Chapter 12
Business Intelligence and Decision Support Systems
Goals of the Chapter
The primary objective of this chapter is to recognize the importance of data, the management issues that relate to it, and its life cycle. Other objectives include relating data management to multimedia and document management, explaining the concept of data warehousing, data mining, analytical processing, and knowledge discovery management.
An Overview
Section 12.1 – The Need for Business Intelligence – The section serves as an overview of Business Intelligence and its use in business. It discusses the problems associated with disparate data stores where data are not integrated into a single reporting system. The section discusses the technologies involved in Business Intelligence and the vendors involved. It also talks about predictive analytics, alerts and decision support.
Section 12.2 – BI Architecture, Reporting and Performance Management – This section discusses the modes of data extraction and integration into a standardized, usable and trustworthy one. It also discusses the different types of reporting systems available to organizations, data mining, query and analysis. The section provides an insight into Business Performance Management (BPM) as a way for business managers to know if their organizations are achieving their strategic goals
Section 12.3 – Data, Text and Web Mining and BI Search – This section discusses data mining technology, tools, and techniques. Information types, data mining applications, text mining, and web mining are explored. There is also a discussion of the failures of data mining.
Section 12.4 – Managers and Decision-Making Processes – This section delves into the roles played by business managers and how these roles are supported by business information systems. It talks about the decision support tools that are used for business decision making.
Section 12.5 – Decision Support Systems – This section discusses the systems associated with business decision making by managers within organizations. It discusses the characteristics, capabilities, structure and components of decision support systems. It also discusses the special tools for decision support.
Section 12.6 – Automated Decision Support – Some decision support systems require some automation to address repetitive managerial problems. This section discusses the characteristics, benefits and applications that are associated with automated decision support systems.
Practical Applications
IT@Work
IT at Work 12.1 – eHarmony Uses Predictive Analytics for Compatibility Matching – The case discusses the successful use of predictive analytics in the matchmaking industry, matching couples using compatibility variables.
IT at Work 12.2 – U.K. Fashion Chain Uses BI and DSS to Predict and Replenish Intelligently – This case discusses how Bank, a U.K. based fashion chain uses BI tools to analyze which trends are taking off and uses the information gained to make sure goods are always in stock. Using historical data, the system forecasts future buying patterns, allowing managers to order the required merchandise for its stores.
IT at Work 12.3 - Ben and Jerry’s keeps track of its Pints – This case discusses the use of intelligence software as a resource optimization tool. The ice cream maker is able to track, understand, and manage information on the thousands of consumer responses it receives on its products and promotional activities. Through daily customer feedback analysis, Ben & Jerry’s are able to identify trends and modify its marketing campaigns and its products to suit consumer demand.
IT at Work 12.4 – Predictive Analysis Help Save Gas and Protect Green – Real-time traffic flows are analyzed to maximize the efficiency of flow through at highly congested time. The importance of real-time analytics that are adaptive is made relevant in this case.
IT at Work 12.5 – Using DSS to Determine Risk – This case shows how DSS played an important role in determining the risk involved in a proposed joint venture with a petrochemical company to develop a chemical plant.
Questions for Review
1. What are three reasons companies invest in BI?
Managers could not get the information they needed for decision making, planning, or to assess performance
2. What three conditions made getting a unified view of individual customers impossible?
Data silos, non-standardized data, and disparate information systems
3. What is meant by a “trusted view” of the data? Why wouldn’t data be trusted?
When data are not integrated into a single reporting system, there is no trusted real-time view of what is going on across the enterprise. With the integration of data, managers get a much more trusted view of their data which comes from one single database. Data needs to be trusted for effective decision making, planning and performance review of their business.
4. What are three data deficiencies that frustrate decision makers because of disparate data?
Getting information too late, getting data at the wrong level of detail, either too detailed or too summarized and not being able to coordinate with other departments across the enterprise
5. Explain predictive analytics. What are two business pressures driving adoption of predictive analytics?
Predictive analytics is the branch of data mining that focuses on forecasting trends and estimating probabilities of future events.
The business pressures driving adoption of predictive analytics include
• Increase customer loyalty and satisfaction
• Improve growth strategies
6. What are operational, tactical, and strategic decisions?
Operational decisions ensure that day-to-day operations are running correctly and efficiently.
Tactical decisions ensure that existing operations and processes are in alignment with business objectives and strategies.
Strategic decisions create a road map for sustained success and business growth.
7. Define data extraction and data integration, and explain why they are needed.
Data extraction is the pulling of data of interest from various sources such as OLAP, ERP, CRM, SCM, other applications, legacy and local data stores, and the Web. The extracted data will lack standardization and consistency because different systems use different data terminology or field names. Integrating data involves obtaining data from multiple sources, reformatting it, and loading it into a central repository, such as another database, a data mart, or a data warehouse, for analysis or to another operational system to support a business process.
Both of these processes are needed to convert unusable data into one that can be effectively used for reporting, analysis, and so on
8. What is data latency? How does giving users the ability to create their own reports reduce data latency? What is the age of fresh data?
The speed with which data is captured is referred to as data latency. When users are given access to creating their own reports, they are able to have direct access to the data and not have to rely on other people to provide that service.
It is a measure of data “freshness,” specifically, data that are less than 24 hours old.
9. Explain the capabilities of dashboards and scorecards. Why are they important BI tools?
Dashboards and scorecards are interactive user interfaces as well as reporting tools. Dashboards provide easy-to-use access to enterprise data and help companies track business performance and optimize decision making. Dashboard users are typically supervisors and specialists, whereas scorecard users are executives, managers, and staff. Information is presented in graphs, charts, and tables that show actual performance vs. desired metrics for at-a-glance views of the health of the organization. Dashboards and scorecards give managers and executive a quick look into large amounts of data and allow for faster decision making.
10. What is the benefit to end users of having ad hoc query capabilities?
Ad hoc queries allow users to request information that is not available in periodic reports, as well as to generate new queries or modify old ones with significant flexibility over content, layout, and calculations.
11. What is a multidimensional view of data? Sketch such a view in 3D and label the multiple dimensions for a service company.
There are many software tools for users to create queries and analyze data. These tools give users fast, simple, and controllable access to data to perform ad hoc analysis in multiple dimensions. The multidimensional view allows users to see multiple views of data and if drawn, will look like a 3-dimentional drawing of a cube.
12. Define business performance management (BPM). What is the objective of BPM?
Business performance management (BPM) requires that managers have methods to quickly and easily determine how well the organization is achieving its goals and objectives, and whether or not all of the organization is aligned with the strategic direction. The objective of BPM is to optimize the overall performance of an enterprise.
13. What is text mining? Give three examples of text that would be mined for intelligence purposes.
Text mining helps organizations find the “hidden” content of documents, including additional useful relationships, relate documents across previously unnoticed divisions and group documents by common themes.
14. How does text mining differ from search?
Text mining is not the same thing as a search engine on the Web. In a search, we are trying to find what others have prepared. With text mining, we want to discover new patterns that may not be obvious or known.
15. What is Web mining? Give three examples of Web content that would be mined for intelligence purposes.
Web mining is the application of data mining techniques to discover actionable and meaningful patterns, profiles, and trends from Web resources. Web mining is used to understand customer behavior, evaluate a Web site’s effectiveness, and quantify the success of a marketing campaign.
16. Describe one advantage and one disadvantage of data mining tools.
Data mining tools are interactive, visual, and understandable, and work directly on the data warehouse of the organization.
However, some techniques deployed by data mining tools may be far beyond the understanding of the average business analyst or knowledge worker
17. List three data mining applications for identifying business opportunities.
• Retailing and sales- Predicting sales, determining correct inventory levels and distribution schedules among outlets, and loss prevention.
• Banking- Forecasting levels of bad loans and fraudulent credit card use, credit card spending by new customers, and which kinds of customers will best respond to and qualify for new loan offers.
• Manufacturing and production- Predicting machinery failures; finding key factors that control optimization of manufacturing capacity.
• Healthcare- Correlating demographics of patients with critical illnesses; developing better insights on symptoms and their causes and how to provide proper treatments.
• Broadcasting- Predicting which programs is best to air during prime time, and how to maximize returns by interjecting advertisements.
• Marketing- Classifying customer demographics that can be used to predict which customers will respond to a mailing or Internet banners, or buy a particular product, as well as to predict other consumer behavior.
18. Why does a decision involve two or more alternatives?
A decision refers to a choice made between two or more alternatives. Two broad decision categories are problem solving and opportunity exploiting. One option will not amount to a decision because one will not have to make a choice between one and the other.
19. Why is it not possible to fully automate business decisions?
Not all business decisions can be automated simply because not all of them are routine. Some of these, especially those that are unstructured, rely on intuition, judgment, and experience which cannot be automated.
20. What is the difference between decisions made by mid-level managers and decisions made by top-level managers?
Mid-level managers make fairly routine decisions, and these can be fully automated. The job of top managers is the least routine and therefore the most difficult to automate.
21. Identify and explain the three phases of decision making.
The three phases of the decision making process includes:
Intelligence Phase- Managers examine a situation, then identify and define the problem.
The Design Phase- decision makers construct a model that represents and simplifies the problem or opportunity. This is done by making assumptions and expressing the relationships among all variables. The model is then validated, and decision makers set criteria for the evaluation of alternative potential solutions that are identified
The Choice Phase- involves selecting a solution, which is tested “on paper.”
22. Why are models used in decision making? What is an inherent risk of using models in decision making?
A decision model is a simplified representation, or abstraction of reality. Simplicity is helpful because a lot of complexity may be irrelevant to a specific problem. One simplification method is making assumptions. The risk when using assumptions is when they are wrong, then the entire foundation for the analysis is flawed.
23. Give an example of a structured, an unstructured, and a semi-structured decision. Which of these types of decisions can be optimized? Why?
Structured decisions refer to routine and repetitive problems for which standard solutions exist. Examples are formal business procedures, cost minimization, profit maximization and algorithms
Unstructured decisions involve a lot of uncertainty for which there are no definitive or clear-cut solutions. With unstructured decisions, for example, each decision maker may use different data, assumptions, and processes to reach a conclusion
Semi-structured decisions fall between the polar positions. Most of what are considered to be true decision support systems are focused on semi-structured decisions. Examples of semi-structured problems are trading bonds, setting marketing budgets for consumer products, and performing capital acquisition analysis.
Structured decisions can be optimized because information systems can help in this process. Decision support systems can help with optimization for semi-structured decisions.
24. Describe an automated decision support (ADS) system
Automated decision support (ADS) systems are rule-based systems that automatically provide solutions to repetitive managerial problems.
25. What are business rules? What role do they play in automating the decision-making process?
Automating the decision-making process is usually achieved by capturing a manager’s expertise in a set of business rules that are embedded in a rule-driven workflow. These rules determine what action needs to be taken, based on the results of the evaluation.
26. What types of decisions are best suited for ADSs? Why?
ADSs are most suitable for repetitive decisions that are made frequently, or responses that are needed rapidly using online information. This allows these repetitive decisions to be made faster.
27. Explain two examples of ADS applications
Product or service customization- Purchasers can customize a product or service such as a PC or cell phone plan. The ADS then configures the most appropriate final product (service) and its cost. Dell Computer is using this approach.
Yield or price optimization- Airlines use automated decision-making applications to set prices based on seat availability and hour or day of purchase, also known as revenue management or yield management. A method called price optimization is used to maximize profitability in real estate rentals.
Questions for Discussion
1. Discuss the strategic benefits of business analytics.
Business analytics provides the models, which are formulas or algorithms, and procedures to BI. Using business analytics software, the user can make queries, request ad hoc reports, or conduct analyses. There are predictive analytic tools designed for hands-on use by managers who want to do their own forecasting and predicting. These tools assist managers in condensing large amounts of data into concise, usable form for faster decision making thereby staying ahead of the competition.
2. Will BI replace the business analyst?
BI will in the future get more complicated and their tools will get better at interpreting trends in data. This trend will end up replacing some of the functions performed by business analysts for many companies. These tools will however not be able to replace every function of a human business analyst due to the nature of certain forms of data that will require the use of human perception and judgment. This discussion should dwell on the difference between computer-generated responses and the capacity for individual judgment. Understanding cannot be replaced by calculation speed.
3. Differentiate predictive analysis from data mining. What do they have in common?
The major components of BI are data warehouse and/or marts, predictive analytics, data mining, data visualization software, and a business performance management system. Predictive analysis uses different algorithms to forecast results and relationships among variables as well as to identify data patterns. Data mining is one of the tools. The main link between predictive analysis and data mining is that data mining is a tool used in predictive analysis.
4. Describe the concepts underlying Web-mining and Web-analytics.
Web mining is the application of data mining techniques to discover actionable and meaningful patterns, profiles, and trends from Web resources. The term Web mining is used to refer to both Web-content mining and Web-usage mining. Web-content mining is the process of mining Web sites for information. Web-usage mining involves analyzing Web access logs and other information connected to user browsing and access patterns on one or more Web localities. analytics is a component of Web mining that sifts through data to identify patterns of behavior that suggest, for example, what offers customers might respond to in the future, or which customers you may be in danger of losing.
5. Relate competitive analysis to BI.
Competitive analysis analyzes online algorithms, in which the performance of an online algorithm is compared to the performance of an optimal offline algorithm that can view the sequence of requests in advance. Business intelligence is an umbrella term that includes architectures, tools, databases, and applications that have been implemented over several decades under different names by various researchers and vendors.
6. Why is real time BI becoming critical?
Real Time BI means having access to information and data as it happens in real time. Computer systems are getting more sophisticated and are able to capture and transmit data and information to any corner of the world in real time. More and more companies are taking advantage of real time access to business analytics to stay ahead. In the future, companies are going to spend more and more money to advance their systems in that regard and all others will have to follow suit to keep up tithe the competition.
7. Discuss the need for real-time computer support.
Computer support to groups is designed to improve the process of making decisions in groups, which can meet face-to-face or online. The support increases the effectiveness of decisions and reduces the wasted time and other negative effects of face-to-face meetings. Computer support in the whole decision making process generally quickens the process of decision making.
8. What could be the biggest advantages of a mathematical model that supports a major investment decision?
Mathematical models allow the analysis and comparison of a very large, sometimes near-infinite number of possible alternative solutions. With today’s advanced technology and communications, managers frequently have a large number of alternatives from which to choose. Investment decisions depend on very complex mathematical models to help decide the profitability of an investment and the potential ROI and investment managers need to be very sure about what they are putting money into before they make those decisions.
9. Your company is considering opening a branch in China. List several typical activities in each phase of the decision (intelligence, design, choice, and implementation).
At the intelligence phase, managers will examine a situation, then identify and define the problems that need to be tackled. In the design phase, decision makers construct a model that represents and simplifies the opportunity. This is done by making assumptions and expressing the relationships among all variables. The model is then validated, and decision makers set criteria for the evaluation of alternative potential solutions that are identified. The choice phase involves selecting a solution, which is tested “on paper”. Once this proposed solution seems to be feasible, the last phase is implemented. Successful implementation results in resolving the original problem or opportunity.
10. How is the term model used in this chapter? What are the strengths and weaknesses of modeling?
Modeling was used to show a graphical representation of a problem or opportunity that helps decision makers see a picture or a quick snapshot of the task at hand. Models help make quick decisions to stay competitive in the marketplace. Models on the other hand make use of assumptions and these usually may not provide the best source of detailed information in certain cases that require precise detail.
11. American Can Company announced that it was interested in acquiring a company in the health maintenance organization (HMO) field. Two decisions were involved in this act: (1) the decision to acquire an HMO, and (2) the decision of which one to acquire. How can a DSS, ES, or ESS be used in such a situation?
A decision support system (DSS) combines models and data to solve semi-structured and some unstructured problems with intensive user involvement. A DSS is an approach that can improve the effectiveness of decision making, decrease the need for training, improve management control, facilitate communication, reduce costs, and allow for more objective decision making. DSSs deal mostly with unstructured problems. DSSs will help the company see a good view of the performance of any of the companies they ant to acquire and thus make a better decision on which one to acquire.
12. Discuss how GDSSs can negate the dysfunctions of face-to-face meetings (Chapter 4).
A group decision support system (GDSS) is an interactive computer-based system that facilitates the solution of semi-structured and unstructured problems when made by a group of decision makers by concentrating on the process and procedures during meetings. The objective of a GDSS is to support the decision process. Some dysfunctions of face-to-face meetings include lack of new ideas, time-consuming process, inappropriate influences and some members may be afraid to speak up. A GDSS allows contribution from all members of the meeting and increases participation.
13. Read the CIGNA case (Online Minicase 1) and answer the following questions:
a. Describe the motivation for developing the eCare system.
Behavioral Health’s business processes for making approval determinations were supported in different locations using several different software applications and certain rules in use sometimes conflicted with each other, and knowledge was lost due to employee turnover. In addition, the rules were in inflexible if-then statements. Accessing and changing the rules was a slow, cumbersome, and expensive process.
b. Explain the role of the intelligent systems and their potential benefits in the case.
Intelligent systems in this case were to help automate decision-making situations and in this case make approval determinations. Therapists submit requests for authorization of benefits over the Internet and receive, in many cases, an immediate machine-generated approval.
c. What are the major difficulties you can anticipate in the process of developing and using the system?
Several aspects of using the system that could prove difficult will include the interpretation of expert knowledge into business rules and also the human acceptance and use of the system, There could be some resistance to the system from technologically challenged users.
d. How are these systems different from traditional analytical systems described earlier in the chapter?
These systems basically think for themselves and usually remove the human analytical aspect of analysis.
14. Explain why even an intelligent system can fail.
Expert systems, one form of intelligent system, can advise someone to do something illegal, unethical, etc because they don’t have human decision-making capabilities built in. Since these systems can rely exclusively on information systems and not humans, they can fail.
Exercises and Group Assignments
The assignments considered in this chapter require a substantial effort to review literature, research for new information, conduct surveys, collect and analyze the information, and prepare necessary reports. Since these questions are not extremely difficult to solve, I will not attempt to answer them here.
Internet Exercises
These exercises require current and relevant web material. Instructors are encouraged to review these sites before using them, or to include their own unique internet-based examples. Check online at Wiley.com for up to date web materials.
Minicase
Lexmark International improves Operations with BI
The case discusses Lexmark’s choice in BI solution, and identifies some of the complexities in information flow. Students should be aware of the complications that the acquisition of a standard application presents with its inclusion in a non-standard environment.
1. Identify the challenges faced by Lexmark regarding information flows.
Being in an extremely competitive business, Lexmark needs detailed, accurate, and timely information for decision support and strategy implementation. This is especially important when it comes to data flow between Lexmark and its retail partners. The most important information is detailed by item, sales volumes, and inventory levels.
2. How were information flows provided before and after implementation of the BI system?
Before BI implementation: Information flows were slow, inefficient, error-ridden, lacked sufficient detail, and did not reach mobile users easily. In delivering the data, results were often copied from spreadsheets and pasted into reports, typically taking four days or longer to produce answers to common business questions. Sales representatives out in the field had to dial into the intranet. Once a connection was established, analysts and sales representatives had to write SQL queries and navigate the mainframe to generate reports for management, some of which were based on inaccurate, week-old data. As a result, information flows could not support the competitive needs of the company.
After BI implementation: The system enables buyers, financial analysts, marketing analysts, regional managers, merchandisers, and field sales representatives to analyze sales and inventory data from their desktop or mobile devices. The system, which is fed by IBM’s data warehouse, provides users with the ability to track sales performance and inventory levels of every Lexmark product at each of the thousands of retail stores worldwide. A large number of reporting and analysis tools are available in the software, including extensive reports, statistical models (over 50), and visualization techniques. Almost all Lexmark employees are using the systems. Novice workers are able to use the information to improve how they do their job. Management can better understand business trends and make appropriate strategic decisions.
3. Identify decisions supported by the new system.
Lexmark reported that decision makers now receive timely, accurate, and detailed information. It helped to identify sales opportunities, increased partner loyalty, eliminated inventory problems, and increased profitability.
The new system supported decisions regarding customer relationships, forecasted product demand, sales leads, supply chain management, inventory management, profitability analysis, and financial management.
4. How can the system improve customer service?
Lexmark sells commodity products, which can be bought from many other competitors. So customer service is critical to its success. With the new system, Lexmark has a better understanding of consumer demand by country and store, so they can better decide, for example, on pricing and promotions. The system can improve customer service by providing intelligence to identify imminent inventory shortages, and avoid them before they impact customers. The system can also be used to identify the most profitable customers; set up automatic shipments; and respond quickly to customer needs.
5. Go to MicroStrategy’s Web site (microstrategy.com) and examine the capabilities of Retail BI System. Prepare a list.
The Retail BI System, which is called SQL-Retail Business Intelligence System, can be found at http://www.microstrategy.com/Solutions/ByPartner/MI9_MMS/. It comes pre-configured with a wide range of standard reports and metrics, designed specifically to measure the performance of all aspects of a retail business, including corporate performance versus plans, store performance, buyer performance, warehouse performance, vendor performance, merchandise performance, and associate performance. Its capabilities are:
• It is a complete management system plus an executive information system for “point and click” information retrieval.
• It provides retailers with the capability to manage centralized inventory control of stores and distribution centers, as well as retail merchandising functions such as procurement and retail price management.
• The system also provides sophisticated automated replenishment of stores and warehouses together with allocation and distribution control functions designed to optimize retail performance and reduce the burden of inventory control management.

6. Go to the SAS Web site (sas.com) and find their Retail Intelligence product (take the interactive tour). Compare it with MircoStrategy’s product. Also, compare it with Oracle Retail (oracle.com/retek).
• SAS Retail Intelligence Solutions are tailored to meet the unique needs of the retail industry by delivering valuable insights into customer behavior, store performance, merchandise performance, demand forecasting, marketing campaigns, and supply-chain costs. You can do this without spending a lot of time and resources force-fitting generic tools into a retail environment. SAS Retail Intelligence Solutions comprise seven components that can be implemented individually or collectively for maximum advantage: SAS Campaign Management for Retail, SAS Customer Insight for Retail, SAS Demand Forecasting for Retail, SAS Performance Reporting for Retail, SAS Strategic Performance Management, SAS Supply Chain Costing for Retail, and SAS Transaction Insight for Retail.
• Oracle Retail solutions help you achieve a streamlined and consolidated financial and operational infrastructure, so you can better utilize resources that differentiate your company from the competition. Oracle Retail solutions can help you improve information integrity for the extended retail enterprise, enable an insight-driven enterprise, enhance collaboration with advanced supply chain planning, empower workforce excellence, and drive excellent customer shopping experiences. Their components are: Oracle Retail Business Intelligence, Oracle Retail Corporate Administration, Oracle Retail In-Store Operations, Oracle Retail Merchandise Operations Management, Oracle Retail Merchandise Planning, Oracle Retail Supply Chain Planning and Execution, and Oracle Retail Profit Optimization.
• Comparing SAS, Oracle, and MicroStrategy’s products, their capabilities are very similar, and they also have similar components.

Problem-Solving Activity:
Dashboards at Eden, Inc.
4. Give an informed response to the questions found at the bottom of page 3, that relate to Strong’s review of the nature of Eden’s dashboards. Those questions are:
Did he do the right thing?
John Strong asked his friend, a recent ASU MSIM graduate, Pat Webster about the dashboard situation and got the “silo metrics” response. Strong started to reconsider the nature of Eden’s dashboards. He clearly did the right thing by consulting a knowledgeable person outside the company and taking the response into consideration. Did they take accountability too far?
Not yet. Several VPs immediately used their dashboards to come to the conclusion that delivery problems under the control of COO Edwards were the root cause. This shows the performance visibility power of the dashboards.
Isn’t accountability a good thing?
When designing dashboards, most consultants, and as a result many managers, fall into a trap: They think the point of metrics is to hold people accountable, and so, they design a business dashboard whose gauges are each tied to a branch of the organizational chart, or where individual manager results roll up to the whole organization's results.
At the dashboard level of analysis, no one manager is logically accountable for how well the measures come out. What a manager can do is define goals in ways that support the organizational strategy, including business alignment goals, and then one or more metrics to assess progress toward the goals. Would you redesign the dashboards? If so, how? If not, what would be your expectation of COO Edward’s future behaviors?
The answer is no at this moment. COO Edwards can work with CEO John Strong and other VPs to evaluate the nature of Eden’s dashboards. A well-designed business dashboard helps you understand how healthy your organization is, and whether it's progressing at a fast enough pace toward its destination.
From the case description text, COO Ken Edwards utilizes a dashboard that provides insights into ongoing operations. His dashboard also incorporates models that support and simulate the impact of different decision scenarios. For example, he can simulate the impact of switching suppliers, and he can even see – in real-time – ongoing assembly line operations.
In order to keep costs low, there were frequent supplier switches. This implies that late deliveries might be the norm rather than the exception. COO Ken Edwards’ dashboard can provide extra-enterprise information in a timely fashion by simulating the impact of switching suppliers. The current dashboard may already be capable of solving the delivery problem for COO Ken Edwards.
5. Answer the questions at the end of the case on page 6. Those questions are:
What technical approaches might be suggested for this?
The technical approaches might be suggested for this are interorganizational information systems of types such as B2B trading systems, B2B support systems, groupware, and/or shared databases.
What would the steps be to begin talking with the suppliers about this prospect?
It begins with discussions on the recent delivery problem and Eden’s desire of improving the critical success factor - expectations management. It should also involve discussions of possible major IOS infrastructure technologies to be deployed, such as EDI, extranets, XML, Web services, enterprise application integration, and/or extended ERP.
Would this be a standards effort, or a simple file transfer type of approach, or are there other ways this could be handled?
Since the inputs to the dashboard from Eden’s suppliers could be automated, the technical approaches to be taken by Eden need to be in the category of virtual corporations. A standards effort or a simple file transfer type of approach cannot accomplish the virtual corporation goal. The most efficient way of doing this is clearly the IOS solution. By connecting the information systems of business partners, IOSs enable business partners to reduce the costs of routine business transactions, improve the quality of the information flow by reducing or eliminating errors, compress cycle time in the fulfillment of business transactions, eliminate paper processing and its associated inefficiencies and costs, and make the transfer and processing of information easy for users.

Summary
This chapter has focused on business intelligence to make various decisions. As the volume of available data is growing, it has become increasingly important to acquire and store it appropriately, and to incorporate a strategy of sharing the data all across the enterprise. This chapter has discussed various methods to accomplish this such as data warehousing, data mining and decision support systems.
What’s Next?
The next chapter discusses Information Technology Strategy and Planning and their importance in business. The chapter will also recognize the challenges of IT business alignment and attempt to address them. The chapter will also look at IT strategic role within organizations and their value to businesses. Finally the chapter will take a look at outsourcing as a strategy used by many companies to reduce cost.