Week 2&3: Overview of Decision Support Systems,Business Intelligence and Analytics

Decision making

is one of the most important activities in organizations in all kind

Business Environmental Factors (Pressures/Opportunities)

Globalization, Customer demand, Government regulations, Market conditions, Competition, Etc.Organization's Responses

Organization's Responses

Strategy, Partners' collaboration, Real-time response, Agility, Increased productivity, New vendors, New business models, Etc.Decisions and Support

Decisions and Support

Analyses, Predictions, Decisions; Integrated computerized decision support; Business intelligence

Markets

Strong competition

Expanding global markets

Blooming electronic markets on the Internet

Innovative marketing methods

Opportunities for outsourcing with IT support

Consumer demand

Need for real-time, on-demand transactions

Desire for customization

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 legislation

Increasing social responsibility of companies

Greater emphasis on sustainability

Be Reactive, Anticipative, Adaptive, and Proactive

Organizational Responses

• Managers may take actions, such as:

  • Employ strategic planning

  • Use new and innovative business models

  • Restructure business processes

  • Participate in business alliances

  • Improve corporate information systems

  • Improve partnership relationships

  • Encourage innovation and creativity

  • 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 (e.g., in pricing, promotions, new products and services)

  • Automate many tasks of white-collar employees Automate certain decision processes

  • Improve decision making by employing analytics

Closing the Strategy Gap

One of the major objectives of computerized decision support 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 to achieve them

Data and Its Analysis in Decision Making

Industry to employ analytics to develop reports on what is happening, predict what is likely to happen and then make decisions

Requirements: Analyze vast stores of data

Computer Applications

• Transaction processing and monitoring activities to problem analysis and solution applications 

• Analytics and BI Tools 

• Cloud-based technologies 

• Data warehousing 

• Data mining 

• Online analytics processing 

• Dashboards 

Technologies for Data Analysis and Support

Growth of hardware, software and network capacities Group Communication and Collaboration Improved data management Managing giant data warehouses and big data Analytical support Knowledge management Anywhere, anytime support Innovation and artificial intelligence

Decision-Making Process (a.k.a. the scientific approach)

• Managers usually make decisions by following a four-step process:

• Define the problem (or opportunity)

• Construct a model that describes the real-world problem

• Identify possible solutions to the modeled problem and evaluate the solutions

• Compare, choose, and recommend a potential solution to the problem

Phases of Decision-Making Process: Simon (1977)

Humans consciously or sub consciously follow a systematic decision-making process

• Intelligence

• Design

• Choice

• Implementation

• Intelligence Phase

• Design Phase

• Choice Phase

• Implementation Phase

Decision-Making/Modeling Process Details

Intelligence Phase

Reality → Simplification / Assumptions

Design Phase

Problem Statement

Choice Phase

Alternatives

Intelligence Phase

• Organization objectives

• Search and scanning procedures

• Data collection

• Problem identification

• Problem ownership

• Problem classification

• Problem statement

Design Phase

• Formulate a model

• Set criteria for choice

• Search for alternatives

• Predict and measure outcomes

• (Validation of the Model goes back to Reality)

Choice Phase

• Solution to the model

• Sensitivity analysis

• Selection to the best (good) alternative(s)

• Plan for implementation

• (Verification, Testing of the Proposed Solution goes back to Reality)

Implementation Phase

• Implementation of the solution

• Success (returns to Reality) or Failure (returns to Intelligence)

Intelligence Phase

• Identification of the organizational goals related to the issues or concerns (i.e lack of web presence)

• Problem occurs because of dissatisfaction of status quo

• Dissatisfaction is the results of a difference between what people expect and what is occurring

• In this phase, a decision maker attempts to determine whether a problem exists, what its symptoms and explicitly define it.

• The existence of a problem can be determined by monitoring and analyzing the organization's productivity level. The measurement of productivity and the construction of a model are based on real data. The collection of data and the estimation of future data are among the most difficult steps in the analysis.

Design Phase

• Finding, analyzing or developing possible courses of actions

• Model is constructed

• Model - a major characteristics of computerized decision support and BI tools.

• Model - is a simplified representation or abstraction of reality

• Model - for mathematical model, the variables are identified and their mutual relationships are established

Choice Phase

• Critical act of decision making

• This includes the:

  • Search for

  • Evaluation of

  • Recommendation of an appropriate solution to a model

decision support system (DSS)

• Computerized — can facilitate decision via:

  • Speedy computations

  • Improved communication and collaboration

  • Increased productivity of group members

  • Improved data management

  • Overcoming cognitive limits

  • Quality support; agility support Using Web; anywhere, anytime support

Decision Support Framework (Gorry and Scott-Morton, 1971)

• Structured

• Semi-structured

• Unstructured

Structured

Operational Control

• Accounts receivable

• Accounts payable

• Order entry

Structured

Managerial Control

• Budget analysis

• Short-term forecasting

• Personnel reports

• Make-or-buy

Structured

Strategic Planning

• Financial management

• Investment portfolio

• Warehouse location

• Distribution systems

Semi-structured

Operational Control

• Production scheduling

• Inventory control

Semi-structured

Managerial Control:

• Credit evaluation

• Budget preparation

• Plant layout

• Project scheduling

• Reward system design

• Inventory categorization

Semi-structured

Strategic Planning

• Building a new plant

• Mergers & acquisitions

• New product planning

• Compensation planning

• Quality assurance

• HR policies

• Inventory planning

Unstructured

Operational Control

• Buying software

• Approving loans

• Operating a help desk

• Selecting a cover for a magazine

Unstructured

Managerial Control

• Negotiating

• Recruiting an executive

• Buying hardware

• Lobbying

Unstructured

Strategic Planning

• R & D planning

• New tech. development

• Social responsibility planning

Structured Decisions

• encountered repeatedly, have a high level of structure

• It is possible to abstract, analyze, and classify them into specific categories e.g., make-or-buy decisions, capital budgeting, resource allocation, distribution, procurement, and inventory control

• For each category a solution approach is developed $\Rightarrow$ Management Science

Unstructured Decisions

• problems can be only partially supported by standard computerized quantitative methods

• They often require customized solutions

• They benefit from data and information

• Intuition and judgment may play a role

• Computerized communication and collaboration technologies along with knowledge management is often used

Semi-structured

• problems may involve a combination of standard solution procedures and human judgment

• Management Science handles the structured parts while DSS deals with the unstructured parts

• With proper data and information, a range of alternative solutions, along with their potential impacts.

Decision Support Systems (DSS)

"Interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems" — Gorry and Scott-Morton, 1971

Decision Support Systems (DSS)

couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semi-structured problems" — Keen and Scott-Morton, 1978

Decision Support Systems (DSS)

refers to a process for building customized applications for unstructured or semi-structured problems

Components of the DSS Architecture

Data,

Model,

Knowledge/Intelligence,

User,

Interface

Decision Support Systems (DSS)

— often is created by putting together loosely coupled instances of its components

• Data Management Subsystem

• Model Management Subsystem

• User Interface Subsystem

• Knowledge base Management System

Components of Decision Support System (Subsystems)

Data Management Subsystem

Includes the database that contains the data (internal and/or external: ERP/POS, Legacy, Web, etc.); Database management system (DBMS); Can be connected to a data warehouse; Data Directory; Query Facility

Model Management Subsystem

Includes financial, statistical, management science or other quantitative model that provide the system's analytical capabilities and appropriate software management.

Elements: Model base, Modeling language, Model directory, Model execution, integration and command processor, External Models

User Interface Subsystem

Interface, Application interface, User Interface, Graphical User Interface (GUI). DSS — includes Portal, Graphical icons, Dashboard, Color coding, Interfacing with PDAs, cell phones, etc. Interfaces Manager (user)

Knowledge base Management System

Incorporation of intelligence and expertise. Knowledge components: Expert systems, Knowledge management systems, Neural networks, Intelligent agents, Fuzzy logic, Case-based reasoning systems, and so on. Often used to better manage the other DSS components. Interacts with Organizational Knowledgebase.

1970s (Decision Support Systems)

: Routine Reporting, AI/Expert Systems, Decision Support Systems

1980s (Enterprise/Executive IS)

: On-Demand Static Reporting, Relational DBMS

1990s (Business Intelligence)

: Executive Information Systems, Dashboards, Scorecards, Data Warehousing, Enterprise Resource Planning

2000s (Analytics)

: Business Intelligence, BPM, Data/Text/Web Mining, Software as a Service

2010s (Big Data)

: Social Network/Media Analytics, In-Memory/In-Database/MPP, Cloud, Big Data Analytics

2020s (Automation)

: Robotics, Smart Robo-Assistants, AI/Deep Learning, IoT/Sensors, Automated Analytics

Evolution of DSS into Business Intelligence

• DSS is a content-free expression (different things to different people)

• Use of DSS moved from specialist to managers, and then whomever, whenever, wherever

• Enabling tools like OLAP, data warehousing, data mining, intelligent systems, delivered via Web technology have collectively led to the term "business intelligence" (BI) and "business analytics"

Business Intelligence (BI)

is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies

Business Intelligence (BI)

a content-free expression, so it means different things to different people

Business Intelligence (BI)

major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis

Business Intelligence (BI)

helps transform data, to information (and knowledge), to decisions and finally to action

Gartner Group (mid-1990s)

The term BI was coined by the

• Data warehouse

• Business analytics

• Business performance management (BPM)

• User interface

The Architecture of BI

Data warehouse

: a large repository of well-organized historical data

Business analytics

: tools that allow transformation of data into information and knowledge

Business performance management (BPM)

: allows monitoring, measuring, and comparing key performance indicators

User interface

: allows access and easy manipulation of other BI components

Styles of BI (MicroStrategy, Corp.)

• Report delivery and alerting

• Enterprise reporting

• Cube analysis

• Ad-hoc queries

• Statistics and data mining

Business Intelligence (BI)

The ability to provide accurate information when needed, including a real-time view of the corporate performance and its parts

DSS-BI Connection

!FAMILIARIZE!

First: Their architectures are very similar because BI evolved from DSS

Second: DSS directly support specific decision making, while BI provides accurate and timely information, and indirectly support decision making

Third: BI has an executive and strategy orientation, especially in its BPM and dashboard components, while DSS, in contrast, is oriented toward analysts

Fourth: Most BI systems are constructed with commercially available tools and components, while DSS is often built from scratch

Fifth: DSS methodologies and even some tools were developed mostly in the academic world, while BI methodologies and tools were developed mostly by software companies

Sixth: Many of the tools that BI uses are also considered DSS tools (e.g., data mining and predictive analysis are core tools in both)

Differences/Perspective: Although some people equate DSS with BI, these systems are not, at present, the same. Some people believe that DSS is a part of BI—one of its analytical tools; others think that BI is a special case of DSS that deals mostly with reporting, communication, and collaboration (a form of data-oriented DSS). BI is a result of a continuous revolution and, as such, DSS is one of BI's original elements. (MSS = BI and/or DSS)

• Descriptive

• Diagnostic

• Predictive

• Prescriptive

Types of Analytics (The Four Questions of Analytics)

Descriptive

What happened?

Descriptive

Summarizes historical and current data using KPIs, reports, dashboards, charts, and period comparisons. (Past / Present)

Descriptive

KPI dashboards, Scorecards and reports, Trends and period comparisons, Totals, averages, percentages

Diagnostic

Why did it happen?

Diagnostic

Investigates drivers and root causes using drill-down, segmentation, discovery, and comparisons. (Past / Present)

Diagnostic

Drill-down and drill-through, Cohort or segment analysis, Data discovery and mining, Root-cause analysis

Predictive

What is likely next?

Predictive

Uses historical patterns, statistics, regression, machine learning, or AI to estimate future outcomes. Produces forecasts and probabilities — not certainties. (Present $\rightarrow$ Future)

Predictive

Regression and statistical models, Machine learning / AI, Risk scoring, Forecasting and scenario models

Prescriptive

What should we do?

Prescriptive

Recommends actions using predictions, optimization, scenarios, business rules, and constraints. Turns insight and prediction into recommended action. (Future + Action)

Prescriptive

Optimization algorithms, What-if and scenario analysis, Business rules and constraints, Decision-support recommendations