Chapter 1: Decision Support Systems and Business Intelligence — Comprehensive Study Notes

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.
  • Understand an early framework for managerial decision making.
  • Learn the conceptual foundations of the decision support systems (DSS).
  • Describe the business intelligence (BI) methodology and concepts and relate them to DSS.
  • Describe the concept of work systems and its relationship to decision support.
  • List the major tools of computerized decision support.
  • Understand the major issues in implementing computerized support systems.

The Changing Business Environment

  • Companies are moving aggressively to computerized support of their operations => Business Intelligence (BI).
  • Business Pressures–Responses–Support Model.
  • Business pressures are a result of today’s competitive business climate.
  • Responses to counter the pressures and support to better facilitate the process.

The Business Environment Model

  • The model shows how environmental factors drive organizational responses and decisions.
  • Flow: Environmental factors → Organization's responses → Decisions and support.
  • Example factors: Globalization, market conditions, customer demand, competition, regulatory environment, partnerships, real-time response, agility, etc.

Business Environment Factors (Factor Descriptions)

  • Markets
    • Description: Strong competition; expanding global markets; booming electronic markets on the Internet; opportunities for outsourcing with IT support; need for real-time, on-demand transactions.
  • Customer demand
    • Description: Desire for customization; demand for quality, diversity of products, and speed of delivery; customers becoming powerful and less loyal.
  • Technology
    • Description: More innovations, new products, and new services; increasing obsolescence rate; increasing information overload; social networking, Web 2.0 and beyond.
  • Societal factors
    • Description: Growing government regulations and deregulation; a more diversified workforce (older, more women); homeland security concerns; Sarbanes-Oxley and related legislation; increased emphasis on sustainability and corporate social responsibility.

Organizational Responses

  • Be Reactive, Anticipative, Adaptive, and Proactive.
  • Managerial actions may include:
    • Strategic planning; new and innovative business models; restructuring processes; participating in alliances; improving corporate information systems; improving partnerships; encouraging innovation and creativity.

Managerial Actions (Continued)

  • Additional actions include:
    • Improve customer service and relationships; move to electronic commerce (e-commerce); move to make-to-order production and on-demand manufacturing and services.
    • Use new IT to improve communication, data access (discovery of information), and collaboration.
    • Respond quickly to competitors’ actions (pricing, promotions, new products/services).
    • Automate many tasks of white-collar employees and some decision processes.
    • Improve decision making by employing analytics.

Closing the Strategy Gap

  • A major objective of computerized decision support is to reduce the gap between current organizational performance and desired performance (as expressed in mission, objectives, goals) and the strategy to achieve them.

Managerial Decision Making

  • Management is a process by which organizational goals are achieved using resources.
  • Inputs: resources.
  • Output: attainment of goals.
  • Measure of success: ext{success} = rac{ ext{Outputs}}{ ext{Inputs}}.
  • Management ≈ Decision Making: selecting the best solution from two or more alternatives.

Mintzberg's 10 Managerial Roles

  • Interpersonal roles:
    1) Figurehead, 2) Leader, 3) Liaison
  • Informational roles:
    4) Monitor, 5) Disseminator, 6) Spokesperson
  • Decisional roles:
    7) Entrepreneur, 8) Disturbance handler, 9) Resource allocator, 10) Negotiator

Decision Making Process (the scientific approach)

  • Managers usually make decisions by following a four-step process:
    1) Define the problem (or opportunity)
    2) Construct a model that describes the real-world problem
    3) Identify possible solutions and evaluate them
    4) Compare, choose, and recommend a potential solution

Why Use Computerized DSS

  • DSS facilitates decisions via:
    • Speedy computations.
    • Improved communication and collaboration.
    • Increased productivity of group members.
    • Improved data management.
    • Overcoming cognitive limits.
    • Quality support and agility support.
    • Web access: support anywhere, anytime.

A Decision Support Framework – Cont.

  • Degree of Structuredness (Simon, 1977):
    • Highly structured (a.k.a. programmed)
    • Semi-structured
    • Highly unstructured (i.e., non-programmed)
  • Types of Control (Anthony, 1965):
    • Strategic planning (top-level, long-range)
    • Management control (tactical planning)
    • Operational control

A Decision Support Framework (by Gory and Scott-Morton, 1971)

  • Classic framework showing interactions between problem intelligence, design, and choice, with implementation feedback.

Simon's Decision-Making Process

  • Problems or opportunities → Intelligence (environment scanning) → Design (creativity, finding alternatives, analyzing solutions) → Choice (compare and select the best solution) → Implementation (deploy the solution into action).

Computer Support for Structured Decisions

  • Structured problems:
    • Repeated problems with a high level of structure.
    • Examples: make-or-buy decisions, capital budgeting, resource allocation, distribution, procurement, inventory control.
  • For each category, a solution approach is developed (Management Science).

Management Science Approach

  • Also called Operations Research (OR).
  • Five-step MS approach:
    1) Define the problem.
    2) Classify the problem into a standard category.
    3) Construct a model describing the real-world problem.
    4) Identify possible solutions and evaluate them.
    5) Compare, choose, and recommend a potential solution.

Automated Decision Making (ADS)

  • A relatively new approach to supporting decision making.
  • Applies to highly structured decisions.
  • ADS is a rule-based system that provides a solution to repetitive managerial problems in a specific area.
  • Example: simple-loan approval system.

Automated Decision Making (History & Drivers)

  • ADS originated in the airline industry as revenue (yield) management systems.
  • Dynamically price tickets based on actual demand.
  • Today, many service industries use similar pricing models.
  • ADS are driven by business rules.

Computer Support for Unstructured Decisions

  • Unstructured problems can be only partially supported by standard computerized quantitative methods.
  • Often require customized solutions.
  • Benefit from data and information; intuition and judgment may play a role.
  • Computerized communication and collaboration technologies along with knowledge management are often used.

Computer Support for Semi-Structured Problems

  • Solving semi-structured problems may involve a combination of standard solution procedures and human judgment.
  • MS handles structured parts while DSS handles unstructured parts.
  • With proper data and information, a range of alternative solutions and their potential impacts can be analyzed.

Automated Decision-Making Framework

  • Foundations include: DSS theories and sources, artificial intelligence, business processes, and technology.
  • Type: Customized vs Standard decision rules; Automated decision-making systems.

Classical Definitions of DSS

  • Gorry and Scott-Morton (1971): Interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems.
  • Keen and Scott-Morton (1978): DSS couple intellectual resources of individuals with computer capabilities to improve decision quality; supports semistructured problems.

DSS as an Umbrella Term

  • DSS can describe any computerized system that supports decision making in an organization.
  • Examples: organization-wide knowledge management systems; function-specific DSS (marketing, finance, accounting, manufacturing, planning, SCM, etc.).

DSS as a Specific Application

  • In a narrow sense, DSS refers to a process for building customized applications for unstructured or semi-structured problems.
  • Core components of DSS architecture: Data, Model, Knowledge/Intelligence, User, Interface (API and/or user interface).
  • DSS are often created by assembling loosely coupled instances of these components.

High-Level Architecture of a DSS

  • Core components: Data, Models, Knowledge, User interface.
  • Often depicted as Data → Models → Knowledge/Intelligence → User Interface.

Evolution of DSS into Business Intelligence

  • DSS moved from specialist usage to managers and others “whenever, wherever.”
  • Tools enabling BI include OLAP, data warehousing, data mining, intelligent systems, Web delivery (dashboards, portals).
  • The integrated term becomes BI and business analytics.

Business Intelligence (BI)

  • BI is an umbrella term combining architectures, tools, databases, analytical tools, applications, and methodologies.
  • BI’s major objective: enable easy access to data and models to empower managers to conduct analysis.
  • BI helps transform data into information, knowledge, decisions, and action.

A Brief History of BI

  • Gartner coined the term BI in the mid-1990s, but concepts are older:
    • 1970s: MIS reporting (static/periodic reports).
    • 1980s: Executive Information Systems (EIS).
    • 1990s: OLAP, dynamic multidimensional ad-hoc reporting -> BI term.
    • 2005+: AI and data/text mining, web portals/dashboards.
    • 2010s: ongoing evolution.

The Evolution of BI Capabilities

  • Key milestones include: querying/reporting, ETL, data warehouse, EIS/ESS, DSS, OLAP, dashboards, data marts, scorecards, data mining, predictive analytics, portals, etc.
  • Spreadsheets (MS Excel) as an enabling precursor.

The Architecture of BI

  • Four major components:
    • Data Warehouse: large repository of well-organized historical data.
    • Business Analytics: tools for transforming data into information and knowledge.
    • Business Performance Management (BPM): monitoring and analysis of performance.
    • User Interface: dashboards and visual interfaces for access and manipulation.

A High-Level Architecture of BI (Diagrammatic View)

  • Data Sources → Data Warehouse Environment → Business Analytics Environment → Performance & Strategy Environment → User Interface (dashboard/portal)
  • Roles: Technical staff build/access data warehouse; Business users and Managers/Executives consume BI outputs.

Components in a BI Architecture

  • Data Warehouse: repository of historical data.
  • Business Analytics: tools for transformation, mining, analysis.
  • Business Performance Management (BPM): monitoring, measuring, comparing KPIs.
  • User Interface: dashboards/portals enabling interaction with BI components.

Styles of BI

  • Five BI styles (MicroStrategy example):
    1) Report delivery and alerting
    2) Enterprise reporting (dashboards/scorecards)
    3) Cube analysis (slice-and-dice)
    4) Ad-hoc queries
    5) Statistics and data mining

The Benefits of BI

  • Benefits include: accurate information when needed, real-time view of performance.
  • Thompson (2004) survey results:
    • Faster, more accurate reporting (81%)
    • Improved decision making (78%)
    • Improved customer service (56%)
    • Increased revenue (49%)
  • See Table 1.3 for BI analytic applications, business questions they answer, and value.

The DSS–BI Connection

  • Similar architectures: BI evolved from DSS.
  • DSS directly supports specific decision making; BI provides timely information and supports decisions indirectly.
  • BI has executive/strategy orientation (BPM & dashboards); DSS is more analyst-oriented.
  • Most BI systems use commercially available tools; DSS often built from scratch.
  • DSS methodologies/tools originated largely in academia; BI methodologies/tools developed largely by software companies.
  • Many BI tools are also DSS tools (e.g., data mining, predictive analytics).
  • Debates exist about whether DSS equals BI; current view: MSS = BI and/or DSS.

A Work System View of Decision Support (Alter, 2004)

  • Reframe DSS by dropping “systems” and focusing on the decision-support use of any plausible computerized or non-computerized means to improve decision making in a given business context.
  • Work system: a system in which human participants and/or machines perform a business process, using information, technology, and other resources, to produce products and/or services for internal or external customers.

Elements of a Work System

1) Business process: variations in rationale, sequence of steps, or methods.
2) Participants: training, skills, commitment, real-time or delayed feedback.
3) Information: quality, availability, presentation.
4) Technology: data storage/retrieval, models, algorithms, statistical/graphic capabilities, user interaction.
5) Product and services: evaluating potential decisions.
6) Customers: involvement in decision process and clarity of needs.
7) Infrastructure: efficient use of shared infrastructure.
8) Environment: incorporating concerns from surrounding environment.
9) Strategy: fundamentally different operational strategy for the work system.

Hybrid (Integrated) Support Systems

  • Objective: assist management in solving problems faster and better with computing; blending tools to compensate for each other’s weaknesses.
  • Trend: develop hybrid (integrated) support systems.

Types of Integration for Hybrid Systems

  • Use each tool independently for different aspects.
  • Use several loosely integrated tools by transferring data between them.
  • Use several tightly integrated tools; from the user’s perspective, the tools appear as a unified system.
  • Tools can support each other across different tasks in the problem-solving process.

End of the Chapter

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Notes on Formulas and Key References

  • Performance measure example: ext{Performance} = rac{ ext{Outputs}}{ ext{Inputs}}.
  • Degree of Structuredness references: Simon (1977).
  • Control types references: Anthony (1965).
  • Classic DSS definitions: Gorry & Scott-Morton (1971); Keen & Scott-Morton (1978).
  • Evolutionary trace: DSS to BI; BI origins in MIS/EIS/OLAP lineage.
  • Notable industry example: Revenue management in airlines as an early ADS application.