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Business Intelligence: A Managerial Approach (2nd Edition)

Business Intelligence: A Managerial Approach (2nd Edition). Chapter 1: Introduction to Business Intelligence. Learning Objectives. Understand today’s turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting

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Business Intelligence: A Managerial Approach (2nd Edition)

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  1. Business Intelligence:A Managerial Approach(2nd Edition) Chapter 1: Introduction to Business Intelligence

  2. 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/business analytics methodology Understand the major issues in implementing business analytics

  3. Introduction • Business environment is changing, and its become more complex (pressures). • Force them to respond quickly. • To take a quick decisions they need a relevant amount of data, information and knowledge.

  4. Changing Business Environments and Computerized Decision Support • The Business Pressures-Responses-Support Model • The business environment • Organizational responses: be reactive, anticipative, adaptive, and proactive • Computerized support • Closing the Strategy Gap One of the major objectives of BI 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 for achieving them

  5. Changing Business Environments and Computerized Decision Support

  6. Business Environment Factors • FACTORDESCRIPTION • Markets 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 • Consumer Desire for customization • demand Desire for quality, diversity of products, and speed of delivery • Customers getting powerful and less loyal • Technology More innovations, new products, and new services • Increasing obsolescence rate • Increasing information overload • Social networking, Web 2.0 and beyond • Societal 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

  7. 1.2 A Framework for Business Intelligence (BI) • business intelligence (BI) A conceptual ( umbrella) framework for decision support. It combines architecture, databases (or data warehouse), analytical tools and applications . • BI Objective: enable interactive access to data to enable manipulation of data and to give business managers to take a good decision.

  8. 1.2 A Framework for Business Intelligence (BI) • The process of BI is based on the transformation of data to information, then to decisions, and finally to actions.

  9. A Framework for Business Intelligence

  10. 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” • 2005+ Inclusion of AI (Artificial Intelligent) and Data/Text Mining capabilities; Web-based Portals/Dashboards • 2010s - yet to be seen

  11. A Framework for Business Intelligence (BI) • The Origins and Drivers of Business Intelligence • Organizations are being compelled to capture, understand, and harness their data to support decision making in order to improve business operations • Managers need the right information at the right time and in the right place

  12. A Framework for Business Intelligence (BI) • BI’s Architecture and Components • Data Warehouse • Business Analytics • Business Performance Management (BPM) • User Interface

  13. A Framework for Business Intelligence (BI)

  14. A Framework for Business Intelligence (BI) • BI’s Architecture and Components 1) Data Warehouse (Data Sources) • Data obtained from operational systems needed to support decision making. • the data warehouse included only historical or current data that were organized and summarized, so end users could easily view or manipulate data and information.

  15. A Framework for Business Intelligence (BI) • BI’s Architecture and Components 2 ) Business Analytics -a collection of tools for manipulating, mining, and analyzing the data in the data warehouse; • Create on-demand reports and queries and analyze data (originally called Online Analytical Processing – OLAP) • Automated decision systems • Data Mining: looks for hidden patterns in a collection of data which can be used to predict future behavior.

  16. A Framework for Business Intelligence (BI) • BI’s Architecture and Components 3) Business performance management (BPM) applications and methodology ,BPM extends the monitoring, measuring, and comparing of sales, profit, cost, profitability, and other performance indicators by introducing the concept of management and feedback. (Monitoring , measuring and comparing )

  17. A Framework for Business Intelligence (BI) • BI’s Architecture and Components 4) User Interface: Dashboards and Other Information Broadcasting Tools • Dashboards A visual presentation of critical data for executives to view. It allows executives to see hot spots in seconds and explore the situation • Examples of dashboards and scorecards: http://www.idashboards.com/?gclid=CIDDrpLR05QCFQNaFQodSWDQkQ

  18. Time savings Single version of truth Improved strategies and plans Improved tactical decisions More efficient processes Cost savings Faster, more accurate reporting Improved decision making Improved customer service Increased revenue Many benefits are intangible A Framework for Business Intelligence (BI) • The Benefits of BI

  19. Automated Decision Making(ADS) • It’s a rule based systems that provide a solution, usually in one functional area E.g.( finance, manufacturing ) to a specific repetitive managerial problem. • Its used in the Airline industry, dynamicly price ticket based on demands.

  20. Event-Driven Alerts • Its an example of ADS, which is a warning or action that is activated when a predefined or unusual event occur. • For example: credit card comp. make a predictive analysis models to identify cases possible fraud.

  21. 1.3 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 and leaders(middles)  Partnership between customers and providers

  22. 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

  23. 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)

  24. 1.4 Transaction Processing VersusAnalytic Processing (OLTP Vs OLAP) • Transaction (OLTP) 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  The main goal is to have high efficiency

  25. Transaction Processing VersusAnalytic Processing • Online analytic processing (OLAP) systems are involved in extracting information from data stored by OLTP systems and Analyze them.  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

  26. 1.5 Successful BI Implementation • Impelementing BI can be lengthy, expensive and failure prone. • The Typical BI User: the successful of BI must benefit to the enterprise as whole. • one important characteristic of a company that excels in its approach to BI is proper appreciation for different classes of potential users.

  27. 1.5 Successful BI Implementation • Appropriate Planning and Alignment with 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 should help execute the business strategy and not be an impediment for it!

  28. 1.5 Successful BI Implementation • Real-time, On-demand BI • The demand for “real-time” BI is growing! • Traditional BI use a static data but Real time BI use a dynamic online data.

  29. Issues for Successful BI 1) 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?

  30. Issues for Successful BI 2) Justifying via cost-benefit analysis  It is easier to quantify costs  Harder to quantify benefits  Most of them are intangibles

  31. Issues for Successful BI 3) Security and Privacy  Still an important research topic in BI  How much security/privacy? 4) Integration of Systems and pplications  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.

  32. 1.6 Major BI Tools and Techniques • Tool categories ( table 1.3)  Data management (DBMS)  Reporting, status tracking (OLAP)  Visualization (DASHBOARD)  Strategy and performance management (BPM)  Business analytics ( DATA MINING)  Social networking & (Web 2.0)  New/advanced tools/techniques to handle massive data sets for knowledge discovery

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