Business Intelligence Transparencies 1

©Pearson Education 2009 Objectives What business intelligence (BI) represents. The technologies associated with business intelligence including: data warehousing, online analytical processing (OLAP), and data mining. The main concepts associated with a data warehouse. The relationship between online transaction processing (OLTP) systems and a data warehouse. The main concepts associated with a data mart. 2

©Pearson Education 2009 Objectives Designing a database for decision-support using a technique called dimensionality modeling. The important concepts associated with online analytical processing (OLAP) systems. The main categories of OLAP tools. The main concepts associated with data mining. How a business intelligence (BI) tool such as Microsoft Analytical Services provides decision- support. 3

©Pearson Education 2009 Business intelligence The processes for collecting and analyzing data, the technologies used in these processes, and the information obtained from these processes with the purpose of facilitating corporate decision–making. The main technologies associated with business intelligence includes: data warehouse, online analytical processing (OLAP), data mining. 4

©Pearson Education 2009 Data warehouse A database system that is designed to support decision-making by presenting an integrated view of corporate data that is copied from disparate data sources. Data held in a data warehouse is described as being subject-oriented, integrated, time-variant, and non-volatile (Inmon, 1993). The main source of data for the data warehouse are online transaction processing (OLTP) systems. 5

©Pearson Education 2009 Comparison of OLTP with data warehousing 6

©Pearson Education 2009 Typical architecture of a data warehouse 7

©Pearson Education 2009 Data mart A subset of a data warehouse, which supports the decision-making requirements of a particular department or business area. 8

©Pearson Education 2009 Designing databases for decision-support 9 Decision-support databases can be designed using traditional database design or specialist techniques such as dimensionality modeling. Dimensionality modeling aims to build a data model (called dimensional model) that has a consistent and intuitive structure to facilitate efficient multi- dimensional analysis of data.

©Pearson Education 2009 Dimensionality modeling 10 Creates a dimensional model (DM) called a star schema that has a fact table containing factual data in the center, surrounded by smaller dimension tables containing denormalized reference data. As the bulk of data is represented as facts, the fact tables can be extremely large relative to the dimension tables. Dimension tables contain descriptive textual information and are used as the constraints (search conditions) in queries on the fact data.

©Pearson Education 2009 Star schema for StayHome DVD sales 11

©Pearson Education 2009 Online analytical processing (OLAP) Stores large volumes of multi-dimensional data that is aggregated (summarized) to various levels of detail to support advanced analysis of this data. Multi-dimensional data can be characterized through many different views. For example DVD sales can be viewed by product, customer, and/or sales channel. 12

©Pearson Education 2009 Examples of OLAP applications 13

©Pearson Education 2009 Online analytical processing (OLAP) OLAP tools are categorized according to the architecture of the underlying database (providing the data for the purposes of online analytical processing). There are three main categories of OLAP tools: Multi-dimensional OLAP (MOLAP or MD-OLAP); Relational OLAP (ROLAP); Hybrid OLAP (HOLAP). 14

©Pearson Education 2009 Typical architecture for multi- dimensional OLAP (MOLAP) 15

©Pearson Education 2009 Typical architecture for relational OLAP (ROLAP) 16

©Pearson Education 2009 Typical architecture for hybrid OLAP (HOLAP) 17

©Pearson Education 2009 Cube Browser of Microsoft SQL analytical services 18

©Pearson Education 2009 Data mining The process of extracting valid, previously unknown, comprehensible, and actionable knowledge from large databases and using it to provide decision-support. 19

©Pearson Education 2009 Examples of data mining applications 20

©Pearson Education 2009 Data mining tools Important features of data mining tools include; data preparation; selection of data mining operations (algorithms); product scalability and performance; facilities for understanding results. 21

©Pearson Education 2009 Data Mining Model Browser of Microsoft SQL Analytical Services 22

©Pearson Education 2009 Dependency Network Browser of Microsoft SQL Analytical Services 23

©Pearson Education 2009 Data warehousing and data mining Major challenge to exploit data mining is identifying suitable data to mine. Data mining requires a single, separate, clean, integrated, and self-consistent source of data. A data warehouse is well equipped for providing data for mining. 24