Chapter z01 z– zSpecify zthe zQuestion: zUsing zBusiness zAnalytics zto zAddress zBusiness
zQuestions
Chapter z1 zEnd-of-Chapter zAssignment zSolutions
Multiple zChoice zQuestions
1. (LO z1.1) zA zcoordinated, zstandardized zset zof zactivities zconducted zby zboth zpeople zand zequipment zto
zaccomplish zazspecific zbusiness ztask zis zcalled .
a. business zprocesses
b. business zanalysis
c. business zprocedure
d. business zvalue
2. (LO z1.2) zAccording zto zthe zinformation zvalue zchain, zdata zcombined zwith zcontext zis
a. Information.
b. Knowledge.
c. Insight.
d. Value.
3. (LO z1.5) zWhich zphase zof zthe zSOAR zanalytics zmodel zaddresses zthe zproper zway zto zcommunicate
zresults zto zthezdecision zmaker?
a. Specify zthe zquestion
b. Obtain zthe zdata
c. Analyze zthe zdata
d. Report zthe zresults
4. (LO z1.5) zWhich zphase zof zthe zSOAR zanalytics zmodel zinvolves zfinding zthe zmost zappropriate zdata zneeded
zto zaddresszthe zbusiness zquestion?
a. Specify zthe zquestion
b. Obtain zthe zdata
c. Analyze zthe zdata
d. Report zthe zresults
5. (LO z1.5) zWhich zquestions zseek zinformation zabout zTesla’s zsales zin zthe znext zquarter?
a. What zhappened? zWhat zis zhappening?
b. Why zdid zit zhappen? zWhat zare zthe zcauses zof zpast zresults?
c. Will zit zhappen zin zthe zfuture? zWhat zis zthe zprobability zsomething zwill zhappen? zCan zwe
zforecast zwhatzwill zhappen?
d. What zshould zwe zdo, zbased zon zwhat zwe zexpect zwill zhappen? zHow zdo zwe zoptimize zour
zperformance zbasedzon zpotential zconstraints?
6. (LO z1.5) zWhich zquestions zseek zinformation zon zthe zrouting zof zproducts zfrom zQueretaro, zMexico zto
zChicago,zUnited zStates zin zthe zlast zquarter?
a. What zhappened? zWhat zis zhappening?
b. Why zdid zit zhappen? zWhat zare zthe zcauses zof zpast zresults?
c. Will zit zhappen zin zthe zfuture? zWhat zis zthe zprobability zsomething zwill zhappen? zCan zwe zforecast
zwhat zwillzhappen?
d. What zshould zwe zdo, zbased zon zwhat zwe zexpect zwill zhappen? zHow zdo zwe zoptimize zour
zperformance zbasedzon zpotential zconstraints?
© zMcGraw zHill zLLC. zAll zrights zreserved. zNo zreproduction zor zdistribution zwithout zthe zprior zwritten zconsent zof
zMcGraw zHill zLLC.
1
, Chapter z01 z– zSpecify zthe zQuestion: zUsing zBusiness zAnalytics zto zAddress zBusiness
zQuestions
7. (LO z1.5) zWhich zquestions zask zwhy znet zincome zis zincreasing zwhen zrevenues zare zdecreasing,
zcounter ztozexpectations?
a. What zhappened? zWhat zis zhappening?
b. Why zdid zit zhappen? zWhat zare zthe zcauses zof zpast zresults?
c. Will zit zhappen zin zthe zfuture? zWhat zis zthe zprobability zsomething zwill zhappen? zCan zwe zforecast
zwhat zwillzhappen?
d. What zshould zwe zdo, zbased zon zwhat zwe zexpect zwill zhappen? zHow zdo zwe zoptimize zour
zperformance zbasedzon zpotential zconstraints?
8. (LO z1.5) zWhich zquestions zhelp zmanagers zunderstand zhow zto zorganize zfuture zshipments zbased zon
zexpectedzdemand?
a. What zhappened? zWhat zis zhappening?
b. Why zdid zit zhappen? zWhat zare zthe zcauses zof zpast zresults?
c. Will zit zhappen zin zthe zfuture? zWhat zis zthe zprobability zsomething zwill zhappen? zCan zwe zforecast
zwhat zwillzhappen?
d. What zshould zwe zdo, zbased zon zwhat zwe zexpect zwill zhappen? zHow zdo zwe zoptimize zour
zperformancezbased zon zpotential zconstraints?
9. (LO z1.5) zWhich zterm zrefers zto zthe zcombined zaccuracy, zvalidity, zand zconsistency zof zdata zstored zand
zused zoverztime?
a. Data zintegrity
b. Data zoverload
c. Data zvalue
d. Information zvalue
10. (LO z1.3) zA zspecialist zwho zknows zhow zto zwork zwith, zmanipulate, zand zstatistically ztest zdata zis za
a. decision zmaker.
b. data zscientist.
c. data zanalyst.
d. decision zscientist.
11. (LO z1.4) zWhich ztype zof zanalysts zpredicts zthe zamount zof zmoney zthat za zcompany zwill zreceive zfrom zits
zcustomers ztozhelp zmanagement zevaluate zfuture zinvestments zbased zon zexpected zinvestment zperformance,
zsuch zas zinvestments zin zequipment zor zemployee ztraining?
a. Marketing zanalyst
b. Operations zanalyst
c. Financial zanalyst
d. Accounting zanalyst
12. (LO z1.4) zWhich ztype zof zanalyst zaddresses zquestions zregarding ztax zand zauditing?
a. Marketing zanalyst
b. Operations zanalyst
c. Financial zanalyst
d. Accounting zanalyst
13. (LO z1.5) zSuppose za zcompany zhas ztimely zproduct zreviews zthat zare zavailable zwhen zneeded, zbut zthe
zreviews zarezbiased. zThese zproduct zreviews zare zwhich ztype zof zdata?
a. Reliable
b. Relevant
c. Curated
d. Consistent
© zMcGraw zHill zLLC. zAll zrights zreserved. zNo zreproduction zor zdistribution zwithout zthe zprior zwritten zconsent zof zMcGraw zHill
zLLC.
2
, Chapter z01 z– zSpecify zthe zQuestion: zUsing zBusiness zAnalytics zto zAddress zBusiness
zQuestions
14. (LO z1.6) zWhich zcommon zvisualization ztype zshows ztrends zin zvalues zover ztime?
a. Line zgraph
b. Scatterplot
c. Pie zchart
d. Bar zchart
15. (LO z1.6) zWhich zcommon zvisualization ztype zshows zthe zcomposition zof zvalues zover ztime?
a. Line zgraph
b. Scatterplot
c. Pie zchart
d. Bar zchart
Discussion zQuestions
1. (LO z1.1) zGive zfive zexamples zof zbusiness zprocesses zat zTesla. zHow zdo zthey zcreate zbusiness zvalue zfor
zTesla zand zitszshareholders?
SuggestedzSolution:
Answers zwill zvary,
1. Tesla zprocures zautomobile zparts zfrom zauto zsuppliers z– Because zof zTesla’s zunique zstyling, zgetting
zqualityzparts zfrom zits zsuppliers zon za ztimely zbasis zwill zsupport zits zmanufacturing zbusiness.
2. Tesla zmanufactures zbatteries zfor zits zelectric zvehicle zat zits zdesired zspecifications z– zThe zquantity
zand zqualityzof zits zbatteries zare zof zcritical zimportance zto zTesla.
3. Accepting zand zprocessing zpreorders zfrom zits zcustomers z– zTesla zreceives zsome zindication zof zthe
zdemand zforzeach zof zits zproducts, zthat zhelps zwith zplanning.
4. Tesla zmarkets zits zproducts z– zTesla zworks zto zget zTesla zproducts zin zthe zfront zof zmind zfor zits zcustomers.
5. Tesla zcar zand ztruck zdesign z– zTesla zdesigns zits zautomobiles zin za zway zthat zwill zappeal zto zits
zcustomers z(forzexample, zCybertruck).
2. (LO z1.2) zExplain zthe zinformation zvalue zchain zby zsummarizing zhow zdata zare ztransformed zinto zknowledge
zinsights zfor zdecision-making. zUse zthe zexample zof za zbook zreview zon zAmazon zand zhow zit zmight zlead
zAmazon zto zdecide zhowzmany zof zthose zbooks zto zstock zat zits zwarehouses.
SuggestedzSolution:
Amazon zallows zthose zwho zpurchase zbooks zand zother zproducts zat zits zwebsite zto zgive zproduct zreviews
zand zassignzproduct zratings. zThe zproduct zreviews zmay zprovide ztext zwhich ztextual zanalytics zcould zuse zto
zunderstand zthe zgeneral zsentiment zabout zthe zspecific zbook. zThe zproduct zrating zcould zalso zbe zused zto
zunderstand zhow zwell zthe zbook zis zliked zby zverified zbuyers. zStatistical zcorrelations zcould zbe zrun zamong
zproduct zreview zsentiment, zproduct zratings zand zproduct zsales zto zhelp zforecast zdemand zfor zthe zproduct.
z This zwill zhelp zAmazon zdetermine zhow zmanyzbooks zto zkeep zin zits zwarehouse zready zfor zdelivery.
This zis zan zexample zof zhow zdata zturns zinto zinformation, zknowledge zand zultimately zhelps zwith zdecision
zmaking.
3. (LO z1.3) zExplain zthe zinformation zvalue zchain zby zsummarizing zhow zdata zare ztransformed zinto zknowledge
zinsightszfor zdecision-making. zUse zthe zexample zof za zbook zreview zof zthis zbook zon zAmazon zand zhow zit
zmight zhelp zthe zpublisher, zMcGraw zHill, zdetermine zwhether zto zrevise zthis zbook zfor za znew, zupdated
zedition zas zthe zdiscipline zof zdata zanalytics zevolves.
© zMcGraw zHill zLLC. zAll zrights zreserved. zNo zreproduction zor zdistribution zwithout zthe zprior zwritten zconsent zof
zMcGraw zHill zLLC.
3
, Chapter z01 z– zSpecify zthe zQuestion: zUsing zBusiness zAnalytics zto zAddress zBusiness
zQuestions
SuggestedzSolution:
McGraw zHill zwill zuse zmany zdeterminants zto zdetermine zhow zwell zeach zone zof zits ztextbooks zare zperforming.
They’ll zlook zat zoverall zsales zof zthe zbook, zcompared zto zcompetitors. zBut zthey zmay zalso zsurvey zusers zto
zdetermine zhow zwell zthe zbook zis zliked, zwhat zis zdeficient zin zthe zbook, zwhat znew ztopics zshould zbe
zconsidered, zetc. zAll ztold, zall zof zthe zdata zwill zbe zput ztogether, zanalyzed, zknowledge zwill zbe zgained, zand za
zdecision zwill zbe zmade.
4. (LO z1.3) zExplain zthe zdifference zbetween za zdecision-maker, za zdata zscientist, zand za zbusiness zanalyst.
zWhat zis zthezrole zof zeach?
SuggestedzSolution:
While zthere zare znot zalways zdefinitive zdistinctions zbetween zthese zthree zpositions, zthe zdecision zmaker
zneeds zquestions zanswered zbefore zthey zcan zmake zdata-informed zdecisions. zThe zdata zscientist zis zmost
zfamiliar zwith zthezdata, zas zthat zis ztheir zspecialty, zcollecting zand zmaintaining zdata zin zdatabases,
zmanipulating, ztransforming zand zanalyzing zdata. zThe zbusiness zanalyst zgenerally zunderstands zthe zbusiness
zand zthe zinformation zneeds zof zthe zdecision zmaker, zbut zalso zunderstands zthe zdata. zThe zbusiness zanalyst
zcan zserve zas za zgo zbetween, zbetween zthe zdecision zmaker zand zthe zdata zscientist, zall zworking ztogether zto
zmake zdata-informed zdecisions.
5. (LO1.4) zCompare zand zcontrast zmarketing zanalytics zwith zaccounting zanalytics. z How zare zthey zsimilar?
zHow zarezthey zdifferent?
SuggestedzSolution:
Both zmarketing zand zaccounting zanalytics zaddress zmanagement zquestions zusing zappropriate zdata zand
zanalytics. zBut zthey zalso zdiffer zfrom zeach zother. zFor zexample, zmarketing zanalytics zare zused zto zaddress
zthe zneeds zof zthe zmarketing zdepartment, zthe zbusiness zof zpromoting zand zselling zproducts zand zservices.
z Marketing zanalytics zis zoften zinvolved zin zproviding zinsights zinto zcustomer zpreferences zand ztrends. zIn
zcontrast, zaccounting zanalytics zuseszbusiness zanalytics zto zhelp zmeasure zaccounting zperformance zand
zaddress zaccounting zquestions, zsuch zas zanalyzingzwhether za zcompany zcommitted zfraud zor zpredicting
zfuture zsales zor zearnings zof za zcompany.
6. (LO1.4) zCompare zand zcontrast zfinancial zanalytics zwith zoperations zanalytics. z. z How zare zthey zsimilar?
zHow zarezthey zdifferent?
SuggestedzSolution:
Both zfinancial zanalytics zand zoperations zanalytics zaddress zmanagement zquestions zusing zappropriate zdata
zand zanalytics. zBut zthey zalso zdiffer zfrom zeach zother. zFor zexample, zfinancial zanalytics zuses zbusiness
zanalytics zto zhelp za zcompany zmeasure zand zevaluate zits zfinancial zperformance, zfrom zpredicting zreceivables
zcollection zfrom zits zcustomers zto zhelping zmanagement zevaluate zfuture zinvestments zbased zon zexpected
zinvestment zperformance. zIn zcontrast, zoperations zanalytics zuses zbusiness zanalytics zto zmeasure zand
zimprove zthe zefficiency zand zeffectiveness zofzthe zcompany’s zoperations, zsince zoperations zis zall zactions
zneeded zto zrun zthe zcompany zand zgenerate zincome.
7. (LO z1.5) zIdentify zthe zfour zsteps zin zthe zSOAR zanalytics zmodel. zExplain zhow zmarketing zanalysts zmight zuse
zthe zSOARzmodel zto zhelp zNetflix zbetter zunderstand zits zcustomers.
4
