Business Intelligence Lecture 1: Introduction to Business Intelligence. Ali Ghaffarinejad Amirkabir University of Technology

Transcription

1 Lecture 1: Introduction to Ali Ghaffarinejad Amirkabir University of Technology

2 Resources Text Book Efraim Turban, Ramesh Sharda, Dursun Delen, David King, ": A Managerial Approach", 2nd Edition, Prentice Hall, Resources George M. Marakas, Modern Data Warehousing, Mining, and Visualization: Concepts, Prentice Hall, Core Efraim Turban, "Decision Support and Systems", 9th Edition, Prentice Hall, Larissa T. Moss, Shaku Atre, " Roadmap: The Complete Project Lifecycle for Decision Support Applications", Addison Wesley, Abraham Silberschatz, Henry F. Korth, S. Sudarshan, "Database System Concepts", 6th Edition, McGrow Hill, Lecture 1: Introduction to

3 Learning Objectives Understand today's turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting opportunities) Understand the need for computerized support of managerial decision making Describe the business intelligence (BI) methodology and concepts and relate them to decision support systems (DSS) Understand the issues in implementing BI 3 Lecture 1: Introduction to

4 Changing Business Environment & Computerized Decision Support Companies are moving aggressively to computerized support of their operations => Business Pressures Responses Support Model Business pressures result of today's competitive business climate Responses to counter the pressures Support to better facilitate the process 4 Lecture 1: Introduction to

5 Business Pressures Responses Support Model 5 Lecture 1: Introduction to

6 The Business Environment The environment in which organizations operate today is becoming more and more complex, creating: opportunities, and problems. Example: globalization. Business environment factors: markets, consumer demands, technology, and societal. 6 Lecture 1: Introduction to

7 Business Environment Factors FACTOR Markets Consumer demand Technology Societal legislation DESCRIPTION Strong competition Expanding global markets Blooming electronic markets on the Internet Innovative marketing methods Opportunities for outsourcing with IT support Need for real-time, on-demand transactions Desire for customization Desire for quality, diversity of products, and speed of delivery Customers getting powerful and less loyal More innovations, new products, and new services Increasing obsolescence rate Increasing information overload Social networking, Web 2.0 and beyond Growing government regulations and deregulation Workforce more diversified, older, and composed of more women Prime concerns of homeland security and terrorist attacks Necessity of Sarbanes-Oxley Act and other reporting-related Increasing social responsibility of companies Greater emphasis on sustainability 7 Lecture 1: Introduction to

8 Organizational Responses Be Reactive, Anticipative, Adaptive, and Proactive Managers may take actions, such as: Employing strategic planning. Using new and innovative business models. Restructuring business processes. Participating in business alliances. Improving corporate information systems. Improving partnership relationships. Encouraging innovation and creativity. cont > 8 Lecture 1: Introduction to

9 Organizational Responses, continued Improving customer service and relationships. Moving to electronic commerce (e-commerce). Moving to make-to-order production and on-demand manufacturing and services. Using new IT to improve communication, data access (discovery of information), and collaboration. Responding quickly to competitors' actions (e.g., in pricing, promotions, new products and services). Automating many tasks of white-collar employees. Automating certain decision processes. Improving decision making by employing analytics. 9 Lecture 1: Introduction to

10 Closing the Strategy Gap One of the major objectives of computerized decision support is to facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them. 10 Lecture 1: Introduction to

11 (BI) BI is an evolution of decision support concepts over time. Meaning of EIS/DSS Then: Executive Information System Now: Everybody s Information System (BI) BI systems are enhanced with additional visualizations, alerts, and performance measurement capabilities. The term BI emerged from industry apps. 11 Lecture 1: Introduction to

12 Definition of BI BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. BI a content-free expression, so it means different things to different people. BI's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis. BI helps transform data, to information (and knowledge), to decisions and finally to action. 12 Lecture 1: Introduction to

13 A Brief History of BI The term BI was coined by the Gartner Group in the mid-1990s However, the concept is much older 1970s MIS reporting static/periodic reports 1980s Executive Information Systems (EIS) 1990s OLAP, dynamic, multidimensional, ad-hoc reporting -> coining of the term BI Inclusion of AI and Data/Text Mining capabilities; Webbased Portals/Dashboards 2010s Yet to be seen 13 Lecture 1: Introduction to

14 The Evolution of BI Capabilities 14 Lecture 1: Introduction to

15 The Architecture of BI A BI system has four major components: a data warehouse, with its source data business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse; business performance management (BPM) for monitoring and analyzing performance a user interface (e.g., dashboard) 15 Lecture 1: Introduction to

16 A High-level Architecture of BI 16 Lecture 1: Introduction to

17 Components in a BI Architecture The data warehouse is the cornerstone of any medium-tolarge BI system. Originally, the data warehouse included only historical data that was organized and summarized, so end users could easily view or manipulate it. Today, some data warehouses include access to current data as well, so they can provide real-time decision support (for details see lecture 2). Business analytics are the tools that help users transform data into knowledge (e.g., queries, data/text mining tools, etc.). 17 Lecture 1: Introduction to

18 BI Examples Epagogix is an analytics based BI system that specializes in predicting success of movies based on a detailed analysis of movie scripts. National Australia Bank uses data mining to aid its marketing initiatives. Hoyt Highland Partners, a marketing intelligence firm, assists health care providers with growing their businesses. 18 Lecture 1: Introduction to

19 Components in a BI Architecture Business Performance Management (BPM), which is also referred to as corporate performance management (CPM), is an emerging portfolio of applications within the BI framework that provides enterprises tools they need to better manage their operations (for details see lecture 3). User Interface (i.e., dashboards) provides a comprehensive graphical/pictorial view of corporate performance measures, trends, and exceptions. 19 Lecture 1: Introduction to

20 Styles of BI MicroStrategy, Corp. distinguishes five styles of BI and offers tools for each: 1. report delivery and alerting 2. enterprise reporting (using dashboards and scorecards) 3. cube analysis (also known as slice-and-dice analysis) 4. ad-hoc queries 5. statistics and data mining 20 Lecture 1: Introduction to

21 The Benefits of BI The ability to provide accurate information when needed, including a real-time view of the corporate performance and its parts A survey by Thompson (2004) Faster, more accurate reporting (81%) Improved decision making (78%) Improved customer service (56%) Increased revenue (49%) See Table in the next slide for a list of BI analytic applications, the business questions they answer and the business value they bring. 21 Lecture 1: Introduction to

22 Business Value of BI Analytical Applications Analytic Application Customer Segmentation Propensity to buy Customer profitability Fraud detection Business Question What market segments do my customers fall into, and what are their characteristics? Which customers are most likely to respond to my promotion? What is the lifetime profitability of my customer? How can I tell which transactions are likely to be fraudulent? Business Value Personalize customer relationships for higher satisfaction and retention Target customers based on their need to increase their loyalty to your product line Make individual business interaction decisions based on the overall profitability of customers. Quickly determine fraud and take immediate action to minimize cost. Customer attrition Which customer is at risk of leaving? Prevent loss of high-value customers and let go of lower-value customers. Channel optimization What is the best channel to reach my customer in each segment? Interact with customers based on their preference and your need to manage cost 22 Lecture 1: Introduction to

23 Automated Decision Making A relatively new approach to supporting decision making Applies to highly structured decisions Automated decision systems (ADS) (or decision automation systems) An ADS is a rule-based system that provides a solution to a repetitive managerial problem in a specific area. e.g., simple-loan approval system 23 Lecture 1: Introduction to

24 Automated Decision-Making Framework 24 Lecture 1: Introduction to

25 Automated Decision Making ADS initially appeared in the airline industry called revenue (or yield) management (or revenue optimization) systems. dynamically price tickets based on actual demand Today, many service industries use similar pricing models. ADS are driven by business rules! 25 Lecture 1: Introduction to

26 Intelligence Creation and Use A Cyclical Process of Intelligence Creation And Use BI practitioners often follow the national security model depicted in this figure. 26 Lecture 1: Introduction to

27 Intelligence Creation and Use Steps Involved Data warehouse deployment Creation of intelligence Identification and prioritization of BI projects By using ROI and TCO (cost-benefit analysis) This process is also called BI governance BI Governance Who should do the prioritization? Partnership between functional area heads Partnership between customers and providers 27 Lecture 1: Introduction to

28 BI Governance Issues/Tasks 1. Create categories of projects (investment, business opportunity, strategic, mandatory, etc.) 2. Define criteria for project selection 3. Determine and set a framework for managing project risk 4. Manage and leverage project interdependencies 5. Continuously monitor and adjust the composition of the portfolio 28 Lecture 1: Introduction to

29 Intelligence and Espionage Stealing corporate secrets, CIA, Intelligence vs. Espionage Intelligence The way that modern companies ethically and legally organize themselves to glean as much as they can from their customers, their business environment, their stakeholders, their business processes, their competitors, and other such sources of potentially valuable information Problem too much data, very little value Use of data/text/web mining (see Chapter 4, 5) 29 Lecture 1: Introduction to

30 Transaction Processing Versus Analytic Processing Transaction processing systems are constantly involved in handling updates (add/edit/delete) to what we might call operational databases. ATM withdrawal transaction, sales order entry via an ecommerce site updates DBs Online analytic processing (OLTP) handles routine on-going business ERP, SCM, CRM systems generate and store data in OLTP systems The main goal is to have high efficiency 30 Lecture 1: Introduction to

31 Transaction Processing Versus Analytic Processing Online analytic processing (OLAP) systems are involved in extracting information from data stored by OLTP systems Routine sales reports by product, by region, by sales person, etc. Often built on top of a data warehouse where the data is not transactional Main goal is effectiveness (and then, efficiency) provide correct information in a timely manner More on OLAP will be covered in Chapter 2 31 Lecture 1: Introduction to

32 Successful BI Implementation Implementing and deploying a BI initiative is a lengthy, expensive and risky endeavor! Success of a BI system is measured by its widespread usage for better decision making. The typical BI user community includes All levels of the management hierarchy (not just the top executives, as was for EIS) Provide what is needed to whom he/she needs it A successful BI system must be of benefit to the enterprise as a whole. 32 Lecture 1: Introduction to

33 BI and Business Strategy To be successful, BI must be aligned with the company s business strategy. BI cannot/should not be a technical exercise for the information systems department. BI changes the way a company conducts business by improving business processes, and transforming decision making to a more data/fact/information driven activity. BI should help execute the business strategy and not be an impediment for it! 33 Lecture 1: Introduction to

34 BI for Business Strategy Strategy should be aligned with BI/DW has the capability to execute the initiative by establishing a BI Competency Center (BICC) which can: Demonstrate linkage BI to strategy. Encourage interaction between the potential business users and the IS organization. Both sides have a lot to learn from each other Serve as a repository and disseminator of best BI practices among the different lines of business. Advocate and encourage standards of excellence. Help stakeholders understand the crucial role of BI. 34 Lecture 1: Introduction to

35 Real-time, On-demand BI The demand for real-time BI is growing! Is real-time BI attainable? Technology is getting there Automated, faster data collection (RFID, sensors, ) Database and other software technologies (agent, SOA, ) are advancing Telecommunication infrastructure is improving Computational power is increasing while the cost for these technologies is decreasing Trent -> Business Activity Management 35 Lecture 1: Introduction to

36 Issues for Successful BI Developing vs. Acquiring BI systems Developing everything from scratch Buying/leasing a complete system Using a shell BI system and customizing it Use of outside consultants? Justifying via cost-benefit analysis It is easier to quantify costs Harder to quantify benefits Most of them are intangibles 36 Lecture 1: Introduction to

37 Issues for Successful BI Security and Privacy Still an important research topic in BI How much security/privacy? Integration of Systems and Applications BI must integrate into the existing IS Often sits on top of ERP, SCM, CRM systems Integration to outside (partners of the extended enterprise) via internet customers, vendors, government agencies, etc. 37 Lecture 1: Introduction to

38 Major BI Tools and Techniques Tool categories Data management Reporting, status tracking Visualization Strategy and performance management Business analytics Social networking & Web 2.0 New/advanced tools/techniques to handle massive data sets for knowledge discovery 38 Lecture 1: Introduction to

39 Major BI Vendors In recent years, the landscape of BI vendors has changed Cognos acquired by IBM in 2008 IBM also acquired SPSS in 2009 Hyperion acquired by Oracle in 2008 Business Objects acquired by SAP in 2009 Microstrategy May be the only independent large BI vendor Others include Microsoft, SAS, Teradata (mostly considered a DW vendor) 39 Lecture 1: Introduction to

40 BI Resources Teradata University Network A great and free academic resource for BI (the available resources include cases, articles, tools including Microstrategy, datasets, exercises, etc. The Data Warehousing Institute (tdwi.org) The OLAP Report (olapreport.com) DSS Resources (dssresources.com) Network (b-eye-network.com) AIS World (isworld.org) Microsoft Enterprise Consortium (enterprise.waltoncollege.uark.edu/mec) 40 Lecture 1: Introduction to