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Data Warehouses, OLAP and Data Mining

Data Warehouses, OLAP and Data Mining. Take Aways. Course Objectives: Find data and covert data to useful information and knowledge through the development of knowledge-worker applications such as databases (using Access) – Business Intelligence

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Data Warehouses, OLAP and Data Mining

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  1. Data Warehouses, OLAP and Data Mining

  2. Take Aways Course Objectives: Find data and covert data to useful information and knowledge through the development of knowledge-worker applications such as databases (using Access) – Business Intelligence Understand what is meant by Business Intelligence (BI) and how it is created; Understand the difference between a database and a data warehouse; Understand the information system tools used to create BI; Understand how to create CrossTab Queries in Access – used for Business Intelligence (Drilling Down through Data) 3-2

  3. Business Intelligence What is Business Intelligence and why is it important? Business Intelligence (BI) is a broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions (Howard Dresner: Garner analyst, 1989)

  4. The Scope of BI Development of a single or few related BI Applications: Excel Spreadsheets; Data Mart Development of infrastructure to support enterprise-wide BI: Data Warehouse; Customized Applications Support for organizational transformation: Supports New Business Model – Harrah’s

  5. BI Technologies Data Warehouses Data Marts Data Visualization Tools Digital Dashboards OLAP (Online Analytical Process) Tools Data Mining/Predictive Analytic Tools

  6. Major BI Software Suppliers SAS (www.sas.com) Hyperion (www.hyperion.com), now owned by Oracle), Business Objects (www.businessobjects.com, now owned by SAP), Information Builders (www.informationbuilders.com), SPSS (www.spss.com, now owned by IBM), and Cognos Corporation (www.cognos.com, now owned by IBM)

  7. DATA WAREHOUSE FUNDAMENTALS • Data warehouse – a logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making tasks • The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes

  8. DATA WAREHOUSE FUNDAMENTALS • Extraction, transformation, and loading (ETL) – a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse • Data mart – contains a subset of data warehouse information

  9. Data Warehouses What Is a Data Warehouse?

  10. DATA WAREHOUSE FUNDAMENTALS • Data Warehouse Model

  11. MULTIDIMENSIONAL ANALYSIS AND DATA MINING • Databases contain information in a series of two-dimensional tables • In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows • Dimension – a particular attribute of information

  12. MULTIDIMENSIONAL ANALYSIS AND DATA MINING • Cube– common term for the representation of multidimensional information

  13. Data Warehouses and Data MiningData Marts – Smaller Data Warehouses • Data mart - a subset of a data warehouse in which only a focused portion of the data warehouse information is kept.

  14. MULTIDIMENSIONAL ANALYSIS AND DATA MINING • Data mining – the process of analyzing data to extract information not offered by the raw data alone • To perform data mining users need data-mining tools • Data-mining tools– use a variety of techniques to find patterns and relationships in large volumes of information and infer rules from them that predict future behavior and guide decision making • Include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents

  15. OLAP & Data Mining Tools

  16. Data Mining Applications

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