Assignment Bia Old

7/28/2019 Assignment Bia Old

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ASSIGNMENT OF BUSINESS INTELLIGENCE ANDANALYSIS

MANMEET KAUR

13315903911

MBA-4TH

SEM.-B


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ASSIGNMENT

Ques. Explain the traditional system development life cycle.

The phrase 'systems life cycle' simply describes the steps that are taken in a project, from the

time that the project is started to when it is finished. When any computer-related project is

initiated, a number of distinct steps, or stages, can be identified in the life of the project. Each

of these stages will involve people doing jobs and producing 'things', for example, a design

document, a test plan or a piece of program code. Each of these things takes the project a

little further towards completion. Things that have to be produced at the end of each stage

are known as 'deliverables'.

The idea behind the project life cycle is that the deliverables associated with each stage in the

project must be produced and checked off by the Project Manager before the next stage can

begin. A stage cannot be started until the previous stage is finished. This stops a project getting

ahead of itself. For example, it will stop someone trying to start the stage called

'implementation' (the stage where you actually make the project using a database

application or code) before all of the design documentation has been completed. You may

have had some experience of this scenario yourselves with coursework - you don't want to do

the paperwork or a detailed design, you just want to get on and do the project! This, however,

is the road to potential disaster! For example:

How can a project be designed if it is not clear what the problem is? How can a project be built if it is not designed? How can it be installed if it is not properly tested? What happens if a key project member leaves - how can someone new pick up where

they left off if half of the paperwork is missing or incomplete?

How can a Project Manager accurately manage a project if they can't clearly see thatdeliverables are being completed on time and within the budget?

How can someone make changes to the product in the future if the documentation isincomplete?

The list of potential problems goes on and on. A project life cycle gives a project a structureand therefore allows a Project Manager to manage the project rather than reacting to things

when they go wrong! We can summarise the project life cycle (sometimes called the 'waterfall

model') with the following diagram:


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The systems development life cycle (SDLC) is a conceptual model used in project

management that describes the stages involved in an information system development

project, from an initial feasibility study through maintenance of the completed application.

Various SDLC methodologies have been developed to guide the processes involved, includingthe waterfall model (which was the original SDLC method); rapid application development

(RAD); joint application development (JAD); the fountain model; the spiral model; build and

fix; and synchronize-and-stabilize. Frequently, several models are combined into some sort of

hybrid methodology. Documentation is crucial regardless of the type of model chosen or

devised for any application, and is usually done in parallel with the development process.

Some methods work better for specific types of projects, but in the final analysis, the most

important factor for the success of a project may be how closely the particular plan was

followed.

In general, an SDLC methodology follows the following steps:

The existing system is evaluated. Deficiencies are identified. This can be done by interviewing

users of the system and consulting with support personnel.

The new system requirements are defined. In particular, the deficiencies in the existing system

must be addressed with specific proposals for improvement.

The proposed system is designed. Plans are laid out concerning the physical construction,

hardware, operating systems, programming, communications, and security issues.

The new system is developed. The new components and programs must be obtained and

installed. Users of the system must be trained in its use, and all aspects of performance must betested. If necessary, adjustments must be made at this stage.

The system is put into use. This can be done in various ways. The new system can phased in,

according to application or location, and the old system gradually replaced. In some cases, it

may be more cost-effective to shut down the old system and implement the new system all at

once.
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Once the new system is up and running for a while, it should be exhaustively evaluated.

Maintenance must be kept up rigorously at all times. Users of the system should be kept up-

to-date concerning the latest modifications and procedures

Ques.Why is prototyping considered to be a suitable process for the

development of DSS ?A prototype is the sample implementation of the system that shows limited and main

functional capabilities of the proposed system. After a prototype is built, it is delivered to the

customer for the evaluation. The prototype helps the customer determine how the feature will

function in the final software. The customer provides suggestion and improvements on the

prototype. The development team implements the suggestion in the new prototype, which is

again evaluated by the customer. The process continues until the customer and the

development team understands the exactrequirement of the proposed system. When

the final prototype is developed, the

requirement is considered to be frozen.

The prototyping approach is used in the

requirement gathering and in the analysis

phase to capture the exact requirement of

the proposed system. After the

requirements are frozen, the remaining

phases of the development process needs to

be executed to complete the development

of the software system.

An e-commerce website, such as shopping site is an example where you can implement the

prototyping approach. You can develop the prototype of the various web pages of the

shopping site such as catalogue page, product order page etc., and present it to the customer

for approval. If the customer approves the prototype of the site, requirements are states again

and the design of the web site is initiated. If the customer does not approve the web site, the

development team revisits the prototype and resubmits it to the customer for approval. This

process continues until the prototype is approved.

Prototyping systems development focuses on the iterative creation of a new information

system. Rather than going through the whole SDLC process for everything that could be

potentially envisioned with the system, a portion of the system is chosen to use to create a

prototype. The prototype does not go through extensive requirements analysis and instead

focuses on getting something created quickly for immediate use by end-users in order to

gather feedback to either modify the prototype or begin the process again with another

component. While the prototyping development method gets users involved in the system's


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development and typically brings about real results quickly, it can be difficult to manage

prototyping-based projects due to its differences with SDLC (Alavi 1984). Since the mid-80s,

prototyping has matured as an information systems development method.

Prototyping has emerged as a powerful method for developing IT systems but especially DSS.

The ability to go quickly from concept to usable prototype system means that users can givefeedback very quickly. That feedback enables the project to either move forward or go back

for revisions. In the classic waterfall methodologies like SDLC, there is not a very good way to

go back since everything is supposed to be determined before the implementation work

begins. In the fast changing real world, software projects that take several months are a risk

because the market landscape can quickly change, potentially making the project obsolete

before it is ever finished. Prototyping enables an organization to reduce this risk, and it is

especially useful for DSS development.

Need of Prototyping Model

This type of System Development Method is employed when it is very difficult to obtain exactrequirements from the customer(unlike waterfall model, where requirements are clear). While

making the model, user keeps giving feedbacks from time to time and based on it, a

prototype is made. Completely built sample model is shown to user and based on his

feedback, the SRS(System Requirements Specifications) document is prepared. After

completion of this, a more accurate SRS is prepared, and now development work can start

using Waterfall Model.

Now lets discuss the disadvantages and advantages of the Prototype model in Software

Development Method.

Prototyping Process Model

Advantages of Prototyping Model

1) When prototype is shown to the user, he gets a proper clarity and 'feel' of thefunctionalityof

the software and he can suggest changes and modifications.2) This type of approach of developing the software is used for non-IT-literate people. They

usually are not good at specifying their requirements, nor can tell properly about what they

expect from the software.

3) When client is not confident about the developer's capabilities, he asks for a small

prototype to be built. Based on this model, he judges capabilities of developer.

4) Sometimes it helps to demonstrate the concept to prospective investors to get funding for
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project.

5) It reduces risk of failure, as potential risks can be identified early and mitigation steps can

be taken.

6) Iteration between development team and client provides a very good and conductive

environment during project.

7) Time required to complete the project after getting final the SRS reduces, since thedeveloper has a better idea about how he should approach the project.

Disadvantages of Prototyping Model:

1) Prototyping is usually done at the cost of the developer. So it should be done using minimal

resources. It can be done using Rapid Application Development (RAD) tools. Please note

sometimes the start-up cost of building the development team, focused on making prototype,

is high.2) Once we get proper requirements from client after showing prototype model, it may be of

no use. That is why, sometimes we refer to the prototype as "Throw-away" prototype.

3) It is a slow process.

4) Too much involvement of client, is not always preferred by the developer.

5) Too many changes can disturb the rhythm of the development team.

Ques. Write short note on Intelligent DSS

Intelligent Decision Support Systems (IDSS) is a term that describes decision supportsystems that make extensive use ofartificial intelligence (AI) techniques. Use of AI techniques

in management information systems has a long history, indeed terms such as Knowledge-

based systems (KBS) and intelligent systems have been used since the early 1980s to describe

components of management systems, but the term "Intelligent decision support system" is

thought to originate with Clyde Hols apple and Andrew Whinston in the late 1970s. Flexible

manufacturing systems (FMS), intelligent marketing decision support systems and medical

diagnosis systems can also be considered examples of intelligent decision support systems.

Ideally, an intelligent decision support system should behave like a human

consultant; supporting decision makers by gathering and analysing evidence, identifying anddiagnosing problems, proposing possible courses of action and evaluating the proposed actions.

The aim of the AI techniques embedded in an intelligent decision support system is to enable

these tasks to be performed by a computer, whilst emulating human capabilities as closely as

possible.

Many IDSS implementations are based on expert systems, a well established type of KBS that

encode the cognitive behaviours of human experts using predicate logic rules and have been
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shown to perform better than the original human experts in some circumstances. Expert

systems emerged as practical applications in the 1980s based on research in artificial

intelligence performed during the late 1960s and early 1970s. They typically combine

knowledge of a particular application domain with an inference capability to enable the

system to propose decisions or diagnoses. Accuracy and consistency can be comparable to (or

even exceed) that of human experts when the decision parameters are well known (e.g. if acommon disease is being diagnosed), but performance can be poor when novel or uncertain

circumstances arise.

Some research in AI, focused on enabling systems to respond to novelty and uncertainty in

more flexible ways is starting to be used in intelligent decision support systems. For

example intelligent agents that perform complex cognitive tasks without any need for human

intervention have been used in a range of decision support applications. Capabilities of these

intelligent agents include knowledge sharing, machine learning, data mining, and

automated inference. A range of AI techniques such as case based reasoning, rough

sets] and fuzzy logic have also been used to enable decision support systems to perform betterin uncertain conditions.

Ques. Give the schematic view

of DSS and explain each of the

components of DSS in brief.

A decision support system (DSS) is a

computer-based information

system that supports business or

organizational decision-making

activities. DSSs serve the

management, operations, and

planning levels of an organization

and help to make decisions, which

may be rapidly changing and not

easily specified in advance. Decision

support systems can be either fully

computerized, human or a

combination of both.

DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a

combination of raw data, documents, and personal knowledge, or business models to identify

and solve problems and make decisions.

Typical information that a decision support application might gather and present includes:
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inventories of information assets (including legacy and relational data sources, cubes, data

warehouses, and data marts),comparative sales figures between one period and the next,

projected revenue figures based on product sales assumptions.

Decision support systems vary greatly in application and complexity, but they all share specific

features. A typical Decision support systems has four components: data management, model

management, knowledge management and user interface management.

Data Management Component

The data management component performs the function of storing and maintaining the

information that you want your Decision Support System to use. The data managementcomponent, therefore, consists of both the Decision Support System information and the

Decision Support System database management system. The information you use in

your Decision Support System comes from one or more of three sources:

-Organizational information; you may want to use virtually any information available in

the organization for your Decision Support System. What you use, of course, depends on what

you need and whether it is available. You can design your Decision Support System to access

this information directly from your companys database and data warehouse. However,

specific information is often copied to the Decision Support System database to save time in

searching through the organizations database and data warehouses.

-External information: some decisions require input from external sources of information.

Various branches of federal government, Dow Jones, Compustat data, and the internet, to

mention just a few, can provide additional information for the use with a Decision Support

System.

-Personal information: you can incorporate your own insights and experience your

personal information into your Decision Support System. You can design your Decision Support

System so that you enter this personal information only as needed, or you can keep the

information in a personal database that is accessible by the Decision Support System.

Model Management Component

The model management component consists of both the Decision Support System models and

the Decision Support System model management system. A model is a representation of some

event, fact, or situation. As it is not always practical, or wise, to experiment with reality, people
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build models and use them for experimentation. Models can take various forms.

Businesses use models to represent variables and their relationships. For example, you would

use a statistical model called analysis of variance to determine whether newspaper, TV, and

billboard advertizing are equally effective in increasing sales.

Decision Support Systems help in various decision-making situations by utilizing models that

allow you to analyze information in many different ways. The models you use in a Decision

Support System depend on the decision you are making and, consequently, the kind of

analysis you require. For example, you would use what-if analysis to see what effect the

change of one or more variables will have on other variables, or optimization to find the most

profitable solution given operating restrictions and limited resources. Spreadsheet software

such as excel can be used as a Decision Support System for what-if analysis.

The model management system stores and maintains the Decision Support Systems models. Its

function of managing models is similar to that of a database management system. The modelmanagement component can not select the best model for you to use for a particular problem

that requires your expertise but it can help you create and manipulate models quickly and

easily.

User Interface Management Component

The user interface management component allows you to communicate with the Decision

Support System. It consists of the user interface management system. This is the component

that allows you to combine your know-how with the storage and processing capabilities of the

computer.

The user interface is the part of the system you see through it when enter information,

commands, and models. This is the only component of the system with which you have direct

contract. If you have a Decision Support System with a poorly designed user interface, if it is

too rigid or too cumbersome to use, you simply wont use it no matter what its capabilities.

The best user interface uses your terminology and methods and is flexible, consistent, simple,

and adaptable.

For an example of the components of a Decision Support System, lets consider the Decision

Support System that Lands End has tens of millions of names in its customer database. It sellsa wide range of womens, mens, and childrens clothing, as well various household wares. To

match the right customer with the catalog, lands end has identified 20 different specialty

target markets. Customers in these target markets receive catalogs of merchandise that they

are likely to buy, saving Lands End the expense of sending catalogs of all products to all 20

million customers. To predict customer demand, lands end needs to continuously monitor

buying trends. And to meet that demand, lands end must accurately forecast sales levels. To


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accomplish theses goals, it uses a Decision Support System which performs three tasks:

-Data management: The Decision Support System stores customer and product

information. In addition to this organizational information, Lands End also needs external

information, such as demographic information and industry and style trend information.

-Model management: The Decision Support System has to have models to analyze the

information. The models create new information that decision makers need to plan product

lines and inventory levels. For example, Lands End uses a statistical model called regression

analysis to determine trends in customer buying patterns and forecasting models to predict

sales levels.

-User interface management: A user interface enables Lands End decision makers to

access information and to specify the models they want to use to create the information they

need.

Knowledge Management Component

The knowledge management component, like that in an expert system, provides information

about the relationship among data that is too complex for a database to represent. It consists

of rules that can constrain possible solution as well as alternative solutions and methods for

evaluating them.

For example, when analyzing the impact of a price reduction, a Decision Support Systemshould signal if the forecasted volume of activity exceeds the volume that the projected staff

can service. Such signaling requires the Decision Support System to incorporate some rules-of-

thumb about an appropriate ratio of staff to sales volume. Such rules-of-thumb, also known

as heuristics, make up the knowledge base.

Benefits of DSS

Improves personal efficiency Speed up the process of decision making Increases organizational control Encourages exploration and discovery on the part of the decision maker Speeds up problem solving in an organization Facilitates interpersonal communication Promotes learning or training Generates new evidence in support of a decision


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Creates a competitive advantage over competition Reveals new approaches to thinking about the problem space Helps automate managerial processes Create Innovative ideas to speed up the performance

Ques. Write short note on knowledge based expert system.

Knowledge Based Expert Systems

Expert systems are computer programs that contain expert knowledge stored in a knowledge

base. This information or knowledge can be used to examine plant data and form conclusions

about the way the plant, or piece of equipment within that plant is performing. The

advantage of this approach is that it does not require detailed mathematical understanding

of plant operation in

order to provide

important informationto plant engineers.

Steelmaking processes

generate fume that

must be captured and

controlled to prevent

emissions to

atmosphere. This is

done with large

extraction fans and bag

filter cleaning systems.

The operation of these

systems is critical to

ensure good

environmental

performance of the steelmaking process and optimisation is necessary to ensure that the fume

extraction systems are run economically. Fume capture also helps to recycle the metallic oxides

back into the steelmaking process. Owing to the complexity of large extraction systems it is

important to pre-empt plant malfunction so that site engineers are aware of potential

problems that may cause system failure or fugitive particulate emissions to atmosphere.

A prototype expert system has recently been installed at a local electric arc based steelworks.

The model accesses real-time data from the melting shop, extraction system and bag filter

plant and is able to perform the two roles of information and advice.

Real-time data are displayed as dynamic plant mimics so that site engineers are able to see

the condition of the extraction system without need to be on plant. An expert system

containing a knowledge base of plant rules informs engineers of any plant operational


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anomalies. Typical expert rules might check for the correct operation of dampers controlling

the filter cleaning sequences, or to check for damaged filter bags that might eventually cause

roof emissions.

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