Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition BUSINESS INTELLIGENCE ANALYTICS Chapter 3 AND DATA SCIENCE Descriptive Analytics II Business Intelligence and Data Warehousing Ramesh Sharda Dursun Delen Efraim Turban PEarson Pearson Copyright 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 3 Descriptive Analytics II: Business Intelligence and Data Warehousing Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Slides in this presentation contain hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7
Learning Objectives (1 of2 3. 1 Understand the basic definitions and concepts of data warehousing 3.2 Understand data warehousing architectures 3.3 Describe the processes used in developing and managing data warehouses 3. 4 Explain data warehousing operations 3. 5 Explain the role of data warehouses in decision support Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (1 of 2) 3.1 Understand the basic definitions and concepts of data warehousing 3.2 Understand data warehousing architectures 3.3 Describe the processes used in developing and managing data warehouses 3.4 Explain data warehousing operations 3.5 Explain the role of data warehouses in decision support
Learning Objectives (2 of 2) 3.6 Explain data integration and the extraction transformation, and load(EtL) processes 3.7 Understand the essence of business performance management (BPM 3. 8 Learn balanced scorecard and Six Sigma as performance measurement systems Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (2 of 2) 3.6 Explain data integration and the extraction, transformation, and load (ETL) processes 3.7 Understand the essence of business performance management (BPM) 3.8 Learn balanced scorecard and Six Sigma as performance measurement systems
Opening Vignette Targeting Tax Fraud with Business Intelligence and Data Warehousing 1. Why is it important for IRS and for U.s. state governments to use data warehousing and business intelligence(BI) tools in managing state revenues? 2. What were the challenges the state of maryland was facing with regard to tax fraud 3. What was the solution they adopted Do you agree with their approach? Why 4. What were the results that they obtained did the investment in bl and data warehousing pay off? 5. What other problems and challenges do you think federal and state governments are having that can benefit from bl and data warehousing? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette Targeting Tax Fraud with Business Intelligence and Data Warehousing 1. Why is it important for IRS and for U.S. state governments to use data warehousing and business intelligence (BI) tools in managing state revenues? 2. What were the challenges the state of Maryland was facing with regard to tax fraud? 3. What was the solution they adopted? Do you agree with their approach? Why? 4. What were the results that they obtained? Did the investment in BI and data warehousing pay off? 5. What other problems and challenges do you think federal and state governments are having that can benefit from BI and data warehousing?
Business Intelligence and Data Warehousing bI used to be everything related to Business Analytics use of data for managerial decision Descriptive Prescriptive support Predictive Now, it is a part of What happened What will happen? What should I do? g What is happening? Why will it happen? Why should I do it? Business Analytics in Business reporting Data mining v Optimization v Dashboards v Text mining v Simulation -BI= Descriptive 5/ Data warehousing. Forecasting y Web/media mining Decision modeling Expert systems Analytics Well defined Accurate projections est possible business problems of future events and iness decisions and opportunities outcomes and actions Business Intelligence Advanced Analytics Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Business Intelligence and Data Warehousing • BI used to be everything related to use of data for managerial decision support • Now, it is a part of Business Analytics – BI = Descriptive Analytics What happened? What is happening? What will happen? Why will it happen? What should I do? Why should I do it? ü Business reporting ü Dashboards ü Scorecards ü Data warehousing ü Data mining ü Text mining ü Web/media mining ü Forecasting ü Optimization ü Simulation ü Decision modeling ü Expert systems Well defined business problems and opportunities Accurate projections of future events and outcomes Best possible business decisions and actions Questions Enablers Outcomes Descriptive Predictive Prescriptive Business Analytics Business Intelligence Advanced Analytics
What is a Data Warehouse? A physical repository where relational data are specially organized to provide enterprise-Wide, cleansed data in a standardized format A relational database?(so what is the difference?) The data warehouse is a collection of integrated subject-oriented databases designed to support DSS functions where each unit of data is relevant to some moment in timeIs non-volatile and Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved What is a Data Warehouse? • A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format • A relational database? (so what is the difference?) • “The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time
A Historical Perspective to Data Warehousing ˇ Mainframe computers V Centralized data storage ˇ Big Data analytics Simple data entry V Data warehousing was born ˇ Social media analytics Routine reporting V Inmon, Building the Data Warehouse V Text and Web analytics Primitive database structures Kimball. The Data Warehouse Toolkit v Hadoop, Map Reduce NoSo Teradata Incorporated V EDW architecture design In-memory in-database 1970s-1980s-1990s 2000s 2010s V Mini/personal computers (PCs) v Exponentially growing Web data V Business applications for PCs Consolidation of DW/BI industry v Distributer DBMs Data warehouse appliances emerged Relational DBMS Business intelligence popularized Teradata ships commercial DBs Data mining and predictive modeling v Business Data Warehouse coined Open source software V SaaS Paas Cloud computing Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Historical Perspective to Data Warehousing
Characteristics ofDws ° Subject oriented Integrated Time-variant(time series) Nonvolatile Summarized Not normalized Metadata Web based relational/ multi-dimensional Client/server, real-time/right-time/active Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Characteristics of DWs • Subject oriented • Integrated • Time-variant (time series) • Nonvolatile • Summarized • Not normalized • Metadata • Web based, relational/multi-dimensional • Client/server, real-time/right-time/active
Data mart A departmental small-scale DW that stores only limited/relevant data ependent data mart A subset that is created directly from a data warehouse Independent data mart A small data warehouse designed for a strategic business unit or a department Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Data Mart A departmental small-scale “DW” that stores only limited/relevant data • Dependent data mart A subset that is created directly from a data warehouse • Independent data mart A small data warehouse designed for a strategic business unit or a department
Other Dw Components Operational data stores (ODS) a type of database often used as an interim area for a data warehouse Oper marts An operational data mart Enterprise data warehouse(EDW) a data warehouse for the enterprise Metadata-“ data about data” In dw metadata describe the contents of a data warehouse and its acquisition and use Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Other DW Components • Operational data stores (ODS) – A type of database often used as an interim area for a data warehouse • Oper marts – An operational data mart • Enterprise data warehouse (EDW) – A data warehouse for the enterprise • Metadata – “data about data” – In DW metadata describe the contents of a data warehouse and its acquisition and use
