BUSINESS INTELLIGENCE & DATAWAREHOUSING

Transcription

1 BUSINESS INTELLIGENCE & DATAWAREHOUSING Professor: JOSÉ CURTO DÍAZ Academic Background Adjunct professor, Social and Behavioral Area, IE Business School Adjunct professor, Information Systems Area, IE Business School Associate professor, EIMT Department, UOC Part-time professor, U-TAD Part-time professor, IEB Part-time professor, EOI Part-time professor, KSchool Associate professor, Department of Economics, Universidad Auto noma de Barcelona (Barcelona: ) Ph.D in Network and Information Technologies (in process), Universitat Oberta de Catalunya Digital Marketing, IE Business School International Executive MBA, IE Business School Master in Executive Management of IT Systems, Universitat Oberta de Catalunya Master in Business Intelligence, Universitat Oberta de Catalunya Web and e-commerce postgraduate studies, Universitat Oberta de Catalunya B.S. in Mathematics, Universitat Auto noma de Barcelona Author of books / academic material: Introduccio n al Business Intelligence, Data Warehousing, Customer Analytics and Data-driven Organizations (Spanish) Corporate Experience CEO, Delfos Research (UK) Co-founder and Data Strategist, Questionity (Spain: ) Independent Research Analyst (UK: ) Senior Research Analyst, IDC Research (Spain: ) Business Intelligence Manager, ICNET Consulting (Spain: ) Solutions Manager, Stratebi Business Solutions (Spain: ) PLM Analyst, Thales Group (Spain, France: ) Java Programmer, EDS (Spain: ) Published by IE Publishing Department. Last revised, November

2 LEARNING OBJECTIVES In recent years, organisations have aligned technology and business in order to adapt to a challenging and hyper-competitive market. In a situation where organisations in many industries offer similar products and use similar technologies, business processes are established as one of the last points of differentiation. Also, data is becoming the new stronghold on which leverage the differentiation of business processes. As a result, increasingly more companies use different analytical strategies to improve decision-making, optimising processes and creating data-driven products. However, as in many other areas, technology is not sufficient to generate competitive advantage status. For example, decision-making requires actual art (based on experience and intuition) and science (based on analysis) to survive the increasing complexity of the market. Understanding how to use data and analytics to create competitive business can be the difference to the success or failure of a business advantage. This course consists of twelve sessions and it is designed to introduce and learn Business Intelligence and Data Warehousing: the first steps to transform a company into a data-driven organization. This course has several objectives: Introducing Business Intelligence (BI) and Data Warehouse (DW) to the student Learn and practice some BI & DW techniques required for the data scientist's toolkit: data warehousing, ETL and Dashboards Understanding how to develop a BI and DW project from the beginning to the end Identifying main roles and tasks in BI and DW MATERIALS During this course the following resources will be used along different sessions: Book: Kimball, R. and Ross, M. (2013) The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd Edition) Indiana: Wiley Publishing. Book: Linstedt, D. and Olschimke, M. (2015). Building a Scalable Data Warehouse with Data Vault 2.0. Walthma: Morgan Kaufmann Software: Tableau (Analysis), Pentaho Data Integration (ELT) and MySQL/MySQL Workbench (Relational Database, Modeller). Data set(s): the professor will provide the required data sets to be used during this course. Further readings or complementary cases are specified in every session. Specific software documentation and instructions will be provided at the begining of the course. 2

3 PROGRAM SESSION 1 (FACE TO FACE) What is Business Intelligence and Data Warehousing Business Intelligence & Data Warehousing Relationship between concepts Why Business Intelligence & Data Warehousing are needed BI in the Age of Big Data Examples of companies Benefits and Challenges for a Company Market: situation and players The case must be read in advance as it will be discussed in class. M.D.: Business Intelligence (SI1-131-I-M) M.D.: Big Data (SI2-107-I-M) P.C.: Business Intelligence Software at SYSCO ( PDF-ENG) R.A.: You May Not Need Big Data After All (R1312F-PDF-ENG) SESSION 2 Components of Business Intelligence and Data Warehousing Data Warehousing Components: Data Mart, Data Warehouse, Operational Data Store, Staging Area, Data Integration, Metadata Data Warehouse architecture Data Warehouse Design methodologies: Kimball vs. Inmon vs. Data Vault Business Intelligence Components: Data Warehouse, Platform, OLAP, Reporting, Dashboards, Scorecards, Balanced Scorecard, Analytics and Alerts The group project will be presented in this session. B.C.: Chapter 1: Data Warehousing, Business Intelligence, and Dimensional Modeling Primer (The Data Warehouse Toolkit) SESSION 3 Mastering data warehouse design (I) Definition of facts, dimensions and metrics Types of Facts, dimensions and metrics Discussion of several examples Tool: MySQL, MySQL Workbench B.C.: Chapter 2: Kimball Dimensional Modeling Techniques Overview (The Data Warehouse Toolkit) 3

4 SESSION 4 Mastering data warehouse design (II) How to design a data warehouse Practice: Designing a data warehouse Tool: MySQL, MySQL Workbench The "Caterpillar Case" is the individual assignment. Every student must present the proposal solution one week after session 4. B.C.: Chapter 18: Dimensional Modeling Process and Tasks (The Data Warehouse Toolkit) P.C.: Caterpillar Tunnelling: Revitalizing User Adoption of Business Intelligence (W13513-PDF- ENG) SESSION 5 Mastering ETL design (I) Data Integration: techniques and technologies Introduction to the 34 ETL Subsystems ETL development Lifecycle Tool: Pentaho Data Integration B.C.: Chapter 19: ETL Subsystems and Techniques (The Data Warehouse Toolkit) SESSION 6 Mastering ETL design (II) Data extraction Cleansing and Conforming Handling dimension tables Loading Fact Tables Practice: How to design an ETL process Tool: Pentaho Data Integration B.C.: Chapter 20: ETL System Design and Development Process and Tasks (The Data Warehouse Toolkit) SESSION 7 Mastering ETL design (III) Practice: How to design an ETL process Tool: Pentaho Data Integration B.C.: Chapter 20: ETL System Design and Development Process and Tasks (The Data Warehouse Toolkit) 4

5 SESSION 8 Data Governance What is Data Governance Principles of Data Governance How to implement a Data Governance program Technologies in Data Governance SESSION 9 Mastering Analysis (I): Reporting Reporting: definition, types, elements Types of metrics Types of graphs (how to choose a graph) Practice: how to analyze data Tool: Tableau Software SESSION 10 Mastering Analysis (II): Dashboard Dashboard: definition, types, elements Dashboard vs. Scorecard vs. Balanced Scorecard Practice: how to analyze data Tool: Tableau Software SESSION 11 Mastering Analysis (III) Data Visualization Data Storytelling How Data Visualization can benefit reports and dashboards Practice: how to analyze data Tool: Tableau Software SESSION 12 The value of BI & DW Session with companies. Some companies will present real business cases where Business Intelligence helped to create competitive advantages. The students will have the opportunity to ask and discuss any interest related with the topics of the course. EVALUATION METHOD The evaluation consist in three workgroup assigments, one individual assignment and class participation. The three practical activities are linked and belong to the same Business Intelligence project. 5

6 Criteria Score % Data Warehouse Design 25% ETL Design 25% Analysis 20% Case Discussion (s1) & Class Participation 10% Individual Case 20% Students are expected to attend every class and to participate in the class discussions. Class participation grades are based on two aspects: your attendance in class and your contributions to the class discussions. Contributions to discussions will focus on the quality, not the quantity of the contribution; therefore students who participate often will not necessarily receive a better grade than those who participate less often. One must recognize, however, that there is an art to quality participation that is only learned by trial and error. Therefore, students are encouraged to begin contributing to the discussions early in the course. As the value of this course stems from class discussion, participation and practice, your attendance at class sessions is critical to learning the material and to enhancing the discussions. Therefore, your participation grade will include your class attendance. If you are unable to attend a class, please call the instructor prior to the class period to let him know. If you must miss a session, you may write and submit a THREE-page analysis of the issues discussed in the references (chapters, cases, technical notes or articles) in order to avoid penalizing your participation grade. It is due by the beginning of the next class and no late write-ups will be accepted. Each student in the class is required to participate in a working team. Working teams, therefore, will serve as a forum where students test and refine their analysis of the topic addressed. The working teams may be particularly useful in providing students with a sense of their increasing expertise in the application of research and problem-solving skills and methodologies that are developed by a "student-centered" learning approach. Workgroup - Data Warehouse Design: 25% Each group must present a design of a data warehouse based on the case to be presented at the second session. This design will be based on the methodologies learned in sessions 3, 4 and 5. The student will use a dataset to be provided at the beginning of the course. The purpose of this activity is to acquire the necessary skills for the development of data warehouses. More detailed information will be presented in Session 5. This activity must be delivered before session 6. Workgroup - ETL Design: 25% From the data warehouse model presented in the previous activity, each group must create ETL processes for loading data into the data warehouse. This design will be based on the topics learned in sessions 6, 7 and 8. The purpose of this activity is to acquire the necessary skills for datawarehousing. More detailed information will be presented in Session 8. This activity must be delivered before session 9. Workgroup - Analysis: 20% Each team will be have to propose one analysis (reporting, dashboard, data visualization, data storytelling) related to dataset and data mart presented in session 8. The dashboard will be the final step in the development of the Business Intelligence project. This design will be based on the methodologies learned in sessions 9, 10 and 11. More detailed information will be presented in Session 9. This activity must be delivered to days after session 12. Individual Case: 20% Each student will propose a solution for a business case related to business intelligence. This case will focus on the managerial aspects of this kind of projects and the impact on the organization. 6

7 REFERENCES Course Readings Kimball, R. and Ross, M. (2013) The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd Edition) Indiana: Wiley Publishing Business Intelligence Atre, S. and Moss, L. (2003) Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications. Boston: Addison-Wesley Professional Devlin, B. (2013) Business unintelligence: Insight and Innovation beyond Analytics and Big Data. New Jersey: Technics Publications Loshin, D. (2012) Business Intelligence: The Savvy Manager's Guide (The Morgan Kaufmann Series on Business Intelligence) (2nd Edition). New York: Morgan Kaufmann Howson, C. (2013) Successful Business Intelligence, Second Edition: Unlock the Value of BI & Big Data. New York: McGraw-Hill Osborne Media Data Warehousing Adamson, C. (2006) Mastering Data Warehouse Aggregates: Solutions for Star Schema Performance. New York: Wiley Becker, B., Ross, M., Thornthwaite, W., Mundy, J. and Kimball, R. (2008) The Data Warehouse Lifecycle Toolkit. Indiana: Wiley Publishing Caserta, J. and Kimball, R. (2004) The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Indiana: Wiley Publishing Corr, L. (2011). Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema. Leeds: DecisionOne Press Koncilia, C. and Wrembel, R. (2006) Data Warehouses and Olap: Concepts, Architectures and Solutions. Hersley: IGI Global Thomsen, E. (2002) OLAP Solutions: Building Multidimensional Information Systems (2nd Edition). New York: Wiley Venerable, M. (1998) Data Warehouse Design Solutions. New York: Wiley Balanced Scorecard Kaplan, R. and Norton, D. (1996). The Balanced Scorecard: Translating Strategy into Action 6 Boston: Harvard Business Review Press Niven, P. (2014) Balanced Scorecard Evolution: A Dynamic Approach to Strategy Execution. Indiana: Wiley Publishing Project Management (in the context of Business Intelligence and Data Warehousing) Collier, K. (2011) Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series) Boston: Addison-Wesley Professional Moss, L. (2013) Extreme Scoping: An Agile Approach to Enterprise Data Warehousing and Business Intelligence. New Jersey: Technics Publications Corr, L. (2011) Reeves, L.L. (2009) A Manager s Guide to Data Warehousing. Indiana: Wiley Publishing Visualization Cairo, A. (2012) The Functional Art: An introduction to information graphics and visualization. Berkley: PeachPit Press, a division of Pearson Education Eckerson, W. (2010) Performance Dashboards: Measuring, Monitoring, and Managing Your Business (2nd Edition). New York: Wiley 7

8 Few, S. (2012) Show Me the Numbers: Designing Tables and Graphs to Enlighten (2nd Edition). Burlingame: Analytic Press Few, S. (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Burlingame: Analytic Press Few, S. (2013) Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, Second Edition. Burlingame: Analytic Press Beyond Business Intelligence Krishnan, K. (2013) Data Warehousing in the Age of Big Data (The Morgan Kaufmann Series on Business Intelligence). New York: Morgan Kaufmann Laursen G. and Thorlund, J. (2010) Business Analytics for Managers: Taking Business Intelligence Beyond Reporting. New York: Wiley 8