1

The Systems Perspective of a DSS

Slides modified from Marakas

A DSS is really not complete without considering one ………..

Chapter 13 in Marakas

Today’s philosophical thought from Aristotle ………..“The whole is more than the sum of its parts, the part is more than a fraction of the whole”

2

What is a System?

System – an interrelated set of elements, with an identifiable boundary that work together to achieve a common objective/goal

– e.g. a car, any other machine, humans– And so a problem too has components as part of its whole

A closed system seldom interacts with the environment to receive input or generate output. Also called stable or mechanistic.

– Often highly structured and routine in operation– Self-sustaining largely

An open system is less structured and operates in a self-organizing manner. Also called adaptive or organic.

– Self-organising / adapting to environment– Business and information systems

A subsystem is one of the “interrelated set of elements” noted above.

3

Comparison of Open and Closed Systems

4

The Subsystem – Functional Decomposition

The process of breaking a system down into its component subsystems is called functional decomposition.

By using a structured decomposition, we can study a subsystem independently from the larger system.

There can be many layers of decomposition. We stop at the layer that provides the most beneficial and useful information to us.

We can also apply this to decisions by breaking them down into smaller, more focused decisions.

5

E.g. functional decomposition of a house

This is not dissimilar to our thinking with Juniper in terms of processes and sub-processes – and the same for decisions

6

DSS in the Context of Information Systems

A DSS is a specific type of information system that has its own set of characteristics:

Data stores are distributed and there are usually several of them.

Most data flow into the DSS from external applications. A DSS generally takes, rather than gives, data.

A DSS usually has few end users and there is a narrow boundary of communication with them.

These users have a high level of control.– DM can be external or internal to DSS

7

Context Diagrams of Two Design Approaches

DecisionMaker Outside

Boundary

DecisionMaker Inside

Boundary

8

Processes and Data Flows Within the Generalized DSS

9

Design Specifics Depend on Several Questions

What are the specific objectives of the DSS application?– i.e. the specific decision and outputs required

What are the external sources and recipients? How will the DSS communicate with them?

What is the exact nature of the data flows between the DSS and these external entities?

What data will reside within the boundary of the DSS application? When will external data be stored within the boundary?

What are the detailed temporal processes contained within the DSS application?

10

Information Quality Issues In DSS Design

The more information we possess, the less uncertainty we endure about the outcome. Finer granularity in the information leads to greater clarity.

It is not enough to simply gather more information, however, because we may not have the ability to process it all.

In general, relatively structured problems require less information than unstructured problems.

Since better-quality information costs more to produce, the quality of information becomes a cost-benefit analysis between the cost of information and the sensitivity of the decision.

“Garbage in garbage out!”

11

Information Quality Service Levels

A Service Level Agreement (SLA) is negotiated Information can be considered a form of service to an end

user. The degree to which information contributes to the decision process depends on its quality.

The quality of information is related to how closely it matches its intended purpose.

– E.g a speed gauge in a car is of sufficient quality to establish how fast we are going and further divisions are not necessary

To assess the quality of information needed, we must first ascertain the sensitivity of the decision to the quality of information available.

– Up to DSS designer to assess information quality levels needed and to assess the various sources to obtain it

12

Factors In Determining Information Quality

13

Factors In Determining Information Quality

An agreed-upon set of factors can be used to determine both the level of the information required and the quality of information available.

Relevance – can it be directly applied?– how closely does the information match the need?– Filtering out (best guess?) information through experience– Err on the side of too much rather than too little (cost?)

Correctness – does it represent reality? – Or “how similar is it to reality”?– e.g. = 3.1416 may be sufficient for our purposes but it is

not accurate– Decision maker judges the correctness according to needs

Accuracy – is the info ‘close enough’ to true?– Error tolerance: 90% confidence interval– Are we close enough to NOT effect the outcome of the

decision?

14

Factors In Determining Information Quality

Precision – what is the maximum accuracy?– too little can lead to poor results– excessive may increase cost with no added value– Stored data in DSS may be higher than their

representation to the decision maker

Accurate/imprecise

inaccurate/imprecise

accurate/precise

inaccurate/precise

= true value

15

Factors In Determining Information Quality

Completeness – are things missing? Timeliness - When is it needed? When is it available? When was it

collected?– Value of information deteriorates with time– E.g. don’t issue cash based on balance 2 days ago!

Usability – can the user figure out what to do?– Low cognitive effort– Quality linked to presentation format (e.g. a graph)

Accessibility – can the user get to it? Consistency – is it stored in predictable way?

– E.g. Are measurement units the same in different scenarios? Conformity to Expected Meaning – is it presented the way the user needs

it (i.e. meaningful)? One user or many users? Cost – what is the total cost of acquiring it? It is not free!

– Dependent on trade-off in other factors– E.g. increased timeliness may decrease accuracy– So the economics of the DSS depend on all of these factors

16

Defining the DSS Information System Architecture

When we speak of IS architecture, we focus on these three high-level issues:

Interoperability – the degree to which information can be delivered to the point of use in an efficient manner.

Compatibility – the degree to which the DSS will work in harmony with other platforms and data stores in an organization.

Scalability – the degree to which it can be expanded to accommodate an increase in processing requirements.

17

Elements of a Typical DSS Architecture

The DSS is often defined as a subset of the organization’s overall IS, yet still contains most of the same elements as its “parent”

A robust and well-defined architecture should contain details about the following elements:

Databases Models

End users End user tools

Platforms Communications

Administration Tools

18

A generic DSS Architecture

Networking and telecommunications facilities

Hardware and Operating System Platforms

DSS User interface

End User Tools

DBMS Model developmentModel Management

Database ModelBase

External data End Users

Programmers

19

Questions to Ask in Determining Platform Requirements

Do current policies constrain or dictate platform choice? What is the size and distribution of the end user

community? Will all users employ the same basic set of applications? To what extent must the existing organizational system

be able to share data with the DSS platform? Do the necessary development tools exist within the

organization now or must new tools be bought? Read-only access to databases? Or full write access too? What is the expected processing power required?

– For scalability Will the system need an administrator? Or will the end

user be responsible?

20

The Role of the Internet in DSS Development and Use

The Internet is the world’s database and is rapidly becoming the world’s leading source of information.

Despite reliance on the Internet, the same basic issues of design, information quality and suitability must be addressed.

The Internet has several advantages: (1) a typical DSS end user can be anywhere, (2) the cost of becoming “DSS connected” is lower, and (3) almost anyone with a computer is a potential end user and the learning curve is reduce.

There are also some disadvantages: (1) access is often slow, (2) good web designers are not necessarily good DSS designers, and (3) we are just now developing programming languages robust enough to handle the most serious DSS applications.

21

Key Point Summary

Definition of a system– Open and closed systems– Functional decomposition

General concepts of a DSS– architecture– Data flows and system boundaries– End users

Issues of information quality– Accessibility, completeness etc

Interoperability, compatibility and scalability issues of DSS’s

The role of the internet