SlideShare a Scribd company logo
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
ASSIGNMENT
DRIVE SPRING 2016
PROGRAM BACHELOR OF BUSINESS ADMINISTRATION (BBA)
SEMESTER IV
SUBJECT CODE & NAME BB0020– MANAGING INFORMATION
BK ID B0099
CREDITS 4
MARKS 60
Note: Answer all questions. Kindly note that answers for 10 marks questions should be
approximately of 400 words. Each question is followed by evaluation scheme.
Question.1. Define Data. Explain the different types of data.
Answer:Data are basic valuesorfacts.Note that the term 'data' is considered plural in the scientific
community,asin'the data are collected',not'the data iscollected'; however, not everyone follows
this, so sometimes you'll see data used as singular.
Everytask a computercarriesout workswithdata insome way.Without data, a computer would be
pretty useless. It is, therefore, important to understand how to represent and organize data. This
lesson will look at different types of data used in computer systems, how they are represented in
digital form, and how they are organized in databases.
Analog vs. Digital Data
There are two general ways to represent data:
Question.2. With a neat diagram explain the communication
process.
Answer:Communicationisthe artof transmittinginformation, ideas and attitudes from one person
to another.Educationwithitscorrelatedactivitiesof teachingandlearning,involvescommunication
as well as reciprocal interacting between the teacher and pupils, as channel of realizing its
objectives. The term “communication’ has been
Question.3. Explain the different types of information approaches
Answer:Aninformationsystem(IS) isanyorganized system for the collection, organization, storage
and communication of information. More specifically, it is the study of complementary networks
that people and organizations use to collect, filter, process, create and distribute data.
A computer information system is a system composed of people and computers that processes or
interpretsinformation. The term is also sometimes used in more restricted senses to refer to only
the software used to run a computerized database or to
Question.4. a. Explain five principles of information.
Answer:Many large corporations with significant dependency on intellectual property and
personally identifiable information are struggling with protecting their data. Improvements in
attackerproficiency,increasingnumbers of analyticssystemsstoringsensitive data, and continually
evolvingriskswithcloudcomputing,mobilityandoutsourcingmake defense capabilities difficult to
build and maintain. Information security leaders must apply both their expertise and influence
wisely: identifying and targeting the high priority
b. Information retrieval process
Answer:Information retrieval (IR) is the activity of obtaining information resources relevant to an
information need from a collection of information resources. Searches can be based on or on full-
text (or other content-based) indexing.
Automated information retrieval systems are used to reduce what has been called "information
overload".Manyuniversitiesandpubliclibrariesuse IRsystemsto provide access to books, journals
and other documents. Web search engines are the most visible IR applications.
Question.5. Write short notes on the following:
a. Expenditure reports
Answer:
The Expenditure Report is a graphical representation of the percentages of the different kinds of
expendituresmade bycandidate/committees. This report has been categorized on the basis of the
types of expenditure.
Contribution Refunds
A contribution may be refunded under the following circumstances:
The original check is returned uncashed;
A contribution was made that exceeded the
b. Predictive reports
Answer:Predictive analytics and reports is an area of data mining that deals with extracting
information from data and using it to predict trends and behavior patterns. Often the unknown
event of interest is in the future, but predictive analytics can be applied to any type of unknown
whetheritbe inthe past, presentorfuture.Forexample,identifyingsuspectsafteracrime has been
committed, or credit
c. Demand reports
Answer:Demand reports show the demand for courses at the end of the scheduler. They are used
duringthe course adjustmentprocesstodetermine the need for making changes to courses. These
reports are loaded to the INFODESK for review by the Departments & Schools rather than printed
and distributed. There are a
d. Hybrid reports
Answer:With the use of hybrid model as a tool for visualisation we come to the possibility of
creatingreportswiththe insightin data according to the chosen criteria, regardless of their source,
whereby it is possible to easily compare data from different systems, without to need of take
account of the systemtheycome from.Forexample,reportscanshow companies’achievements on
the lowestlevels(nativelylocatedinDataWarehouse),togetherwithplandata on higher levels and
different “what-if” analysis. These kind
e. Trend report
Answer:Trend analysis or report is the practice of collecting information and attempting to spot a
pattern, or trend, in the
Question.6. Explain the activities of knowledge management cycle.
Answer:Knowledge management cycle is a process of transforming information into knowledge
within an organization. It explains how knowledge is captured, processed, and distributed in an
organization.Inthischapter,we will discussthe prominentmodelsof knowledge managementcycle.
Till date,fourmodelshave beenselected based on their ability to meet the growing demands. The
four models are the Zack, from Meyer and
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601

More Related Content

What's hot

Machine Learning: Considerations for Fairly and Transparently Expanding Acces...
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...Machine Learning: Considerations for Fairly and Transparently Expanding Acces...
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...
QuantUniversity
 
Machine Learning
Machine Learning Machine Learning
Machine Learning
AyanGain
 
Database
DatabaseDatabase
Database
Respa Peter
 
DataMining Techniq
DataMining TechniqDataMining Techniq
DataMining Techniq
Respa Peter
 
Internship project report,Predictive Modelling
Internship project report,Predictive ModellingInternship project report,Predictive Modelling
Internship project report,Predictive Modelling
Amit Kumar
 
Data science lecture3_doaa_mohey
Data science lecture3_doaa_mohey Data science lecture3_doaa_mohey
Data science lecture3_doaa_mohey
Doaa Mohey Eldin
 
Data science lecture2_doaa_mohey
Data science lecture2_doaa_mohey Data science lecture2_doaa_mohey
Data science lecture2_doaa_mohey
Doaa Mohey Eldin
 
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...
Edureka!
 
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...
Simplilearn
 
Support Vector Machine
Support Vector MachineSupport Vector Machine
Support Vector Machine
NilanjanaPradhan2
 
Trending Topics in Machine Learning
Trending Topics in Machine LearningTrending Topics in Machine Learning
Trending Topics in Machine Learning
Techsparks
 
End-to-End Machine Learning Project
End-to-End Machine Learning ProjectEnd-to-End Machine Learning Project
End-to-End Machine Learning Project
Eng Teong Cheah
 
IRJET- Improved Real-Time Twitter Sentiment Analysis using ML & Word2Vec
IRJET-  	  Improved Real-Time Twitter Sentiment Analysis using ML & Word2VecIRJET-  	  Improved Real-Time Twitter Sentiment Analysis using ML & Word2Vec
IRJET- Improved Real-Time Twitter Sentiment Analysis using ML & Word2Vec
IRJET Journal
 
Guy Riese Literature Review
Guy Riese Literature ReviewGuy Riese Literature Review
Guy Riese Literature Review
guyrie
 
Applications of machine learning
Applications of machine learningApplications of machine learning
Applications of machine learning
SakshiTiwari63
 
Data Mining In Market Research
Data Mining In Market ResearchData Mining In Market Research
Data Mining In Market Research
kevinlan
 
Machine Learning Training Bootcamp
Machine Learning Training BootcampMachine Learning Training Bootcamp
Machine Learning Training Bootcamp
Tonex
 
Data mining: Classification and prediction
Data mining: Classification and predictionData mining: Classification and prediction
Data mining: Classification and prediction
DataminingTools Inc
 
Regression with Microsoft Azure & Ms Excel
Regression with Microsoft Azure & Ms ExcelRegression with Microsoft Azure & Ms Excel
Regression with Microsoft Azure & Ms Excel
Dr. Abdul Ahad Abro
 
Machine Learning Applications in Credit Risk
Machine Learning Applications in Credit RiskMachine Learning Applications in Credit Risk
Machine Learning Applications in Credit Risk
QuantUniversity
 

What's hot (20)

Machine Learning: Considerations for Fairly and Transparently Expanding Acces...
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...Machine Learning: Considerations for Fairly and Transparently Expanding Acces...
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...
 
Machine Learning
Machine Learning Machine Learning
Machine Learning
 
Database
DatabaseDatabase
Database
 
DataMining Techniq
DataMining TechniqDataMining Techniq
DataMining Techniq
 
Internship project report,Predictive Modelling
Internship project report,Predictive ModellingInternship project report,Predictive Modelling
Internship project report,Predictive Modelling
 
Data science lecture3_doaa_mohey
Data science lecture3_doaa_mohey Data science lecture3_doaa_mohey
Data science lecture3_doaa_mohey
 
Data science lecture2_doaa_mohey
Data science lecture2_doaa_mohey Data science lecture2_doaa_mohey
Data science lecture2_doaa_mohey
 
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...
 
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...
 
Support Vector Machine
Support Vector MachineSupport Vector Machine
Support Vector Machine
 
Trending Topics in Machine Learning
Trending Topics in Machine LearningTrending Topics in Machine Learning
Trending Topics in Machine Learning
 
End-to-End Machine Learning Project
End-to-End Machine Learning ProjectEnd-to-End Machine Learning Project
End-to-End Machine Learning Project
 
IRJET- Improved Real-Time Twitter Sentiment Analysis using ML & Word2Vec
IRJET-  	  Improved Real-Time Twitter Sentiment Analysis using ML & Word2VecIRJET-  	  Improved Real-Time Twitter Sentiment Analysis using ML & Word2Vec
IRJET- Improved Real-Time Twitter Sentiment Analysis using ML & Word2Vec
 
Guy Riese Literature Review
Guy Riese Literature ReviewGuy Riese Literature Review
Guy Riese Literature Review
 
Applications of machine learning
Applications of machine learningApplications of machine learning
Applications of machine learning
 
Data Mining In Market Research
Data Mining In Market ResearchData Mining In Market Research
Data Mining In Market Research
 
Machine Learning Training Bootcamp
Machine Learning Training BootcampMachine Learning Training Bootcamp
Machine Learning Training Bootcamp
 
Data mining: Classification and prediction
Data mining: Classification and predictionData mining: Classification and prediction
Data mining: Classification and prediction
 
Regression with Microsoft Azure & Ms Excel
Regression with Microsoft Azure & Ms ExcelRegression with Microsoft Azure & Ms Excel
Regression with Microsoft Azure & Ms Excel
 
Machine Learning Applications in Credit Risk
Machine Learning Applications in Credit RiskMachine Learning Applications in Credit Risk
Machine Learning Applications in Credit Risk
 

Similar to Bb0020 managing information

Real World End to End machine Learning Pipeline
Real World End to End machine Learning PipelineReal World End to End machine Learning Pipeline
Real World End to End machine Learning Pipeline
Srivatsan Srinivasan
 
Training in Analytics and Data Science
Training in Analytics and Data ScienceTraining in Analytics and Data Science
Training in Analytics and Data Science
Ajay Ohri
 
Mb0047 management information system
Mb0047   management information systemMb0047   management information system
Mb0047 management information system
smumbahelp
 
Credit card fraud detection using python machine learning
Credit card fraud detection using python machine learningCredit card fraud detection using python machine learning
Credit card fraud detection using python machine learning
Sandeep Garg
 
Evidence Based Healthcare Design
Evidence Based Healthcare DesignEvidence Based Healthcare Design
Evidence Based Healthcare Design
Carmen Martin
 
Mi0036 business intelligence tools
Mi0036  business intelligence toolsMi0036  business intelligence tools
Mi0036 business intelligence tools
smumbahelp
 
Introduction to Data Analytics and data analytics life cycle
Introduction to Data Analytics and data analytics life cycleIntroduction to Data Analytics and data analytics life cycle
Introduction to Data Analytics and data analytics life cycle
Dr. Radhey Shyam
 
Business Intelligence
Business IntelligenceBusiness Intelligence
Business Intelligence
Sukirti Garg
 
Health Plan Survey Paper
Health Plan Survey PaperHealth Plan Survey Paper
Health Plan Survey Paper
Lisa Olive
 
Self Study Business Approach to DS_01022022.docx
Self Study Business Approach to DS_01022022.docxSelf Study Business Approach to DS_01022022.docx
Self Study Business Approach to DS_01022022.docx
Shanmugasundaram M
 
KIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdf
KIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdfKIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdf
KIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdf
Dr. Radhey Shyam
 
Bba205 management information system
Bba205  management information systemBba205  management information system
Bba205 management information system
smumbahelp
 
Mb0047 management information system
Mb0047   management information systemMb0047   management information system
Mb0047 management information system
smumbahelp
 
Mb0047 management information system
Mb0047   management information systemMb0047   management information system
Mb0047 management information system
smumbahelp
 
1 UNIT-DSP.pptx
1 UNIT-DSP.pptx1 UNIT-DSP.pptx
1 UNIT-DSP.pptx
PothyeswariPothyes
 
Om0011 enterprise resource planning
Om0011  enterprise resource planningOm0011  enterprise resource planning
Om0011 enterprise resource planning
smumbahelp
 
Assignment 2 mis - doha 2018
Assignment 2  mis - doha  2018Assignment 2  mis - doha  2018
Assignment 2 mis - doha 2018
smumbahelp
 
Assignment 2 mis - doha 2018
Assignment 2  mis - doha  2018Assignment 2  mis - doha  2018
Assignment 2 mis - doha 2018
smumbahelp
 
DATASCIENCE vs BUSINESS INTELLIGENCE.pptx
DATASCIENCE vs BUSINESS INTELLIGENCE.pptxDATASCIENCE vs BUSINESS INTELLIGENCE.pptx
DATASCIENCE vs BUSINESS INTELLIGENCE.pptx
OTA13NayabNakhwa
 
Om0011 enterprise resource planning
Om0011  enterprise resource planningOm0011  enterprise resource planning
Om0011 enterprise resource planning
smumbahelp
 

Similar to Bb0020 managing information (20)

Real World End to End machine Learning Pipeline
Real World End to End machine Learning PipelineReal World End to End machine Learning Pipeline
Real World End to End machine Learning Pipeline
 
Training in Analytics and Data Science
Training in Analytics and Data ScienceTraining in Analytics and Data Science
Training in Analytics and Data Science
 
Mb0047 management information system
Mb0047   management information systemMb0047   management information system
Mb0047 management information system
 
Credit card fraud detection using python machine learning
Credit card fraud detection using python machine learningCredit card fraud detection using python machine learning
Credit card fraud detection using python machine learning
 
Evidence Based Healthcare Design
Evidence Based Healthcare DesignEvidence Based Healthcare Design
Evidence Based Healthcare Design
 
Mi0036 business intelligence tools
Mi0036  business intelligence toolsMi0036  business intelligence tools
Mi0036 business intelligence tools
 
Introduction to Data Analytics and data analytics life cycle
Introduction to Data Analytics and data analytics life cycleIntroduction to Data Analytics and data analytics life cycle
Introduction to Data Analytics and data analytics life cycle
 
Business Intelligence
Business IntelligenceBusiness Intelligence
Business Intelligence
 
Health Plan Survey Paper
Health Plan Survey PaperHealth Plan Survey Paper
Health Plan Survey Paper
 
Self Study Business Approach to DS_01022022.docx
Self Study Business Approach to DS_01022022.docxSelf Study Business Approach to DS_01022022.docx
Self Study Business Approach to DS_01022022.docx
 
KIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdf
KIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdfKIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdf
KIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdf
 
Bba205 management information system
Bba205  management information systemBba205  management information system
Bba205 management information system
 
Mb0047 management information system
Mb0047   management information systemMb0047   management information system
Mb0047 management information system
 
Mb0047 management information system
Mb0047   management information systemMb0047   management information system
Mb0047 management information system
 
1 UNIT-DSP.pptx
1 UNIT-DSP.pptx1 UNIT-DSP.pptx
1 UNIT-DSP.pptx
 
Om0011 enterprise resource planning
Om0011  enterprise resource planningOm0011  enterprise resource planning
Om0011 enterprise resource planning
 
Assignment 2 mis - doha 2018
Assignment 2  mis - doha  2018Assignment 2  mis - doha  2018
Assignment 2 mis - doha 2018
 
Assignment 2 mis - doha 2018
Assignment 2  mis - doha  2018Assignment 2  mis - doha  2018
Assignment 2 mis - doha 2018
 
DATASCIENCE vs BUSINESS INTELLIGENCE.pptx
DATASCIENCE vs BUSINESS INTELLIGENCE.pptxDATASCIENCE vs BUSINESS INTELLIGENCE.pptx
DATASCIENCE vs BUSINESS INTELLIGENCE.pptx
 
Om0011 enterprise resource planning
Om0011  enterprise resource planningOm0011  enterprise resource planning
Om0011 enterprise resource planning
 

Recently uploaded

Advanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docxAdvanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docx
adhitya5119
 
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdfANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
Priyankaranawat4
 
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPLAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
RAHUL
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
Nguyen Thanh Tu Collection
 
How to deliver Powerpoint Presentations.pptx
How to deliver Powerpoint  Presentations.pptxHow to deliver Powerpoint  Presentations.pptx
How to deliver Powerpoint Presentations.pptx
HajraNaeem15
 
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
PECB
 
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Dr. Vinod Kumar Kanvaria
 
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxChapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Mohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
 
PCOS corelations and management through Ayurveda.
PCOS corelations and management through Ayurveda.PCOS corelations and management through Ayurveda.
PCOS corelations and management through Ayurveda.
Dr. Shivangi Singh Parihar
 
MARY JANE WILSON, A “BOA MÃE” .
MARY JANE WILSON, A “BOA MÃE”           .MARY JANE WILSON, A “BOA MÃE”           .
MARY JANE WILSON, A “BOA MÃE” .
Colégio Santa Teresinha
 
How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience
Wahiba Chair Training & Consulting
 
How to Fix the Import Error in the Odoo 17
How to Fix the Import Error in the Odoo 17How to Fix the Import Error in the Odoo 17
How to Fix the Import Error in the Odoo 17
Celine George
 
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movieFilm vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
Nicholas Montgomery
 
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptxPrésentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
siemaillard
 
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
GeorgeMilliken2
 
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 InventoryHow to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
Celine George
 
clinical examination of hip joint (1).pdf
clinical examination of hip joint (1).pdfclinical examination of hip joint (1).pdf
clinical examination of hip joint (1).pdf
Priyankaranawat4
 
The Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collectionThe Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collection
Israel Genealogy Research Association
 
How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17
Celine George
 
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdfবাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
eBook.com.bd (প্রয়োজনীয় বাংলা বই)
 

Recently uploaded (20)

Advanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docxAdvanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docx
 
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdfANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
 
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPLAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
 
How to deliver Powerpoint Presentations.pptx
How to deliver Powerpoint  Presentations.pptxHow to deliver Powerpoint  Presentations.pptx
How to deliver Powerpoint Presentations.pptx
 
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
 
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
 
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxChapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
 
PCOS corelations and management through Ayurveda.
PCOS corelations and management through Ayurveda.PCOS corelations and management through Ayurveda.
PCOS corelations and management through Ayurveda.
 
MARY JANE WILSON, A “BOA MÃE” .
MARY JANE WILSON, A “BOA MÃE”           .MARY JANE WILSON, A “BOA MÃE”           .
MARY JANE WILSON, A “BOA MÃE” .
 
How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience
 
How to Fix the Import Error in the Odoo 17
How to Fix the Import Error in the Odoo 17How to Fix the Import Error in the Odoo 17
How to Fix the Import Error in the Odoo 17
 
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movieFilm vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
 
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptxPrésentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
Présentationvvvvvvvvvvvvvvvvvvvvvvvvvvvv2.pptx
 
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
 
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 InventoryHow to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
 
clinical examination of hip joint (1).pdf
clinical examination of hip joint (1).pdfclinical examination of hip joint (1).pdf
clinical examination of hip joint (1).pdf
 
The Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collectionThe Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collection
 
How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17
 
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdfবাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
 

Bb0020 managing information

  • 1. Dear students get fully solved assignments Send your semester & Specialization name to our mail id : help.mbaassignments@gmail.com or call us at : 08263069601 ASSIGNMENT DRIVE SPRING 2016 PROGRAM BACHELOR OF BUSINESS ADMINISTRATION (BBA) SEMESTER IV SUBJECT CODE & NAME BB0020– MANAGING INFORMATION BK ID B0099 CREDITS 4 MARKS 60 Note: Answer all questions. Kindly note that answers for 10 marks questions should be approximately of 400 words. Each question is followed by evaluation scheme. Question.1. Define Data. Explain the different types of data. Answer:Data are basic valuesorfacts.Note that the term 'data' is considered plural in the scientific community,asin'the data are collected',not'the data iscollected'; however, not everyone follows this, so sometimes you'll see data used as singular. Everytask a computercarriesout workswithdata insome way.Without data, a computer would be pretty useless. It is, therefore, important to understand how to represent and organize data. This lesson will look at different types of data used in computer systems, how they are represented in digital form, and how they are organized in databases. Analog vs. Digital Data There are two general ways to represent data: Question.2. With a neat diagram explain the communication process. Answer:Communicationisthe artof transmittinginformation, ideas and attitudes from one person to another.Educationwithitscorrelatedactivitiesof teachingandlearning,involvescommunication
  • 2. as well as reciprocal interacting between the teacher and pupils, as channel of realizing its objectives. The term “communication’ has been Question.3. Explain the different types of information approaches Answer:Aninformationsystem(IS) isanyorganized system for the collection, organization, storage and communication of information. More specifically, it is the study of complementary networks that people and organizations use to collect, filter, process, create and distribute data. A computer information system is a system composed of people and computers that processes or interpretsinformation. The term is also sometimes used in more restricted senses to refer to only the software used to run a computerized database or to Question.4. a. Explain five principles of information. Answer:Many large corporations with significant dependency on intellectual property and personally identifiable information are struggling with protecting their data. Improvements in attackerproficiency,increasingnumbers of analyticssystemsstoringsensitive data, and continually evolvingriskswithcloudcomputing,mobilityandoutsourcingmake defense capabilities difficult to build and maintain. Information security leaders must apply both their expertise and influence wisely: identifying and targeting the high priority b. Information retrieval process Answer:Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on or on full- text (or other content-based) indexing. Automated information retrieval systems are used to reduce what has been called "information overload".Manyuniversitiesandpubliclibrariesuse IRsystemsto provide access to books, journals and other documents. Web search engines are the most visible IR applications. Question.5. Write short notes on the following:
  • 3. a. Expenditure reports Answer: The Expenditure Report is a graphical representation of the percentages of the different kinds of expendituresmade bycandidate/committees. This report has been categorized on the basis of the types of expenditure. Contribution Refunds A contribution may be refunded under the following circumstances: The original check is returned uncashed; A contribution was made that exceeded the b. Predictive reports Answer:Predictive analytics and reports is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whetheritbe inthe past, presentorfuture.Forexample,identifyingsuspectsafteracrime has been committed, or credit c. Demand reports Answer:Demand reports show the demand for courses at the end of the scheduler. They are used duringthe course adjustmentprocesstodetermine the need for making changes to courses. These reports are loaded to the INFODESK for review by the Departments & Schools rather than printed and distributed. There are a d. Hybrid reports Answer:With the use of hybrid model as a tool for visualisation we come to the possibility of creatingreportswiththe insightin data according to the chosen criteria, regardless of their source, whereby it is possible to easily compare data from different systems, without to need of take account of the systemtheycome from.Forexample,reportscanshow companies’achievements on the lowestlevels(nativelylocatedinDataWarehouse),togetherwithplandata on higher levels and different “what-if” analysis. These kind e. Trend report
  • 4. Answer:Trend analysis or report is the practice of collecting information and attempting to spot a pattern, or trend, in the Question.6. Explain the activities of knowledge management cycle. Answer:Knowledge management cycle is a process of transforming information into knowledge within an organization. It explains how knowledge is captured, processed, and distributed in an organization.Inthischapter,we will discussthe prominentmodelsof knowledge managementcycle. Till date,fourmodelshave beenselected based on their ability to meet the growing demands. The four models are the Zack, from Meyer and Dear students get fully solved assignments Send your semester & Specialization name to our mail id : help.mbaassignments@gmail.com or call us at : 08263069601