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Mid-term Notes

by: Emily Gurnee

Mid-term Notes MGMT 382

Emily Gurnee
GPA 3.46
Management Information Systems
Dr. Dejoie

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About this Document

A compilation of all my notes from the first half of the semester. I studied these and the cases and did very well on the exam.
Management Information Systems
Dr. Dejoie
Study Guide
50 ?




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This 15 page Study Guide was uploaded by Emily Gurnee on Tuesday October 20, 2015. The Study Guide belongs to MGMT 382 at Purdue University taught by Dr. Dejoie in Winter2015. Since its upload, it has received 52 views. For similar materials see Management Information Systems in Business, management at Purdue University.


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Date Created: 10/20/15
MGMT 382 Midterm Notes Technoloov Information amp Societv 11515 3 Great Moments in Human History Nomadic age food amp shelter Flat technological curve 1 First Wave Agrarian Revolution a b Wealth is the land exhaustibleexclusive i Physical vs intangible usability ii Exclusive ownership amp usability Sedentaryljmore stability c Tof er 3rCI wave d e Critical mass of thinking i One thinker in each tribedoing the work all on his own 1 Agrarian Agethinkers can start working togetherDadvancement More complex tools 2 Second Wave Industrial Revolution a b LorhrDQp Wealth land labor capital dimishedexclusive Centralization amp standardization i Massi cationljassembly line ECONOMIES OF SCALE No more quotAge of ArtisianquotDstandards amp specialization Drives down cost This is not the age of the consumer Consumer has to t the tool instead of tool tting consumer 3 Third Wave Information Revolution apem Wealth knowledge nonexhaustiblenonexclusive Demassi cation What you knowgtwhat you have First time intangible has greatest value i Nonexclusive property 1 Exclusive vs nonexclusive Customization i We are able to customize our news feedswe choose ourselves ii Demographics can39t do this as well as customization 1 Purchase history amp personal ratings Amazon Recreate the age of the consumer Technology Progression amp Shock Tof er Future Shock1970 DAs technology speeds up it becomes harder to adjust 01750AD Tech Knowledge 2X 17501890 AD Tech Knowledge 2X 19001950 AD Tech Knowledge 2X 1990quotln the last 50 years there has been more technological advancement than the previous 10000 yearsquot Future Shock the inability to adjust amp adapt to the change in speed of technology Transportation 1900 common manljwalking main form of transportation since the beginning of time Tof er39s 1St FS Generation Since 1900 Horse amp buggy Automobile Airplanetrain Mass transit Jet Space ight Shuttles Space stations We are always thinking about distance in time instead of miles Technology has allowed families to be more spread out amp fragmented If there39s only one opportunity everyone will take it If the opportunity is always there we never take it Notes 12015 quotExpressivequot use of technology what people are doing to get from Pt A to Pt B A Lesson From History The Automobile Measure distance in minutes not miles Isolated wasteland becomes a choice property Swelled the duties and fees of village constables American vocab ivver skid jaywalker Captured American hearts imaginations and pocket books Comfort in automobiles Status symbol Home away from home P vacy Social mobility suburbs Altered cities into unde ned metro areas Many support amp complementary industries Major purchase permanent commitment average ownership of car 35 years quotdisposablequot society Current Example Information Technology Home amp work life Changing image of community Demassi cation Blurred lines between producer amp consumer Shifting product and service Changing images on property Business Philosophy Money manpower materials machinery management amp INFORMATION HnalThouoht Human factorshared experiences Excitement about what technology can do for you Information amp Business 1 Systems Development 2 Datafactua 3 Information a Analyzed data lsubjective b Must know content c Helps us make decisions d Must be understandable relevant accurate timely actionable e Sometimes something can be m data and information 4 Need to be able to process data a Expert systemExecutive Support System strategic Decision Support System tactical mgmt Management Information System ops Transaction nonmanagement b Expert systemExecutive support system i Reasoning ii Critical success factors c Decision support systems quotWhat ifquot 4 Ethical Issues Case Privacy Accuracy Property Access Most cited when talking IT ethics Accuracy 0 Depend amp make decision on it 0 Assume it is always correct 0Libraries do a lot of work in distinguishing fact from ction amp opinion 0 Internet not as reliable 0 Decisions are made based on information about people and data that did not start the decision making process 0 Up to 50 of local police records are inaccurate in some way 0 Data is only as good as the controls and people who put the data into the system 0Credit report look for red ags 0 Computer are not always right 0 Don t say quotthe computer saidquot The computer will eventually replace you ACCESS quotInformation is the means by which we craft our livesquot 0 Basic level literacypublic schools 0 21St centuryljmust be information literate 0 New information standard in society 0 Better access because of better knowledge of available databases Krannert Property 0 Hard to protect Limited bandwidth 0 How do you split this up 0 Sharing bandwidth with garage openers microwaves even though they are not transmitting data quotDisemmindingquot transferring intellectual property into computer o Is it yours anymore 0 Many laws are written from a physical standpoint o How does this affect information 0 FBI cyber criminal video 0 Using the law against itself Copyrights patentsDintangibles but are very speci cnarrow Laws don39t necessarily apply to the common man39s idea to live life 0 Laws are speci c but information is now being used in every aspect of life Privacy More information amp more ways to process data Increase in using info in decision making Threat of exposure by minute description 0 The more pieces of information someone has the more we can infer information 1984 always under observation o quotIf I can control your actions I can eventually change your thoughts 0 Privacy is more of a hot topic in America than abroad 0 We are one of our own threats to our privacy H Svstems Develooment Module Intro to SDLC Granularitv of information how speci c or general the information is Compexity o More detailed at lower levels of organization 0 Less detailed as you move up CompexityDlncrease as you go up in org Riskljincrease as you go up StructureDmore on bottom less on top Routinenesstore on bottom less on top Cost of failureDmore on top TPS transaction processing system most important least interesting How are svstems built Methodologyljleading to a speci c positive outcome consistency routines ways of doing things that hopefully always work Traditional SDLC P P PP NE Identi cation amp Selection Initiation amp Planning Analysis Deggn Implementation Maintenance amp review Identi cation amp Selection What projects will we take on in a given time frame Limited resources Align projects with critical success factors Symptoms vs problems Initiation amp Planning What do I need to pull this off People needed large scale 10 or 100 Time needed Project manager talk about quotpoolsquot of projects Deals about people amp talent Analysis 0 Understanding how systems work 0 Doing homeworkresearch Decision making amp number crunching Deggn What is the best way to solve my problem Often based on Do we have expertise Choosing from several design alternatives Implementation What looks good on paper 5 bot always how it plays out 0 Testing amp revising Maintenance amp Review Ongoing life cycleDrequires upkeep Example transportation to campus Need to think about this earlier in the process Review look at success 0 Did it do what I thought it would do Why or why not Paralysis by analysis American Airlines Waterfall Model ldenti cati on amp Initiation amp I lmplementati I Maintenanc e amp Review Waterfall Method gt Linearno repetition Can go back but cannot redo the whole step Thoroughness Relatively slow methodology Example leaving phone in classroom 0 Quick doublecheck 0 Can39t fully redo it amp check every seat o Is this the rst thing delivered to the customer or the last thing delivered 0 Your product should be quotperfectquot Prototyping Focus on speed of initial delivery lden ca gt Initiation gt on amp amp Analy5is A i I Implementati IL Maintenanc A e amp Review 0 Don t think through everything completely 0 Start with something amp continually check with partner 0 20 questionsWaterfall Model HotColdDPrototyping allowed to make mistakes 0 Able to make quick iterations 0 Not exhaustive research Who uses this 80 of the time o What is the most likely solution 0 Sketch model ne tuning should lead to quicker results amp involve usercustomer much more 0 Continuous conversation 0 Maybe get to use prototype amp play with it Prototypes sketches outlines models 0 Evolutionary morphing into nal product 0 Proof of concept quotthrowawayquot Do not have the components of nal product What does it look like Model sketch design models Problems with Prototyping Leads people to think system will come as soon as it will Avoidance in creating formal documentation Lack of testing Project is often hard to terminate No indication of performance in operational conditions Company resources not often used ef ciently Don39t usually t well with other corporate systems stands alone Set the boundaries on the front end quotI39ll mow your lawn for 20quot Traditional SDLC vs Prototyping Traditional SDLC Linear Structured Rigid Slowmethodicalthorough First delivered is last delivered Prototyping Iterativecyclical Less structure Flexible Fast initial delivery First delivery is rarely last delivered When to Use Traditional SDLC IPO inputs processes outputs wellknown Routine issues Risk of failure low Cost associated with failure low Prototyping IPO not wellknown Nonroutine issues Risk of failure high Cost of failure high Communication problems exist Concrete form is needed to evaluate Prototypingl used more to support systems that deal with higher levels of organization D decision makers don39t speak quotgeekquotD SHOW ME Selfsourcino aka EUD enduser develobment Advantages Requirement determination improvement Increased user participation Increased sense of ownership Increased speed of development sometimes Disadvantages Inadequate expertise Lack of organizational focus Insuf cient design alternatives suboptimal systems Lack of documentation amp external support Outsourcing Advantages Disadvantages Allows company to focus on core competencies Better predictions of future costs Acquisition of leading edge technology quotbuyquot intellect Can reduce technical know how Reduced control Vulnerability of strategic information Increased dependency on other 0 Reduced cost 0 Improve accountability organization VASA Case How do companies go about developing systems and can this stuff be applied to something other than computers 5 Reasons Why Systems Fail 1 Unclear amp Missing Requirements a Missingincorrectly gathered during analysis phase b Can come from misdiagnosing the problem in the identi cation and selection phase addressing a symptom instead of a problem c Art gt Science i Combination amp right balance of beauty amp power ii No documentation iii Design process is faster without documentation 1 Not everyone is literate 2 Failure to Manage Project Scope a Scope creep project expands beyond initial stated requirements from client b Feature creep developers add features that weren39t part of the initial requirements i 3 and 1pound guns on VASA c Also can come from failure to de ne the scope of the project back in initiation and planning or identi cation and selection phase I Need to know info from VASA casi 2 ships were requested to be built 2 large 2 small Cost of nding error Keel runs at bottom and bears lots of weight of the ship Need quotscarfquot joint to make 108 ft keel into 120 ft Cutting off 16 feet is very expensive Some of the decorations weigh more than the g 3 Failure to Manage Project Plan a Plan is living breathing b Best worst probable constant monitoring c d Don t panic VASA Case i More ships amp smaller timeline 1 3D5 ships 2 When you add an additional ships you need 812 years total 3 If you split the work in half it can be completed in 46 yrs BEST CASE SCENARIO 4 SkippingMinimizing SDLC Phases a b To cut time andor costs phases are cut short or eliminated Testing is one of the rst things to be cut along with systems documentation Analysis amp design phases are also victims i Cuts in adequate analysis lead to missing or unclear requirements ii Cuts in design lead to less than optimal solution options or problems in development and implementation Vasa Case only used 30 men in testingneed at least 240 men on board just for gun crew i Prototyping would be better than traditional design for the VASA 5 Changing Technology Have to be forward thinking Can39t stand still for too long Must really understand you industry eld very well VASA double gun deck i Shift up a lot of weight amp changes the center of gravity ii Thing was not thought about iii Center of gravity was only off by 3 ft iv Worse for ship to be taken by enemy than to be sunk apem It39s not E the VASA would sink but WHEN Database De nitions Data meaningful facts text graphics etc Information data processed to be useful in decisionmaking Database organized collection of logically related data Metadata data that describes data Disadvantages of File Processing Programdata dependence 0 All programs maintain metadata for each le they use 0 Different programs working with the same data may not speak the same quotlanguagequot 0 quotscrubbingquot changing the data to a format we can use 0 Data Redundancy Duplication 0 Different systemsprograms have separate copies of the same data Example Accounting amp HR use same data for different purposes but can t interchange because they use different systems 0 Limited Data Sharing o No centralized control of data Lengthy Development Times 0 Programmers must design their own le formats 0 Excessive Program Maintenance 0 80 of information systems budget Problems with Data Dependencv Each application programmers must maintain their own data 0 Each application program needs to include code for the metadata of each le 0 Each application program must have its own processing routines for reading inserting updating and deleting data 0 Lack of coordination and central control Nonstandard le formats Data Redundancv When data changes in one le it usually causes inconsistencies Bad data U Bad information U Bad decisions U Bad Actions SOLUTION Database Approach Central repository of shared data Data is managed by controlling agent Stored in standardized convenient form Requires a Database Management Svstem DBMS Database Management Svstem DBMS data storage amp retrieval system which permits data to be stored nonredundanty while making it appear to the user as if the data is wellintegrated mnrlliaiiiniingi mnrtmllliizecll shared diam Database Approach Advantages of Manages data resources like an operating system manages hardware resources Program39 Data Independence o Metadata stored in DBMS so applications don39t need to worry about data formats 0 Data queriesupdates managed by DBMS so programs don39t need to process data access routines 0 Results in increased app development maintenance productivity mproved Data Sharing 0 Different users get different view of data Enforcement of Standards 0 All data access is done in same way mproved Data Quality 0 Constraints data validation rules 0 Security BackupRecovery Concurrency 0 Disaster recovery is easier Costs amp Risks l39l Database Upfront costs 0 Installation cost amp complexity 0 Conversion cost Ongoing Cost 0 Requires new specialized personnel 0 Need for explicit Backup amp Recovery 0 Organizational Con ict 0 Old habits die hard ER Model Constructs Entity insurance person place object event concept Entity type collection of entities often corresponds to a table Entities 0 Collect data 0 Store data 0 Question data 0 Report data Attribute property or characteristic of an entity type eld in a table Relationship instance link between entities primarykey amp foreign key equivalents in a related table Relationship type category of relationship link between entity types EntityRelationship Modeling Entity noun 0 Relationship verb Entity SHOULD BE 0 An object that will have many instanced on the database 0 An object that will be composed of multiple attributes An object that we are trying to model Entity SHOULD NOT BE 0 User of database 0 Output of database system report Identi ers Kev Attribute that uniquely identi es individual instances of an entity type 0 Simple vs Composite Key 0 Candidate key an attribute that could be a key amp satis es the requirements for being a key 0 Composite key 2 attributes put together Characteristic of Identi ers Will not change in value 0 Will not be null No intelligent identi ers eg containing locations or people that might change 0 Substitute new simple keys for long composite keys Cardinality of Relationships Onetoone 0 Each entity in relationship has exactly one related entity Onetomany o Entity on one side of the relationship can have many related entities but an entity on the other side will have maximum of 1 related entity Manytomany o Entities on both sides of relationship can have many related entities on the other side


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