1. What are the different types of decisions and how does the decision-making process work?

• List and describe the different levels of decision making and decision-making constituencies

in organizations. Explain how their decision-making requirements differ.

• Distinguish between an unstructured, semistructured, and structured decision.

• List and describe the stages in decision making

The different levels in an organization (strategic, management, operational) have different decision-making requirements.

Strategic management: As part of a strategic planning process top executives

· develop overall organizational goals, strategies, policies, and

· monitor the strategic performance of the organization and its overall direction in the political, economic, and competitive business environment

Tactical management: Business unit managers and business professionals in self-directed teams

· develop short- and medium-range plans, schedules, budgets and specify policies, procedures, and business objectives for their sub-units of the company, and

· allocate resources and monitor the performance of their organizational sub-units, including departments, divisions, process teams, project teams, and other workgroups.

Operational management: Operating managers and members of self-directed teams

· develop short-range plans (e.g. weekly production schedules), and

· direct the use of resources and the performance of tasks according to procedures and within budgets and schedules they establish for the teams and other workgroups of the organization.

Decisions can be structured, semistructured, or unstructured, with structured decisions clustering at the operational level of the organization and unstructured decisions at the strategic level. Decision making can be performed by individuals or groups and includes employees as well as operational, middle, and senior managers.

· Unstructured decisions are those in which the decision maker must provide judgment,evaluation and insight to solve the problem. Each of these decisions is novel, important and nonroutine and there is no well-understood or agreed on procedure for making them.

· Structured decisions are repetitive and routine, and involve a definite procedure for handling them.

· Semi structured are decisions that have elements of both, and where only part of the problem has a clear cut answer provided by an accepted procedure.

There are four stages in decision making: intelligence, design, choice, and implementation. Systems to support decision making do not always produce better manager and employee decisions that improve firm performance because of problems with information quality, management filters, and organizational culture.

  1. Intelligence

• Discovering, identifying, and understanding the problems occurring in the organization

  1. Design

• Identifying and exploring solutions to the problem

  1. Choice

• Choosing among solution alternatives

  1. Implementation

• Making chosen alternative work and continuing to monitor how well solution is working

  1. How do information systems support the activities of managers and management decision


• Compare the descriptions of managerial behavior in the classical and behavioral models.

• Identify the specific managerial roles that can be supported by information systems.

The classical model suggests that managers perform five classical functions. These functions are planning, organizing, coordinating, deciding, and controlling. Although the classical model describes formal managerial functions, it does not provide a description of what managers actually do. The behavioral models suggest that managerial behavior is less systematic, more informal, less reflective, more reactive, less well-organized, and somewhat frivolous. The behavioral models differ from the classical model in that managers perform a great deal of work at an unrelenting pace, managerial activities are fragmented, managers prefer speculation, managers prefer oral forms of communication, and managers give the highest priority to maintaining a diverse and complex web of contacts.

Contemporary research looking at the actual behavior of managers has found that managers’ real activities are highly fragmented, variegated, and briefin duration and that managers shy away from making grand, sweeping policy decisions. Information technology provides new tools for managers to carry out both their traditional and newer roles, enabling them to monitor, plan, and forecast with more precision and speed than ever before and to respond more rapidly to the changing business environment. Information systems have been most helpful to managers by providing support for their roles in disseminating information, providing liaisons between organizational levels, and allocating resources. However, information systems are less successful at s upporting unstructured decisions. Where information systems are useful, information quality, management filters, and organizational culture can degrade decision making.

Information systems support the liaison, nerve center, disseminator, spokesperson, and resource allocator roles. Currently information systems do not support the figurehead, leader, entrepreneur, disturbance handler, and negotiator roles. Information systems are the strongest at the informational role and the weakest at the interpersonal and decisional roles.

3.How do business intelligence and business analytics support decision making?

• Define and describe business intelligence and business analytics.

• List and describe the elements of a business intelligence environment.

• List and describe the analytic functionalities provided by BI systems.

• Compare two different management strategies for developing BI and BA capabilities.

Business intelligence and analytics promise to deliver correct, nearly real-time information to decision makers, and the analytic tools help them quickly understand the information and take action. A business intelligence environment consists of data from the business environment, the BI infrastructure, a BA toolset, managerial users and methods, a BI delivery platform (MIS, DSS, or ESS), and the user interface. There are six analytic functionalities that BI systems deliver to achieve these ends: predefined production reports, parameterized reports, dashboards and scorecards, ad hoc queries and searches, the ability to drill down to detailed views of data, and the ability to model scenarios and create forecasts.

Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions. BI encompasses a wide variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.

The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; driving new revenues; and gaining competitive advantages over business rivals. BI systems can also help companies identify market trends and spot business problems that need to be addressed.

BI data can include historical information, as well as new data gathered from source systems as it is generated, enabling BI analysis to support both strategic and tactical decision-making processes. Initially, BI tools were primarily used by data analysts and other IT professionals who ran analyses and produced reports with query results for business users. Increasingly, however, business executives and workers are using BI software themselves, thanks partly to the development of self-service BI and data discovery tools.

Business intelligence combines a broad set of data analysis applications, including ad hoc analysis and querying, enterprise reporting, online analytical processing (OLAP), mobile BI, real-time BI, operational BI, cloud and software as a service BI, open source BI, collaborative BI and location intelligence. BI technology also includes data visualization software for designing charts and other infographics, as well as tools for building BI dashboards and performance scorecards that display visualized data on business metrics and key performance indicators in an easy-to-grasp way. BI applications can be bought separately from different vendors or as part of a unified BI platform from a single vendor.

BI programs can also incorporate forms of advanced analytics, such as data mining, predictive analytics, text mining, statistical analysis and big data analytics. In many cases though, advanced analytics projects are conducted and managed by separate teams of data scientists, statisticians, predictive modelers and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.

Business intelligence data typically is stored in a data warehouse or smaller data marts that hold subsets of a company’s information. In addition, Hadoop systems are increasingly being used within BI architectures as repositories or landing pads for BI and analytics data, especially for unstructured data, log files, sensor data and other types of big data. Before it’s used in BI applications, raw data from different source systems must be integrated, consolidated and cleansed using data integration and data quality tools to ensure that users are analyzing accurate and consistent information.

In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, business analysts and data management professionals; business users often are also included to represent the business side and make sure its needs are met in the BI development process. To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality to end users on an incremental and iterative basis. Doing so can enable companies to put BI features into use more quickly and to refine or modify development plans as business needs change or new requirements emerge and take priority over earlier ones.

Sporadic usage of the term business intelligence dates back to at least the 1860s, but consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella category for applying data analysis techniques to support business decision-making processes. What came to be known as BI technologies evolved from earlier, often mainframe-based analytical systems, such as decision support systems and executive information systems. Business intelligence is sometimes used interchangeably with business analytics; in other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.

Business analytics (BA) is the practice of iterative, methodical exploration of an organization’s data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making.

BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.

Business analytics examples:

Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.

The second area of business analytics involves deeper statistical analysis. This may mean doing predictive analytics by applying statistical algorithms to historical data to make a prediction about future performance of a product, service or website design change. Or, it could mean using other advanced analytics techniques, like cluster analysis, to group customers based on similarities across several data points. This can be helpful in targeted marketing campaigns, for example.

Specific types of business analytics include:

Descriptive analytics, which tracks key performance indicators to understand the present state of a business;

Predictive analytics, which analyzes trend data to assess the likelihood of future outcomes; and

Prescriptive analytics, which uses past performance to generate recommendations about how to handle similar situations in the future.

4.How do different decision-making constituencies in an organization use business intelligence?

• List each of the major decision-making constituencies in an organization and describe the types of decisions each makes.

• Describe how MIS, DSS, or ESS provide decision support for each of these groups.

• Define and describe the balanced scorecard method and business performance management.

–Unstructured: Decision maker must provide judgment, evaluation, and insight to solve problem

–Structured: Repetitive and routine; involve definite procedure for handling so they do not have to be treated each time as new

–Semistructured: Only part of problem has clear-cut answer provided by accepted procedure

There are four kinds of systems for supporting the different levels and types of decisions, Management information system (MIS) provides routine reports and summaries of transaction-level data to middle and operational level managers to provide answers to structured and semi-structured decision problems.

· Decision-support system(DSS) provide analytical models or tools for analyzing large quantities of data for middle managers who face semi-structured decision situations.

· Executive support systems(ESS) are systems that provide senior management, making primarily unstructured decisions, with external information(news, stock analyses, and industry trends) and high-level summaries of firm performance.

· In other word, MIS, DSS, and ESS provide information and knowledge to different people and levels in the firm, operational employees, middle managers, and senior executives

Senior managers:

–Make many unstructured decisions

–For example: Should we enter a new market?

•Middle managers:

–Make more structured decisions but these may include unstructured components

–For example: Why is order fulfillment report showing decline in Minneapolis?

•Operational managers, rank and file employees

–Make more structured decisions

5.What is the role of information systems in helping people working in a group make decisions more efficiently?

• Define a group decision-support system (GDSS) and explain how it differs from a DSS.

• Explain how a GDSS works and how it provides value for a business.

Group decision support system (GDSS) technology supports project collaboration through the enhancement of digital communication with various tools and resources. These types of programs are used to support customized projects requiring group work, input to a group and various types of meeting protocols.

General process steps of group decision support systems are group brainstorming, classification, prioritization, planning, assessment, documentation, and resolution. The process always involves a facilitator who designs the work space and guides the team. GDSS enhance group decision making through this general process and can then be customized to serve clients in ways that are unique to their businesses. For example, in 1989 GroupSystems, the first company to offer GDSS software, developed a product based on the research of Dr. Jay Nunamaker. The developed software was tailored to specific needs of IBM and the U.S. Navy, in both cases giving a tangible structure for collaboration and enhancing group communication, which resolved issues related to peer dynamics and information flow.