Models and Modeling

Models and Modeling Chapter 3

Chapter 3 Models and Modeling • 3.1. Definitions • 3.2. Models as Aid to Understanding • 3.3. Early Methods (Pre-Modeling) • 3.4. Models in Systems Development • 3.5. Some Early Models

3.1 Definitions Data Information Model Abstraction

Data • What comes first into your mind when hear the word “Data?” • Information • Numbers • Bits • Bytes • Facts • All of these are right . . .

Definition of Data • “Data” is the plural of the Latin “Datum” meaning “Fact.” • Note statisticians usually say “The data are . . “ • Programmers typically say “The data is . . . ” • So “Data” means “Facts” • It is prefer to call them “Raw Facts” since they are not yet processed in any way

3.1 Definitions Data Information Model Abstraction

 Information • What is the difference between • “data” • and • “Information?”

 Information • Definition: • Informationis derived from Data by some form of processing which makes it usefulin some way, typically for making decisions.

3.1 Definitions Data Information Model Abstraction

Smaller Looks the same Made of different stuff Does some of the same things What comes to mind when you hear the word “Model?”

Here’s an example of a model: • Q: How do auto designers decide what shape a car should be? • A1: Build one and drive it. • WRONG! • A2: Build one and put it in a wind tunnel. Closer. • A3: Build a MODEL and put it in a wind tunnel. • RIGHT !!!

Same shape 1/3 scale Clay over wood frame No doors No motor No windows No seats No paint But does the model look like a real car?Not Exactly.

Other Models • House plans • Electrical schematics • Maps • Blueprints • Program flowcharts • Equations - a “Mathematical Model” • Each of these in some way represents something in the real world that is too big or complex to understand as it stands. So each is simplified, or reduced in size, scope or scale.

Model - Definition Thus, a Modelis a simplifiedrepresentationof a complexreality, usually for the purpose of understandingthat reality, and having all the features necessary for the current task or problem.

3.1 Definitions Data Information Model Abstraction

Abstraction • Modeling is actually a form of abstraction. • Model - we buildsomething, • With the features needed for the problem at hand. • This is the essence of Abstraction.

Abstraction Definition: The process of focusing on those features that are essential for the task at hand, and ignoring those that are not.

SUMMARY  Data = Facts  Information = Useful  Model = Simplification of a complex reality.  Abstraction = Focussing on what is relevant for the task.

3.2 Models as an Aid to Understanding • The Pervasiveness of Models • A Child’s First model

The Pervasiveness of Models • Models are: • “Usually for the purpose of understanding” • Models can be: • Equations • Simulations (including video games) • Physical models • Mental models • Etc.

A Child’s First Model. . .

Since birth (or perhaps before) we have all been using object models. . .

Mental Models and our World View A newborn baby is bombarded by random sensory inputs and postulates the existence of OBJECTS

These objects: • Have attributes • Have attribute values • Are capable of behavior • Exhibit this behavior in response to messages In this way the child learns to predict and then to manipulate its environment.

So the child is able to make sense of, and to work with, what must seem to her/him like an INCREDIBLY COMPLEX UNIVERSE. THIS IS THE SAME TASK AN ANALYST FACES WHEN TRYING TO UNDERSTAND A USER’S BUSINESS!!

OBJECTS ARE THE MOST NATURAL AND EFFECTIVE WAY TO HANDLE AND UNDERSTAND COMPLEXITY

3.3 Early Methods (Pre-Modeling) • The Systems Development Life-Cycle (SDLC) • 1950s and Early 1960s: Unsystematic • Late 1960s: Output-Oriented

The Systems Development Life-Cycle (SDLC) • Analysis:What users need system to do • Design: Plan of how the system will do it. • Construction: Write and Test the code • Implementation: • Install software in production • Train users • Parallel run • We will revisit the SDLC in Chapter 6

1950s and Early 1960s: Unsystematic • The Process of Systems Analysis was not well understood. • The Problems were poorly understood. • Focus was often on solutions. • Efficiency v.s. Effectiveness.

Efficiency v.s. Effectiveness Efficiencyis doing the job right; • Effectivenessis • doing the right job!!

Before we begin to solve a problem We must clearly Understandand Defineit.This is what Analysis is all about, but this was not generally recognized until the late 1960s. . .

Late 1960s: Output-Oriented Methodologies • Methodology: • A body of methods, rules and postulates (i.e., beliefs), or a set of procedures, employed by some discipline (in this case MIS.)

Late 1960s: Output-Oriented Methodologies • Start with user’s vision of output reports • Work back through calculation and storage to input.

Late 1960s: Output-Oriented MethodologiesBenefits: • Added organization and rigor to the process • Completeness check

Late 1960s: Output-Oriented MethodologiesProblems: • Design is customized to the known set of outputs • System difficult to change as users’ needs inevitably change. • Small change can cause a cascade of changes back through the system • Maintenance still a problem

3.4 Models in Systems Development • First, some general comments about using models for systems development: • Listening skills • Notations, techniques and sensitivity • Users get a new view of their job • Development effort moved up front • Early detection of errors • Quality • Then, two kinds of earlier models : • Functional decomposition • Process models: Data Flow Diagrams (DFDs)

Listening Skills • “God gave us two ears and one mouth!” • Analyst is here to listen and learn about Users’ business operation and their problems. • Listening is a skill that needs to be developed. • Modeling methods add structure to user interviews • They are toolsfor effective Analysis and Design

It is by interacting with peopleand observing peoplethat we learn to understand our users’ world, and the difficulties they have with some parts of it

 To understand the users’ world we need three things: • Modeling notations • Modeling techniques • People sensitivity

 To understand the users’ world we need three things: • Modeling notations • To document what we learn • To communicate with the users

To understand the users’ world we need three things: • Modeling techniques • To ensure we use the tools properly • To give an accurate picture of the users’ operation

To understand the users’ world we need three things: • People sensitivity • Interviewing and listening skills • To ensure that we gather allrelevant information • So our models form a completeand accuratepicture of the users’ business

Users get a new view of their job We can say that A business is driven by its data or alternatively: A business rests upon a Pool of Data

This data represents all the things the users need to know at each step of their job to make their business run.

Development effort moved up front • All modeling-based methods force us to do more work on the earliest stages of a project. • This is important, since we must first understand and define the problem before we begin designing a solution. • This is related to the need to correct errors early (see next section ).

Development effort moved up front(graph on page 40) New Old Analysis Design Coding Implementation Maintenance

Development effort moved up front But there is a problem: • Management expect see “results” for all the time and money spent, • But we could model for weeks or months and not produce any code or screens. • Modern methodologies thus focus on “Deliverables.”

Development effort moved up front • Deliverables: Documentation or other products that are produced at the end of each phase and sub-phase of the project. • Producing them tells us we have reached the end of that phase.

Early detection of errors In systems development, • 56% of errors are in determining the users’ requirements. • But81%of time, effort and expense are used to correct those 56% of the errors.

Models and Modeling