Ch12.ed wk9businessintelligenceanddecisionsupportsystem

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Management Information Technology focusing on business intelligence (BI) and decision support system (DSS).

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  • Systems help decision makers collect, compile analyze raw data using business models to respond & make decisions. Illustrates the value of BI (business intelligence), data analytics, & DSS for day-to-day operational decision-making & competitive advantage.
  • Informational slide. Company financials who the increases in net profit, sales growth of 6.2% & income growth of 7.6% in 2007. Management has made a science out of monitoring the metrics that allow it to understand when, where, & why consumers buy beer. Insights have allowed the company to post double-digit gains for 20 straight quarters.
  • Article includes trends, vendor lists, etc. This is a great opportunity to bring in discussion about the difficulties associated with integrating KM systems throughout an organization. Sometimes the power of culture is underestimated. Trust may be nonexistent making this an impossible task.
  • Planning system development effectively at the outset is extremely important so that needs at all levels may be considered. Effective planning will help to prevent (minimize) data silo creation, & increase overall efficiency.
  • It is easy to see how strategies are less effective without BI tools. Discuss how leaders & followers might champion such initiatives. Working together in groups to promote change. Experts should be given incentives to help integrate & execute change.
  • Informational slide. Core BI functions & features are listed in this table.
  • Click images for hot links to home pages. Each has a unique product offering; many similarities. These are major organizations that are seeking customers for their respective enterprise systems.
  • Use this graph in discussion of how all of these pressures are impacted by actions of others throughout the organization.
  • Discuss some event-driven alerts within the experience of the students & actions that would ensue from analyses of the data . Inventory level monitoring will necessitate reordering. How might these alerts be built into business processes from what has been discussed in class? Credit card balance payoff, for instance, might trigger follow up that customer may be getting ready to cancel the card, or to take on additional debt such as to purchase a home.
  • A sample performance dashboard is displayed in this figure. In class, it is interesting to look at other examples which can be manipulated so that students can see how & why the indicators change. Software can be configured to alert staff to unusual events & to automatically trigger defined corrective actions.
  • Students may be engaged in lively discussion of examples from their own experiences from each category.
  • Informational slide. Figure illustrates basic BI components that support operational decisions in real-time & tactical & strategic decisions. Operational raw data are commonly kept in corporate or operational databases.
  • Informational slide. Illustration of how a BI system works.
  • Data latency – speed in which data is captured.
  • Valuable capabilities of dashboards. How might these capabilities be exploited for maximum effectiveness by a management team? Full implementation for full benefit.
  • Challenge students to finds ways to improve upon this dashboard application.
  • Facilitate discussion of importance in ad hoc capability in terms of real-time decision making.
  • Informational slide. The relationship between BPM & other components can be seen in this figure. The objective of BPM is strategic – to optimize the overall performance of an enterprise. By linking performance to corporate goals, decision makers can use the day-to-day data generated throughout their organization to monitor KPIs & make decisions that make a difference.
  • It is never wise to ignore analytical tools such as these for successful business operations.
  • Text analytics transforms unstructured text into structured text data. That text data can then be searched, mined & trends discovered. Discuss application in businesses most familiar to students.
  • How is IT of benefit in these processes? Increased accuracy of data. Quicker access to data.
  • Discuss types of decisions, such as scheduling, that may be fairly easily automated.
  • Without IT, this isn’t possible.
  • IT speeds up the decision making process. Generally will increase effectiveness. Like anything, IT is only as good as what goes into it.
  • Task students to find other capabilities.
  • Informational slide. Data are entered from the sources on the left side & the models from the right side in this figure. Knowledge can also be tapped from the corporate knowledge base. As more problems are solved, more knowledge is accumulated in the organizational knowledge base.
  • Discuss how each of these can be turned into successes. Start with sponsor visibility. Communicate personal benefits in supporting such initiatives (what’s in for me). Do not underfund. Communicate successes along the way. Directly tie to profitability & within the control to some extent of everyone.
  • Ch12.ed wk9businessintelligenceanddecisionsupportsystem

    1. 1. Chapter 12 Business Intelligence and Decision Support Systems Information Technology for Management Improving Performance in the Digital Economy 7 th edition John Wiley & Sons, Inc. 12-1
    2. 2. Chapter Outline <ul><li>12.1 The Need for Business Intelligence (BI) </li></ul><ul><li>12.2 BI Architecture, Reporting and Performance Management </li></ul><ul><li>12.3 Data, Text, and Web Mining and BI Search </li></ul><ul><li>12.4 Managers and Decision Making Processes </li></ul>12-2
    3. 3. Chapter Outline (cont’d) <ul><li>12.5 Decision Support Systems </li></ul><ul><li>12.6 Automated Decision Support (ADS) </li></ul><ul><li>12.7 Managerial Issues </li></ul>12-
    4. 4. Learning Objectives <ul><li>Identify factors influencing adoption of business intelligence (BI) and business performance management (BPM). </li></ul><ul><li>Describe data mining, predictive analytics, digital dashboards, scorecards, and multidimensional data analysis. </li></ul><ul><li>Identify key considerations for IT-support of managerial decision-making. </li></ul><ul><li>Understand managerial decision making processes, the decision process, and types of decisions. </li></ul>12-
    5. 5. Learning Objectives – cont’d <ul><li>5. Describe decision support systems (DSSs), benefits, and structure. </li></ul><ul><li>Recognize the importance of real-time BI and decision support for various levels of information workers. </li></ul><ul><li>Be familiar with automated decision support, its advantages, and areas of application. </li></ul>12-
    6. 6. <ul><li>Problems – declining market. Saturation of existing market. </li></ul><ul><li>Solution – wireless capabilities to provide managers with data that are analyzed immediately to provide actionable feedback to maximize sales. </li></ul><ul><li>Results –gained decisive edge & outsmarted its rivals. Data used as strategic weapon. </li></ul>12-
    7. 7. Table 12.1 12-
    8. 8. 12- 12.1 The Need for Business Intelligence (BI)
    9. 9. (E)xtract (T)ransform (L)oad Tools <ul><li>E – involves tools for extracting the data from source systems (silos). </li></ul><ul><li>T – involves converting (transforming) the data into standardized formats. </li></ul><ul><li>L – involves loading & integrating data into a system (such as a data warehouse). </li></ul>12- Check out this great article for much, much more about the topic – ETL: Extract - Transform - Load (and data management and integration)
    10. 10. Table 12.2 12- Sources: Adapted from Oracle (2007) and Imhoff (2006).
    11. 11. Risks with Disparate Data <ul><li>Responsiveness requires intelligence which requires trusted data & reporting systems. </li></ul><ul><li>Silos arise creating decisions based upon inaccurate, incomplete, possibly outdated data. </li></ul>12- * Data that are too late * Data that are wrong level of detail-too much or too little * Directionless data * Unable to coordinate with departments across enterprise * Unable to share data in a timely manner
    12. 12. Table 12.3 12-
    13. 13. Business Intelligence Technologies <ul><li>1990s primarily associated with back office workers & operations such as accounting, finance & human resources. </li></ul><ul><li>2000s expanded to enterprise data to include needs of managers & executives. </li></ul><ul><li>Vendors offered advanced analytic, decision support, easy-to-use interfaces, & improved data visualization tools. Web-based delivery became common-place. </li></ul><ul><li>Evolved from reporting to predicting. </li></ul>12-
    14. 14. BI Vendors 12- Business intelligence – BIG business
    15. 15. Power of Predictive Analytics, Alerts & DSS <ul><li>Real-time view of the data </li></ul><ul><li>Reactive to proactive with respect to future </li></ul><ul><li>Improved data quality </li></ul><ul><li>Shared, common vision of business activity benefitting key decision makers across enterprise </li></ul><ul><li>Simple to view KPIs </li></ul><ul><li>Informed, fast decision making </li></ul><ul><li>Complete, comprehensive audit trails </li></ul>12-
    16. 16. Figure 12.1 12- Top five business pressure driving the adoption of predictive analytics. (Data from Aberdeen Group.)
    17. 17. Figure 12.2 12- Real-time alerts triggered by customer-driven events.
    18. 18. Figure 12.3 12- Click here for a plethora of dashboard examples! Sample performance dashboard.
    19. 19. Table 12.2 12-
    20. 20. Figure 12.4 12- Basic BI components.
    21. 21. Figure 12.5 12- How a BI system works.
    22. 22. Business Intelligence Solutions <ul><li>Must be able to access enterprise data sources such as TPS, e-business & e-commerce processes, operational platforms & databases. </li></ul><ul><li>Needed for real-time decision making. </li></ul><ul><li>Enhanced operational understanding capabilities. </li></ul><ul><li>Improved cost control & customer relationship management. </li></ul>12-
    23. 23. 12- 12.2 BI Architecture, Reporting, and Performance Management
    24. 24. Data Extraction & Integration <ul><li>Many sources such as OLAP, ERP, CRM, SCM, legacy & local data stores, the Web all lacking standardization & consistency. </li></ul><ul><li>ETL tools provide data for analyses to support business processes. </li></ul><ul><li>Central data repository with data security & administrative tools for information infrastructure. </li></ul>12-
    25. 25. Enterprise Reporting Systems <ul><li>Provide standard, ad hoc, or custom reports. </li></ul><ul><li>95% of Fortune 500 rely on BI to access information & reports they need. </li></ul><ul><li>Reduces data latency. </li></ul><ul><li>Decreases time users must spend collecting the data; increases time spent on analyzing data for better decision-making. </li></ul>12-
    26. 26. Dashboards & Scorecards <ul><li>Dashboards are typically operation & tactical in application & use. </li></ul><ul><li>Scorecard users are executive, manager, staff strategic level in application & use. </li></ul>12-
    27. 27. Table 12.4 12-
    28. 28. 12- Check out this great example of a marketing dashboard used at BMW!
    29. 29. Figure 12.6 12- Multidimensional view of sales revenue data.
    30. 30. Business Performance Management <ul><li>Requires methods to quickly & easily determine performance versus goals, objectives & alignment strategies. </li></ul><ul><li>Relies on BI analysis reporting, queries, dashboards & scorecards. </li></ul><ul><li>Objective is strategic – to optimize overall performance of an organization. </li></ul>12-
    31. 31. Figure 12.7 12- Business performance management (BPM) for monitoring and assessing performance.
    32. 32. Table 12.5 12-
    33. 33. 12- 12.3 Data, Text, and Web Mining and BI Search
    34. 34. Text-Mining <ul><li>Content that is mined include unstructured data from documents, text from email messages & log data from Internet browsing. </li></ul><ul><li>May be major source of competitive advantage. </li></ul><ul><li>Needs to be codified with XML & extracted to apply predictive data mining tools to generate real value. </li></ul><ul><li>Comprises up to 80% of all information collected. </li></ul>12- Click link for an informative article in cio.com – Text Analytics: Your Customers are Talking About You
    35. 35. Advantages & Disadvantages of Data Mining <ul><li>Tools that are interactive, visual, understandable, & work directly on data warehouse of organization. </li></ul><ul><li>Simpler tools used by front line workers for immediate & long-term business benefits. </li></ul><ul><li>Techniques may be too sophisticated or require extensive knowledge & training. </li></ul><ul><li>May require expert statistician to utilize effectively, if at all. </li></ul>12-
    36. 36. 12- 12.4 Managers and Decision Making Processes
    37. 37. Figure 12.8 12- Manager’s decision role.
    38. 38. Managers Need IT Support from DSS Tools <ul><li>Scenarios, alternatives & risks are many. </li></ul><ul><li>Time is always critical consideration & stress level is high. </li></ul><ul><li>Requires sophisticated analysis. </li></ul><ul><li>Geographically dispersed decision makers with collaboration required. </li></ul><ul><li>Often requires reliable forecasting. </li></ul>12-
    39. 39. Automating Manager’s Job <ul><li>Routine decisions by mid-level managers (frontline employees) may be automated fairly easily & frequently. </li></ul><ul><li>Automation of routine decisions leaves more time for supervising, training & motivating nonmanagers. </li></ul><ul><li>Top level managerial decision making is seldom routine & very difficult to automate. </li></ul>12-
    40. 40. IT Available to Support Managers (MSS) <ul><li>DSS - indirect support – discovery, communication & collaboration with web facilitation. </li></ul><ul><li>DSS – provide support primarily to analytical, quantitative types of decisions. </li></ul><ul><li>ESS – early BI – supports informational roles of executives. </li></ul><ul><li>GDSS – supports managers & staff working in groups, remotely or closely. </li></ul><ul><li>Common devices – PDAs, Blackberrys, iPhones. </li></ul>12-
    41. 41. Figure 12.9 12- IT support for decision making.
    42. 42. Figure 12.10 12- Phases in the decision-making process.
    43. 43. Decision Modeling & Models <ul><li>Decision model – simplified representation, or abstraction of reality. </li></ul><ul><li>Simplicity is key. </li></ul><ul><li>Based upon set of assumptions. </li></ul><ul><li>Requires monitoring & adjustment periodically as assumptions change. </li></ul><ul><li>Modeling – virtual experiments reduce cost, compress time, manipulate variables, reduces risk. </li></ul>12-
    44. 44. Framework for Computerized Decision Analysis <ul><li>Structured – routine & repetitive problems. </li></ul><ul><li>Unstructured – lots of uncertainty, no definitive or clear-cut solutions. </li></ul><ul><li>Semistructured – between the extremes. Most true DSS are focused here. </li></ul>12-
    45. 45. 12- 12.5 Decision Support Systems
    46. 46. DSS & Managers <ul><li>Need new & accurate information. </li></ul><ul><li>Time is critical. </li></ul><ul><li>Complex organization for tracking. </li></ul><ul><li>Unstable environment. </li></ul><ul><li>Increasing competition. </li></ul><ul><li>Existing systems could not support operational requirements. </li></ul>12-
    47. 47. Table 12.6 12-
    48. 48. Characteristics & Capabilities - DSS <ul><li>Sensitivity analysis for “what if” & goal-seeking strategy setting. Increases system flexibility & usefulness. </li></ul><ul><li>Basic components – database, model base, user interface, users & knowledge base. </li></ul>12-
    49. 49. Figure 12.11 12- Conceptual model of DSS and its components.
    50. 50. 12- 12.6 Automated Decision Support (ADS)
    51. 51. ADS <ul><li>Rule-based systems with automatic solutions to repetitive managerial problems. </li></ul><ul><li>Closely related to business analytics. </li></ul><ul><li>Automating the decision-making process is usually achieved by capturing manager’s expertise. </li></ul><ul><li>Rules may be part of expert systems or other intelligent systems. </li></ul>12-
    52. 52. Characteristics & Benefits of ADS <ul><li>Rapidly builds business rules to automate or guide decision makers, & deploys them into almost any operating environment. </li></ul><ul><li>Injects predictive analytics into rule-based applications, increasing their power & value. </li></ul><ul><li>Combines business rules, predictive models & optimization strategies flexibly into enterprise applications. </li></ul>12-
    53. 53. ADS Applications - Examples 12- Customizing products & services for customers Revenue yield management Uses filtering for handling & prioritizing claims effectively
    54. 54. 12- 12.7 Managerial Issues
    55. 55. Why BI Projects Fail <ul><li>Failure to recognize as enterprise-wide business initiatives. </li></ul><ul><li>Lack of sponsorship. </li></ul><ul><li>Lack of cooperation. </li></ul><ul><li>Lack of qualified & available staff. </li></ul><ul><li>No appreciation of negative impact on business profitability. </li></ul><ul><li>Too much reliance on vendors. </li></ul>12-

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