The Business Intelligence Manager One Case Study

Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) Chapter 2: Data Warehousing Learning Objectives  Understand the basic definitions and concepts of data warehouses  Learn different types of data warehousing architectures; their comparative advantages and disadvantages  Describe the processes used in developing and managing data warehouses  Explain data warehousing operations (Continued…) Copyright © 2014 Pearson Education, Inc. Slide 2- 2 Learning Objectives  Explain the role of data warehouses in decision support  Explain data integration and the extraction, transformation, and load (ETL) processes  Describe real-time (a.k.a. right-time and/or active) data warehousing  Understand data warehouse administration and security issues Copyright © 2014 Pearson Education, Inc. Slide 2- 3 Opening Vignette… Isle of Capri Casinos Is Winning with Enterprise Data Warehouse  Company background  Problem description  Proposed solution  Results  Answer & discuss the case questions. Copyright © 2014 Pearson Education, Inc. Slide 2- 4 Questions for the Opening Vignette 1. Why is it important for Isle to have an EDW? 2. What were the business challenges or opportunities that Isle was facing? 3. What was the process Isle followed to realize EDW? Comment on the potential challenges Isle might have had going through the process of EDW development. 4. What were the benefits of implementing an EDW at Isle? Can you think of other potential benefits that were not listed in the case? 5. Why do you think large enterprises like Isle in the gaming industry can succeed without having a capable data warehouse/business intelligence infrastructure? Copyright © 2014 Pearson Education, Inc. Slide 2- 5 Main Data Warehousing Topics  DW definition  Characteristics of DW  Data Marts  ODS, EDW, Metadata  DW Framework  DW Architecture & ETL Process  DW Development  DW Issues Copyright © 2014 Pearson Education, Inc. Slide 2- 6 What is a Data Warehouse?  A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format  “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” Copyright © 2014 Pearson Education, Inc. Slide 2- 7 A Historical Perspective to Data Warehousing ü ü ü ü ü Mainframe computers Simple data entry Routine reporting Primitive database structures Teradata incorporated 1970s ü ü ü ü ü Centralized data storage Data warehousing was born Inmon, Building the Data Warehouse Kimball, The Data Warehouse Toolkit EDW architecture design 1980s ü ü ü ü ü 1990s Mini/personal computers (PCs) Business applications for PCs Distributer DBMS Relational DBMS Teradata ships commercial DBs ü Business Data Warehouse coined ü ü ü ü ü 2000s ü ü ü ü ü ü ü Big Data analytics Social media analytics Text and Web Analytics Hadoop, MapReduce, NoSQL In-memory, in-database 2010s Exponentially growing data Web data Consolidation of DW/BI industry Data warehouse appliances emerged Business intelligence popularized Data mining and predictive modeling Open source software SaaS, PaaS, Cloud Computing Copyright © 2014 Pearson Education, Inc. Slide 2- 8 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 … Copyright © 2014 Pearson Education, Inc. Slide 2- 9 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 Copyright © 2014 Pearson Education, Inc. Slide 2- 10 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 a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use Copyright © 2014 Pearson Education, Inc. Slide 2- 11 Application Case 2.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry Questions for Discussion 1. What are the main challenges for TELCOs? 2. How can data warehousing and data analytics help TELCOs in overcoming their challenges? 3. Why do you think TELCOs are well suited to take full advantage of data analytics? Copyright © 2014 Pearson Education, Inc. Slide 2- 12 A Generic DW Framework No data marts option Applications (Visualization) Data Sources Access ETL Process Metadata Extract POS Transform Enterprise Data warehouse Integrate Other OLTP/wEB Data mart (Finance) Load Replication External data Data mart (Engineering) Data mart (…) Copyright © 2014 Pearson Education, Inc. / Middleware Data mart (Marketing) Select Legacy Routine Business Reporting API ERP Data/text mining OLAP, Dashboard, Web Custom built applications Slide 2- 13 Application Case 2.2 Data Warehousing Helps MultiCare Save More Lives Questions for Discussion 1. What do you think is the role of data warehousing in healthcare systems? 2. How did MultiCare use data warehousing to improve health outcomes? Copyright © 2014 Pearson Education, Inc. Slide 2- 14 DW Architecture Three-tier architecture  1. 2. 3.  Data acquisition software (back-end) The data warehouse that contains the data & software Client (front-end) software that allows users to access and analyze data from the warehouse Two-tier architecture First two tiers in three-tier architecture is combined into one … sometimes there is only one tier? Copyright © 2014 Pearson Education, Inc. Slide 2- 15 DW Architectures Tier 1: Client workstation Tier 1: Client workstation Tier 2: Application server Tier 3: Database server Tier 2: Application & database server Copyright © 2014 Pearson Education, Inc. Slide 2- 16 Data Warehousing Architectures  Issues to consider when deciding which architecture to use:  Which database management system (DBMS) should be used?  Will parallel processing and/or partitioning be used?  Will data migration tools be used to load the data warehouse?  What tools will be used to support data retrieval and analysis? Copyright © 2014 Pearson Education, Inc. Slide 2- 17 A Web-Based DW Architecture Web pages Client (Web browser) Internet/ Intranet/ Extranet Application Server Web Server Data warehouse Copyright © 2014 Pearson Education, Inc. Slide 2- 18 Alternative DW Architectures (a) Independent Data Marts Architecture ETL Source Systems Staging Area Independent data marts (atomic/summarized data) End user access and applications (b) Data Mart Bus Architecture with Linked Dimensional Datamarts ETL Source Systems Staging Area Dimensionalized data marts linked by conformed dimensions (atomic/summarized data) End user access and applications (c) Hub and Spoke Architecture (Corporate Information Factory) ETL Source Systems Staging Area Normalized relational warehouse (atomic data) End user access and applications Dependent data marts (summarized/some atomic data) Alternative DW Architectures (d) Centralized Data Warehouse Architecture ETL Source Systems Staging Area Normalized relational warehouse (atomic/some summarized data) End user access and applications (e) Federated Architecture Data mapping / metadata Existing data warehouses Data marts and legacy systems Logical/physical integration of common data elements End user access and applications  Each architecture has advantages and disadvantages!  Which architecture is the best? Ten factors that potentially affect the architecture selection decision 1. Information interdependence between organizational units 2. Upper management’s information needs 3. Urgency of need for a data warehouse 4. Nature of end-user tasks 5. Constraints on resources 6. Strategic view of the data warehouse prior to implementation 7. Compatibility with existing systems 8. Perceived ability of the inhouse IT staff 9. Technical issues 10. Social/political factors Copyright © 2014 Pearson Education, Inc. Slide 2- 21 Teradata Corp. DW Architecture Copyright © 2014 Pearson Education, Inc. Slide 2- 22 Data Integration and the Extraction, Transformation, and Load (ETL) Process  ETL = Extract Transform Load  Data integration Integration that comprises three major processes: data access, data federation, and change capture.  Enterprise application integration (EAI) A technology that provides a vehicle for pushing data from source systems into a data warehouse  Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, Web services, etc. Copyright © 2014 Pearson Education, Inc. Slide 2- 23 Data Integration and the Extraction, Transformation, and Load (ETL) Process Transient data source Packaged application Data warehouse Legacy system Extract Transform Cleanse Load Data mart Other internal applications Copyright © 2014 Pearson Education, Inc. Slide 2- 24 ETL (Extract, Transform, Load)  Issues affecting the purchase of an ETL tool  Data transformation tools are expensive  Data transformation tools may have a long learning curve  Important criteria in selecting an ETL tool  Ability to read from and write to an unlimited number of data sources/architectures  Automatic capturing and delivery of metadata  A history of conforming to open standards  An easy-to-use interface for the developer and the functional user Copyright © 2014 Pearson Education, Inc. Slide 2- 25 Data Warehouse Development Data warehouse development approaches    Inmon Model: EDW approach (top-down) Kimball Model: Data mart approach (bottom-up) Which model is best?  Table 2.3 provides a comparative analysis between EDW and Data Mart approach  One alternative is the hosted warehouse Copyright © 2014 Pearson Education, Inc. Slide 2- 26 Application Case 2.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing Questions for Discussion 1. How big and complex are the business operations of Starwood Hotels & Resorts? 2. How did Starwood Hotels & Resorts use data warehousing for better profitability? 3. What were the challenges, the proposed solution, and the obtained results? Copyright © 2014 Pearson Education, Inc. Slide 2- 27 Additional Data Warehouse Considerations Hosted Data Warehouses  Benefits:  Requires minimal investment in infrastructure  Frees up capacity on in-house systems  Frees up cash flow  Makes powerful solutions affordable  Enables solutions that provide for growth  Offers better quality equipment and software  Provides faster connections  … more in the book Copyright © 2014 Pearson Education, Inc. Slide 2- 28 Representation of Data in DW  Dimensional Modeling  A retrieval-based system that supports high-volume query access  Star schema  The most commonly used and the simplest style of dimensional modeling  Contain a fact table surrounded by and connected to several dimension tables  Snowflakes schema  An extension of star schema where the diagram resembles a snowflake in shape Copyright © 2014 Pearson Education, Inc. Slide 2- 29 Multidimensionality The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)  Multidimensional presentation  Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry  Measures: money, sales volume, head count, inventory profit, actual versus forecast  Time: daily, weekly, monthly, quarterly, or yearly Copyright © 2014 Pearson Education, Inc. Slide 2- 30 Star versus Snowflake Schema Star Schema Dimension TIME Snowflake Schema Dimension PRODUCT Dimension MONTH Quarter Brand M_Name … … … Fact Table SALES Dimension QUARTER UnitsSold Dimension BRAND Brand Dimension DATE Date LineItem … … Q_Name … Dimension GEOGRAPHY Division Country … … … Dimension CATEGORY Category Fact Table SALES … Dimension PEOPLE Dimension PRODUCT … UnitsSold … Dimension PEOPLE Dimension STORE Division LocID … … Dimension LOCATION State … Copyright © 2014 Pearson Education, Inc. Slide 2- 31 Analysis of Data in DW  OLTP vs. OLAP…  OLTP (online transaction processing)  Capturing and storing data from ERP, CRM, POS, …  The main focus is on efficiency of routine tasks  OLAP (Online analytical processing)  Converting data into information for decision support  Data cubes, drill-down / rollup, slice & dice, …  Requesting ad hoc reports  Conducting statistical and other analyses  Developing multimedia-based applications  …more in the book Copyright © 2014 Pearson Education, Inc. Slide 2- 32 OLAP vs. OLTP Copyright © 2014 Pearson Education, Inc. Slide 2- 33 OLAP Operations  Slice – a subset of a multidimensional array  Dice – a slice on more than two dimensions  Drill Down/Up – navigating among levels of data ranging from the most summarized (up) to the most detailed (down)  Roll Up – computing all of the data relationships for one or more dimensions  Pivot – used to change the dimensional orientation of a report or an ad hoc query-page display Copyright © 2014 Pearson Education, Inc. Slide 2- 34 A 3-dimensional OLAP cube with slicing operations e Ti m Slicing Operations on a Simple TreeDimensional Data Cube Product Cells are filled with numbers representing sales volumes Geography OLAP Sales volumes of a specific Product on variable Time and Region Sales volumes of a specific Region on variable Time and Products Sales volumes of a specific Time on variable Region and Products Copyright © 2014 Pearson Education, Inc. Slide 2- 35 Variations of OLAP    Multidimensional OLAP (MOLAP) OLAP implemented via a specialized multidimensional database (or data store) that summarizes transactions into multidimensional views ahead of time Relational OLAP (ROLAP) The implementation of an OLAP database on top of an existing relational database Database OLAP and Web OLAP (DOLAP and WOLAP); Desktop OLAP,… Copyright © 2014 Pearson Education, Inc. Slide 2- 36 Technology Insights 2.2 Hands-On DW with MicroStrategy  A wealth of teaching and learning resources can be found at TUN portal www.teradatauniversitynetwork.com  The available resources include scripted demonstrations, assignments, white papers, etc… Copyright © 2014 Pearson Education, Inc. Slide 2- 37 DW Implementation Issues  Identification of data sources and governance  Data quality planning, data model design  ETL tool selection  Establishment of service-level agreements  Data transport, data conversion  Reconciliation process  End-user support  Political issues  … more in the book Copyright © 2014 Pearson Education, Inc. Slide 2- 38 Successful DW Implementation Things to Avoid  Starting with the wrong sponsorship chain  Setting expectations that you cannot meet  Engaging in politically naive behavior  Loading the data warehouse with information just because it is available  Believing that data warehousing database design is the same as transactional database design  Choosing a data warehouse manager who is technology oriented rather than user oriented  … more in the book Copyright © 2014 Pearson Education, Inc. Slide 2- 39 Failure Factors in DW Projects  Lack of executive sponsorship  Unclear business objectives  Cultural issues being ignored  Change management  Unrealistic expectations  Inappropriate architecture  Low data quality / missing information  Loading data just because it is available Copyright © 2014 Pearson Education, Inc. Slide 2- 40 Massive DW and Scalability  Scalability  The main issues pertaining to scalability:   The amount of data in the warehouse How quickly the warehouse is expected to grow  The number of concurrent users  The complexity of user queries  Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouse Copyright © 2014 Pearson Education, Inc. Slide 2- 41 Real-Time/Active DW/BI  Enabling real-time data updates for real-time analysis and real-time decision making is growing rapidly  Push vs. Pull (of data)  Concerns about real-time BI  Not all data should be updated continuously  Mismatch of reports generated minutes apart  May be cost prohibitive  May also be infeasible Copyright © 2014 Pearson Education, Inc. Slide 2- 42 Enterprise Decision Evolution and Data Warehousing Copyright © 2014 Pearson Education, Inc. Slide 2- 43 Real-Time/Active DW at Teradata Copyright © 2014 Pearson Education, Inc. Slide 2- 44 Traditional versus Active DW Copyright © 2014 Pearson Education, Inc. Slide 2- 45 DW Administration and Security  Data warehouse administrator (DWA)  DWA should…  have the knowledge of high-performance software, hardware and networking technologies  possess solid business knowledge and insight  be familiar with the decision-making processes so as to suitably design/maintain the data warehouse structure  possess excellent communications skills  Security and privacy is a pressing issue in DW  Safeguarding the most valuable assets  Government regulations (HIPAA, etc.)  Must be explicitly planned and executed Copyright © 2014 Pearson Education, Inc. Slide 2- 46 The Future of DW  Sourcing…  Web, social media, and Big Data  Open source software  SaaS (software as a service)  Cloud computing  Infrastructure…  Columnar  Real-time DW  Data warehouse appliances  Data management practices/technologies  In-database & In-memory processing New DBMS  Advanced analytics  … Copyright © 2014 Pearson Education, Inc. Slide 2- 47 Free of Charge DW Portal for Teaching & Learning  www.TeradataUniversityNetwork.com  Password to signup: Copyright © 2014 Pearson Education, Inc. Slide 2- 48 End of the Chapter  Questions, comments Copyright © 2014 Pearson Education, Inc. Slide 2- 49 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2014 Pearson Education, Inc. Slide 2- 50 …
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