Information System Analysis

Information System Analysis Topic-2

Data Gathering • Observations • Questionnaires • Interviews

Data Gathering \ Observation • Observation usually means just that –watching and seeing • See firsthand the relationships that exist between decision makers and other organizational members.

Data Gathering \ Observation • Types of data gathered this way cannot easily be collected by other techniques. • The key advantage of observation is firsthand information: You can take notes that describe the activities as they occur.

Data Gathering \ Questionnaires • Questionnaires are special-purpose documents that allow the analyst to collect information and opinions from respondents.

Data Gathering \ Questionnaires Types of Questionnaires:- • Free-format questionnaires:- Offer the respondent greater latitude in the answer. • Fixed-format questionnaires:- Contain questions that require selection of predefined responses from individuals. Example:-Multiple-choice questions

Data Gathering \ Questionnaires Questionnaires Procedure:- • Determine what facts and opinions must be collected and from whom you should get them. • Based on the needed facts and opinions, determine whether free or fixed-format questions will produce the best answers. • Write the questions

Data Gathering \ Questionnaires Questionnaires Procedure:- • Test the questions on a small sample of respondents. • Duplicate and distribute the questionnaires

Data Gathering \ Interviews • Interviews are a fact-finding technique whereby the systems analysis collect information from individuals through face-to-face interaction.

Data Gathering \ Interviews Types of Interviews :- • Unstructured interviews are conducted with only a general goal or subject in mind and with few, if any, specific questions. • In structured interviews the interviewer has a specific set of questions to ask of the interviewee.

Data Gathering \ Interviews Types of Interview Questions :- • Open-ended questions allow the interviewee to respond in any way that seems appropriate. • Closed-ended questions restrict answers to either specific choices or short direct responses.

Data Gathering \ Interviews Procedure to conduct an Interview :- • Select interviewees. • Prepare for the Interview • Conduct the Interview • Follow up on the Interview

Data Gathering \ Interviews Interview Question Guidelines :- • Use clear and concise language. • Don’t include your opinion as part of the question. • Avoid long or complex questions. • Avoid threatening questions. • Don’t use “you” when you mean a group of people.

Process Modeling • Process modeling involves graphically representing the processes, or actions, that capture, manipulate, store, and distribute data between a system and its environment and among components within a system.

Data Flow Diagram (DFD) • It is a graphic that illustrates the movement of data between external entities and the processes and data stores within a system. • DFD can represent both physical and logical information systems. • DFD are versatile diagramming tools. With only four symbols, represent data flows, data stores, processes, and sources/sinks (External Entities)

DFD’s Symbols

DFD’s Symbols \ External Entities • Represent people or organizations outside of the system being studied • Shows the initial source and final recipient of data and information • Should be named with a noun, describing that entity

DFD’s Symbols \ External Entities • External entities may be: • A person, such as CUSTOMER or STUDENT. • A company or organization, such as BANK or SUPPLIER. • Another department within the company, such as ACCOUNT DEPARTMENT. • Another system or subsystem, such as the INVENTORY CONTROL SYSTEM.

DFD’s Symbols \ Processes • It is the work or actions performed on data so that they are transformed, stored, or distributed. • Represent either: • A whole system, or A subsystem • Work being done, an activity • Names should be in the form verb-adjective-noun • Note:- when modeling the data processing of a system, it doesn’t matter whether a process is performed manually or by a computer.

DFD’s Symbols \ Data Store • Name with a noun, may represent one of many different physical locations for data. To understand data movement and handling in a system. • Data stores are usually given a unique reference number, such as D1, D2, D3. • Include any data stored, such as: • A computer file or database. • A set of tables . • A manual file of records.

DFD’s Symbols \ Data flow • Data flow shows the data about a person, place, or thing that moves through the system. • Names should be a noun that describes the data moving through the system. • Arrowhead indicates the flow direction.

Developing DFD Use the following guidelines: • Make a list of activities and use it to determine (External Entities, Data Flows, Process, Data Stores) • Create the context level diagram, including all external entities and the major data flow to or from them.

Developing DFD Use the following guidelines: • Create Diagram 0 by analyzing the major activities within the context process. • Include the external entities and major data stores. • Create a child diagram for each complex process on Diagram 0 (Level 1). • Check for errors and make sure the labels you assign to each process and data flow are meaningful.

Creating The Context Diagram • It contains only one process, representing the entire system. • The process is given the number zero. • All external entities are shown on the context diagram as well as major data flow to and from them. • The diagram does not contain any data stores.

Creating Diagram 0 • Diagram 0 is the explosion of the context level diagram. • It should include up to 7 or 9 processes. • Any more will result in a cluttered diagram. • Processes are numbered with an integer. • The major data stores and all external entities are included on Diagram 0.

Creating Child Diagram • Each process on diagram zero may be exploded to create a child diagram. • These diagrams found below Diagram 0 are given the same number as the parent process. • Process 3 would explode to Diagram 3. • Each process is numbered with the parent diagram number, and a unique child diagram number. • Examples are: 3.2 on Diagram 3, the child of process 3. On Diagram 3, the processes would be numbered 3.1, 3.2, 3.3 and so on.

Creating Child Diagram • External entities are usually not shown on the child diagrams below Diagram 0. • If the parent process has data flow connecting to a data store, the child diagram may include the data store as well. • Each process on a lower-level diagram may be exploded to create another child diagram. • A lower-level diagram may contain data stores not shown on the parent process, such as: • A file containing a table of information (such as a tax table).

DFD \ Rules • Basic rules that apply to all DFDs: • Inputs to a process are always different than outputs • Objects always have a unique name • In order to keep the diagram uncluttered, you can repeat data stores and data flows on a diagram

DFD • Process • No process can have only outputs (a miracle) • No process can have only inputs (black hole) • A process has a verb phrase label • Data Store • Data cannot be moved from one store to another • Data cannot move from an outside source to a data store • Data cannot move directly from a data store to sink • Data store has a noun phrase label

DFD • Source/Sink • Data cannot move directly from a source to a sink • A source/sink has a noun phrase label • Data Flow • A data flow has only one direction of flow between symbols • A fork means that exactly the same data go from a common location to two or more processes, data stores, or sources/sinks

DFD • Data Flow (Continued) • A join means that exactly the same data come from any two or more different processes, data stores or sources/sinks to a common location • A data flow cannot go directly back to the same process it leaves • A data flow to a data store means update • A data flow from a data store means retrieve or use • A data flow has a noun phrase label

Data Dictionary • This supplements the DFD by giving an organized listing of all data elements in the system. • It describes the meaning of the data flows and stores on the DFD. • It describes the composition of aggregate packets of data moving along the flows e.g. address might be described as house number, street, town, country etc.

Data Dictionary • The data dictionary is a reference work of data about data (metadata) • It collects, coordinates, and confirms what a specific data term means to different people in the organization.

Data Dictionary The data dictionary may be used for the following reasons: • Provide documentation. • Eliminate redundancy. • Validate the data flow diagram. • Provide a starting point for developing screens and reports. • To develop the logic for DFD processes.

Data Dictionary Data dictionaries contain: • Data flow. • Data structures. • Elements. • Data stores.

Data Dictionary Data structures: • Data structures are a group of smaller structures and elements. • An algebraic notation is used to represent the data structure.

Data Dictionary \ Notation • =Consist of • +and • ( ) “Parentheses” optional ( may or may not be present ) • { } “Braces” iteration ( repeated a number of times ) • [ ] “Brackets” (select one of several alternatives ) • ** a comment. • @ identifier ( key field for a store ) • | (separates the choices in [ ])

Data Dictionary \ Notation Data Dictionary example Customer Name = First Name + (Middle Initial) + Last Name Address = Street + (Apartment) + City + State + Zip + (Zip Expansion) + (Country) Telephone = Area code + Local number

Data Dictionary \ Notation Data Dictionary example • name = title + first_name + ( middle_initial ) + last_name. • title = [Mr|Mrs|Ms|Miss|Dr|Prof] • first_name = { legal_characters } • middle_initial = aplha_character. • last_name = { legal_characters } • legal_characters = [A-Z|a-z|’| |] • alpha_character = [A-Z] • name = * name for dispatch purposes *

Data Dictionary \ Example

Data Dictionary Data Elements:- • Data elements should be defined with descriptive information, length and type of data information, validation criteria, and default values. • Each element should be defined once in the data dictionary.

Information System Analysis