week1

Chapter 1 Slides

Opening Example  Opening Vignette  KONE – minimize downtime and user’s suffering  Solution – IBM Watson IoT Cloud Platform –minimized downtime and shortened repair time

 Changing business environments and evolving needs for decision support and analytics  Big-bet, high-risk decisions.  Cross-cutting decisions, which are repetitive but high risk that require group work

(Chapter 11).  Ad hoc decisions that arise episodically.  Delegated decisions to individuals or small groups.

Decision-Making Process  Four step process

 1. Define the problem (i.e., a decision situation that may deal with some difficulty or with an opportunity).

 2. Construct a model that describes the real-world problem.

 3. Identify possible solutions to the modeled problem and evaluate the solutions.

 4. Compare, choose, and recommend a potential solution to the problem

 Other examples – Quain (2018)- 7 step process

Influence of External and Internal Environments  Technology

 Government  Political  Economic  Sociological and psychological  Environmental

Data and Its Analysis in Decision Making  Organizations and industries use analytics to develop reports do make the best

decisions  Timely  Proactive  Predictive

Technologies for Data Analysis and Decision Support  Group Communication and Collaboration

 Improved data management

 Managing big data

 Analytical support

 Overcoming cognitive limits in processing and storing information

 Knowledge management

 Anywhere and anytime support

Simon’s Process  Three major phases  Intelligence  Design  Choice

Issues in Data Collection  Data are not available. As a result, the model is made with and relies on potentially

inaccurate estimates.  Obtaining data may be expensive.  Data may not be accurate or precise enough.  Data estimation is often subjective.  Data may be insecure.  Important data that influence the results may be qualitative (soft).  There may be too many data (i.e., information overload).  Outcomes (or results) may occur over an extended period. As a result, revenues,

expenses, and profits will be recorded at different points in time. To overcome this difficulty, a present-value approach can be used if the results are quantifiable.  It is assumed that future data will be similar to historical data. If this is not the case, the

nature of the change has to be predicted and included in the analysis

Problem/Design/Choice/Implementation Phases  Problem  Classification  Decomposition  Ownership

 Design  Models

 Choice

 Implementation

Decision Support System Framework  Degree of Structuredness

 Types of Control

 Decision Support Matrix

 Computer Support for Structured, Unstructured, Semistructured Decisions

Characteristics & Capabilities of DSS

Components of a Decision Support System  Data management subsystem

Model management Subsystem  Model base

 MBMS

 Modeling language

 Model directory

 Model execution, integration, and command processor

User Interface Subsystem  Natural language input

 Examples

 Price lookups: “Price 64GB iPhone X.”  Currency conversions: “10 US dollars in euros.”  Sports scores and game times: Just enter the name of a team (“NYC Giants”), and Google

SMS will send the most recent game’s score and the date and time of the next match.

Knowledge based management subsystem  Support any of the other subsystems or act as an independent component. It

provides intelligence to augment the decision maker’s own or to help understand a user’s query so as to provide a consistent answer.

Evolution of Computerized DSS

Framework of Business Intelligence  Definitions of BI

 History of BI

 Architecture of BI

Data warehouse as a foundation of BI  Transaction processing vs. analytic processing

Appropriate Planning and Alignment with business strategy  A center can demonstrate how BI is clearly linked to strategy and execution of strategy.  A center can serve to encourage interaction between the potential business user

communities and the IS organization.  A center can serve as a repository and disseminator of best BI practices between and

among the different lines of business.  Standards of excellence in BI practices can be advocated and encouraged through-out

the company.  The IS organization can learn a great deal through interaction with the user

communities, such as knowledge about the variety of types of analytical tools that are needed.  The business user community and IS organization can better understand why the DW

platform must be flexible enough to provide for changing business requirements.  The center can help important stakeholders like high-level executives see how BI can

play an important role

Analytics  the process of developing actionable decisions or recommendations for actions

based on insights generated from historical data.

What is big data?  Big Data refers to data that cannot be stored in a single storage unit. Big Data

typically refers to data that come in many different forms: structured, un-structured, in a stream, and so forth.

Analytics Examples in Selected Domains  Sports

 Business Office

 Heathcare

 Retail value chain

Artificial Intelligence Overview  AI is based on theories from several scientific fields, and it encompasses a wide

collection of technologies and applications. So, it may be beneficial to look at some of the characteristics of AI in order to understand what it is. The major goal of AI is to create intelligent machines that can do tasks currently done by people. Ideally, these tasks include reasoning, thinking, learning, and problem solving.

 Benefits of AI  Significant reduction in the cost of performing work. This reduction continues over time

while the cost of doing the same work manually increases with time.  Work can be performed much faster.  Work is consistent in general, more consistent than human work.  Increased productivity and profitability as well as a competitive advantage are the major

drivers of AI.

Major AI technologies

Convergence of Analytics and AI  Differences between Analytics and AI

 Why combine intelligent systems

 Big data empowers AI technologies

Overview of the Analytics EcoSystem

Wrap Up  Review the Chapter highlights

 Review the key terms

 Complete the weekly homework