” Can require a great deal of "maintenance.” The cost to capture data, clean it up, and deliver it. The cost to capture data, clean it up, and deliver it. Inability to adapt quickly to changing business conditions or requirements. Inability to adapt quickly to changing business conditions or requirements.

20. Future Developments Development of parallel DB servers with improved query engines will make it possible to access huge data bases in much less time Development of parallel DB servers with improved query engines will make it possible to access huge data bases in much less time Another new technology is data warehouses that allow for the mixing of traditional numbers, text and multi-media. The availability of improved tools for data visualization (business intelligence) will allow users to see things that could never be seen before. Another new technology is data warehouses that allow for the mixing of traditional numbers, text and multi-media. The availability of improved tools for data visualization (business intelligence) will allow users to see things that could never be seen before.

21. Any Questions?