Module 3

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Module -III

System And Data Analysis

(Data Analyzing Modeling)

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Topics• Determining System Requirements (Traditional

Methods, Modern & Radical Methods)

• Structuring System Requirements – Process Modeling – DFD

– Logic Modeling – Structured English & Decision Tables

– Conceptual Modeling – ER Model

• Data Analysis & Techniques (Interpretive, Coding, Recursive Abstraction and Mechanical Technique),

• Types of Analysis (Descriptive, Exploratory, Confirmatory and Predictive)

• Modeling Methodologies (Bottom Up method & Top Down Method)

• Generic and Schematic Data Modeling

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Determining System Requirements

• In this Analyst gather information on what the system should do from many sources as possible. Such sources include user of the current system, reports, forms, and procedures.

• All of the system requirements are carefully documented and made during requirements determination and ordering them into tables, diagrams, and other formats that make them easier to translate into technical system specification.

• The characteristics you need to enjoy solving the mysteries and puzzles are the same one you need to be a good system analyst during requirement determination.

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Methods of Collecting Requirement

1. Traditional Method: Interviews, Questionnaire, Direct Observation.

2. Modern Method: JAD, Prototyping

3. Radical Method: Business Process Reengineering.

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Requirements Structuring

• Two essential views of the current and replacement information systems. Both are describing the same system, but in a different way.

– Process view: The sequence of data movement & handling operations within the system

• Data flow diagrams

– Data: The inherent structure of data independent of how or when it is processed

• Entity-relation diagrams

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Remember to...

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Traditional Methods for Determination Requirements

• Collection of information is at the core of systems analysis.

• Collect the information about the information system that are currently in use.

• To find out how users would like to improve the current systems and organizational operations with new or replacement information system.

• Best way to get the information is : – Talk to the person who are directly or indirectly involved

in different part of the organizations.– Gather the copies of relevant document required by

current systems and business processes– Interviews and direct observation

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Traditional Methods for Determination Requirements

• Administering questionnaires• Interviewing and listening• Interviewing groups• Directly observing users• Analyzing procedures & other

documents

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Administering QuestionnairesAdvantages & Disadvantages

• Strengths– 1.– 2.– 3.

• Weaknesses– 1.– 2.– 3.

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Give Me Ambiguity or Give Me Something Else!

• How often do you back up your computer files?– A. Frequently– B. Sometimes– C. Hardly at all– D. Never

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This is better…

• How often do you back up the computer files stored on the hard disk on the PC you use for over 50% of your work time?– A. Frequently (at least once per week)– B. Sometimes (from 1 to 3 times per

month)– C. Hardly at all (once per month or less)– D. Never

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Interviewing and listening• Interviewing is one of the primary ways analysts

gather information about an information systems project.

• Analyst may spend a large amount of time interviewing people about their work, the information they use to do it.

• It is used to understand organizational direction, policies, and expectations that managers have on the units they supervise.

• During interview you gather facts, opinions and speculation and observe body language, emotions and other signs of what people want and how they access current system.

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Guidelines of effective interviewing

Guidelines

Plan the Interview

Be neutral

Listen and take notes

Review Notes

Seek Diverse views

What is InvolvedPrepare interviewee by making and

appointment and explaining the purpose of the interview.

Prepare a checklist, an agenda, and questions

Avoid asking leading questions.

Give your undivided attention to the interviewee and take notes or tape-record the interview (if permission is granted).

Review your notes within 48 hours of the meeting. If you discover follow-up questions or need additional information, contact the interviewee

Interview a wide range of people, including potential users and managers.

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Interviewing & Listening• Before• During the interview• Afterwards

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During the Interview• Beginning

– Introduction, open-ended questions, interest & attention

• Middle– Open & close-ended questions, f-u questions,

active listening, provide feedback, limit note-taking

• End– Summarize, request feedback and/or f-u, ask

for corrections

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InterviewsAdvantages & Disadvantages

• Strengths– Extracts both

qualitative and quantitative data

– Detailed and summary data

– Good way to find needs and assumptions

• Weaknesses– Requires skills– May be biased; May

collect lots of useless data

– Expensive, time consuming

– Requires other methods to verify results

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How do you choose interview questions?

• Open-ended questions– 1.– 2.– 3.

• Closed-ended questions– 1.– 2.– 3.

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Find out about someone’s job

• Write 3 open-ended questions

• Ask 3 open-ended questions (You may substitute questions during interview.)

• Write down answers

• Write 3 closed-ended questions

• Ask 3 closed-ended questions (You may substitute questions during interview.)

• Write down answers

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Interviewing Groups Advantages & Disadvantages

• Strengths– Not biased by one

user’s opinion– Can get many

user’s opinion

• Weaknesses– With many people

present, decision-making takes time

– Interruptions during process

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Directly Observing Users• Another fact finding method used by the

systems analyst is on-site or direct observation.

• The analyst’s role is that of an information seeker.

• Purpose of on-site observation is to get as close as possible to the real system being studied.

• The analyst observes the physical layout of the current system, the location and movement of people, and the work flow.

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Four Methods of Direct Observation

• Natural or contrived:

• Obtrusive or unobtrusive:

• Direct or indirect:

• Structured or unstructured:

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• Natural or Contrived: A natural observation occurs in a settings such as the employee’s place of work; contrived observation is set up by the observer in a place like a laboratory.

• Obtrusive or Unobtrusive: An obtrusive observation takes place when the respondent knows he/she is being observed; an unobtrusive observation takes place in a contrived way such as behind a one-way mirror.

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• Direct or Indirect: A direct observation takes place when the analyst actually observes the subject or the system at work. In an indirect observation, the analyst uses mechanical devices such as cameras and videotapes to capture information.

• Structured or Unstructured: In structured observation, the observer looks for and records a specific action such as the number of soup cans a shopper picks before choosing one. Unstructured methods place the observer in a situation to observe whatever might be pertinent at the time.

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Disadvantage of Direct Observation

• Intruding into user’s area often results in adverse reactions by the staff. Therefore, adequate preparation and training are important.

• Attitudes and motivations of subjects cannot be readily observed-only the actions that result from them.

• Observations are subject to error due to the observer’s misinterpretation and subjective selection of what to observe, as well as subject altered work pattern during observation.

• Unproductive, long hours are often spent in an attempt to observe specific, one time activities or events.

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You May Need to Analyze Work Procedures

• Work procedures describe a particular job or task

• May show duplication of effort• May find missing steps• May contradict info collected from

interviews, questionnaires, and observations

• Formal systems vs informal systems

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Modern Method

• Joint Application Design• Prototyping

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Radical Method• In Some Organizations, Management is looking

for new ways to perform the current task. These may be radically different from how things are done now, but the payoffs may be enormous:

• Fewer people may be needed to do same work; relationship with customers may improved dramatically; and process become much more efficient and effective, all of which can result in increased profits. The overall process by which current methods are replaced with radically new methods is referred to as Business Process reengineering (BPR)

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• What is BPR?• Business Process Reengineering is the

analysis and design of workflows and processes within an organization. A business process is a set of logically related tasks performed to achieve a defined business outcome. Re-engineering is the basis for many recent developments in management.

• Business Process Reengineering (BPR) is basically the fundamental rethinking and radical re-design, made to an organizations existing resources. It is more than just business improvising.

BPR (Business Process Reengineering)

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• Search for and implementation of radical change in business processes to achieve breakthrough improvements in products and services

• Goals– Reorganize complete flow of data in major

sections of an organization

– Eliminate unnecessary steps

– Combine steps

– Become more responsive to future change

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• Identification of processes to reengineer– Key business processes

• Set of activities designed to produce specific output for a particular customer or market

• Focused on customers and outcome• Same techniques are used as were used for

requirements determination

• Identify specific activities that can be improved through BPR

• Disruptive technologies– Technologies that enable the breaking of long-

held business rules that inhibit organizations from making radical business changes.


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• Process Modeling – DFD (Data Flow

Diagram)

• Logical Modeling – Structure English

& Decision Table

• Conceptual Modeling – ER Diagram

Structuring System Requirements

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• It involves graphical representing the

process, or actions, that capture,

manipulate, store and distribute data

between a system and its environment

and among components within a system.

A common form of process modeling is

DFD (Data Flow Diagram).

Process Modeling

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DFD (Data Flow Diagram)• A data flow diagram is a graphic that

illustrates the movement of data between external entities and the processes and data stores within a system.

• It is a top-down approach, moves from general requirements to more specific requirements, illustrating process, movement, and storage of data in the system.

• It is a way to focus on functions rather than physical implementation.

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DFD

• A process model used to depict the flow of data through a system and the work or processing performed by the system. Synonyms are Bubble chart, Transformation Graph, and Process Model.

• The DFD has also become a popular tool for business process redesign. It is developed by Larry Constantine as a way of expressing system requirements in a graphical form.

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DFD Symbols• A Square – Define a source or destination of

system data.

• An Arrow – Identifies data flow – data in motion.

It is a pipeline through which information flows.

• A Circle Or Bubble – A process that transforms

incoming data flow into outgoing data flow.

• An open Rectangle – It is a data store – data at

rest, or a temporary repository of data.

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Or

Or

= Source or Destination of data

= Process that transform data flow

= Data store

= Data Flow

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DFD Example

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System/Level 0 DFD

External entity - Student

Processes - Check available, Enrol student,

Confirm Registration

Data Flows - Application Form, Course

Details, Course Enrolment Details, Student Details,

Confirmation/Rejection Letter

Data Stores - Courses, Students.

DFD Example

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Logic Modeling• Although DFD are good for identifying the

processes, they do not show the logic inside the processes. Even the processes of the primitive-level DFD do not show the most fundamental processing steps. Just what occurs with in a process?

• Logic modeling involves representing the internal structure and functionality of the process represented on DFD. Processes must be clearly described before they can be translated into a programming language.

• Decision table is the common method for modeling system logic, that allow you to represent a set of conditions and the actions that follow from them in a tabular format.

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Structure English• Structured English is the use of the English

language with the syntax of structured programming. Thus structured English aims at getting the benefits of both the programming logic and natural language. Program logic helps to attain precision while natural language helps in getting the convenience of spoken languages.

• Structured English or "pseudocode" consists of the following elements:– Operation statements written as English phrases

executed from the top down – Conditional blocks indicated by keywords such as IF,

THEN, and ELSE – Repetition blocks indicated by keywords such as DO,

WHILE, and UNTIL

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• Use the following guidelines when writing Structured English:– Statements should be clear and unambiguous – Use one line per logical element – All logic should be expressed in operational, conditional,

and repetition blocks – Logical blocks should be indented to show relationship – Keywords should be capitalized

• Examples of common keywords– START, BEGIN, END, STOP, DO, WHILE, DO WHILE, FOR,

UNTIL, DO UNTIL, REPEAT, END WHILE, END UNTIL, END REPEAT, IF THEN, IF, ELSE, IF ELSE, END IF, THEN, ELSE THEN, ELSE IF, SO, CASE, EQUAL, LT, LE, GT, GE, NOT, TRUE, FALSE, AND, OR, XOR, GET, WRITE, PUT, UPDATE, CLOSE, OPEN, CREATE, DELETE, EXIT, FILE, READ, EOF, EOT, WITH,RETURN

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Example of Structured English

• A bank will grant loan under the following conditions1. If a customer has an account with the bank and

had no loan outstanding, loan will be granted.

2. If a customer has an account with the bank but some amount is outstanding from previous loans then loan will be granted if special approval is given.

3. Reject all loan applications in all other cases.

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IF customer has a Bank Account THEN

IF Customer has no dues from previous account THEN

Allow loan facility

ELSE

IF Management Approval is obtained THEN

Allow loan facility

ELSE

Reject

ENDIF

ENDIF

ELSE

Reject

ENDIF

Solution

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Decision Table• A decision table is a diagram of process logic

where the logic is reasonably complicated. All of

the possible choices and the conditions the

choices depend on are represented tabular form.

• Decision tables have proven to be easier to

understand and review than code, and have been

used extensively and successfully to produce

specifications for complex systems.

• In decision table the three parts to be include the

condition stub, the action stub, and the rules.

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Example• A technical support company writes a decision table

to diagnose printer problems based upon symptoms described to them over the phone from their clients.

Printer troubleshooter

Rules

Conditions

Printer does not print Y Y Y Y N N N N

A red light is flashing Y Y N N Y Y N N

Printer is unrecognised Y N Y N Y N Y N

Actions

Check the power cable X

Check the printer-computer cable X X

Ensure printer software is installed X X X X

Check/replace ink X X X X

Check for paper jam X X

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Conceptual Modeling• The Conceptual data modeling is a representation

of organizational data. The purpose of a conceptual data model is to show as many rules about meaning and interrelationships among data as possible.

• Entity-relationship (E-R) data model are commonly used diagram that show how data are organized in an information system.

• The main goal of conceptual data modeling is to create accurate E-R Diagrams. As a system analyst you do conceptual data modeling at the same time as other requirements analysis and structuring steps during system analysis.

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E-R Diagram• An entity-relationship diagram is a

data modeling technique that creates a graphical representation of the entities, and the relationships between entities, within an information system.

• Data Modeling– Data modeling is the analysis of

data objects that are used in a business or other context and the identification of the relationships among these data objects.

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Purpose Of ERD Verify Accuracy and thoroughness of data

design, current and new, with users. Organize and record organizational data

entities, relationships and scope through decomposition and layering.

Enhance the overall communication between development project team members, system technicians, management and users with the use of graphic models.

Generally simplify and bolster the creative data design process.

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Entity Sets

• Database: collection of entities and relationship among entities

• Entity: object that exists and distinguishable from other objects

• Entity set: collection of similar objects

• Attribute: property of an entity set– Each entity in the set has the same properties

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Attributes

• Domain: set of permitted values for each attributes

• Attribute types: – Simple vs. composite

– Single-valued v.s. multi-valued

– Derived

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E/R Diagram

1. Entity sets: diagrams

2. Attributes: oval

3. Relationship sets between entity

sets: diamond

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How to Represent E-R Diagram

ENTITYENTITY

ER

MODEL

Relationship

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Example E/R Diagram

Dog

Name Breed

Age

License #

Phone

Weight

Name

Name

Phone

Address

Owns

Owner

Kennel

Pays

Boards

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The Three Main Components of ERD

• Entity : The entity is a person, object, place or event for which data is collected.

• Relationship : The relationship is the interaction between the entities.

• Cardinality : The cardinality defines the relationship between the entities in terms of numbers.

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Mapping Cardinality

• Number of entities to which another entity can be associated via a relationship set

• The three main cardinal relationships are :– One-one, expressed as 1:1– One-many, (Many-one) expressed as

1:M– Many-many, expressed as M:N.

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Cardinality of Relationships

one-one many-one many-many

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Data AnalysisTo analys the data we have four techniques• Interpretive techniques : The most common analysis of

qualitative data is observer impression. That is, expert examine the data, interpret it via forming an impression and report their impression in a structured and sometimes quantitative form.

• Coding : Coding is an interpretive technique that both organizes the data and provides a means to introduce the interpretations of it into certain quantitative methods. Most coding requires the analyst to read the data and demarcate segments within it.

• Recursive abstraction : A recursive abstraction, where datasets are summarized, those summaries are then further summarized, and so on.

• Mechanical techniques :Some techniques rely on leveraging computers to scan and sort large sets of qualitative data. At their most basic level, mechanical techniques rely on counting words, phrases, or coincidences of tokens within the data.

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Thank You

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Types of Analysis• Descriptive : Descriptive models quantify

relationships in data in a way that is often used to classify customers or prospects into groups.

• Exploratory :Exploratory data analysis (EDA) is an approach to analysing data for the purpose of formulating hypotheses worth testing, complementing the tools of conventional statistics for testing hypotheses

• Confirmatory• Predictive : Predictive models analyze past

performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness. This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models.

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• Confirmatory Analysis • Inferential Statistics - Deductive Approach

– Heavy reliance on probability models – Must accept untestable assumptions – Look for definite answers to specific questions – Emphasis on numerical calculations – Hypotheses determined at outset – Hypothesis tests and formal confidence interval estimation

• Advantages – Provide precise information in the right circumstances – Well-established theory and methods

• Disadvantages – Misleading impression of precision in less than ideal

circumstances – Analysis driven by preconceived ideas – Difficult to notice unexpected results

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• Exploratory Analysis • Descriptive Statistics - Inductive Approach

– Look for flexible ways to examine data without preconceptions – Attempt to evaluate validity of assumptions – Heavy reliance on graphical displays – Let data suggest questions – Focus on indications and approximate error magnitudes

• Advantages – Flexible ways to generate hypotheses – More realistic statements of accuracy – Does not require more than data can support – Promotes deeper understanding of processes

• Statistical learning

• Disadvantages – Usually does not provide definitive answers – Difficult to avoid optimistic bias produced by overfitting – Requires judgement and artistry - can't be cookbooked