Question.5457 - Question 1: List and comment on four analytics applications in the retail value chain. Question 2: Define OLAP and OLTP. Explain the relationship between the two. Question 3: What is the intent of the analysis of data that is stored in a data warehouse? Question 4: Describe prescriptive analytics. What are the problems it solves, enablers it uses, and outcomes it generates. Question 5: Describe and define Big Data. Why is a search engine a Big Data application? Question 6: List and describe three levels or categories of analytics that are most often viewed as sequential and independent, but also occasionally seen as overlapping.
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Assignment 1 MGT460: Business Intelligence and Analytics Question 1: List and comment on four analytics applications in the retail value chain.Invento...
Assignment xxx Business xxxxxxxxxxxx and xxxxxxxxx Question xxxx and xxxxxxx on xxxx analytics xxxxxxxxxxxx in xxx retail xxxxx chain xxxxxxxxx Optimization xxxx deals xxxx the xxxxxx availability xx products xxxxxxxxx it xxxxxxx that xxxxxxxx are xxxxxxxxx at xxx right xxxxx and xx the xxxxx time xxx in xxxxxxxxxxx quantity xx avoid xxxxxxxxx and xxxxxxxx while xxxxxxxx carrying xxxxx Price xxxxxxxxxx This xxxxx with xxxxxxxxx demand xxxxxxx based xx priceadjustments xx further xxxxx in xxxxxxx competitive xxxxxx and xxxxxxxxx retailers xxxxxx without xxxxxx customers xxxxxxxx Analytics xxxx deals xxxx customers xxxx and xxxxxxxx It xxxxxxxx purchasing xxxxxxxx customers xxxxxxxxxxx and xxxxxxxxxxxx in xxxxx to xxxxxxxxx marketing xxxxxxxxxx increase xxxxxxxx loyalty xxx improve xxxxxxxxxxxxxxx Inventory xxxxxxxxxx this xxxxx with xxxxxxxx production xx tracking xxxxxxxxx stock xxxxxx and xxxxxxxxx need xxx restock xx also xxxxx with xxxxx reduction xxxxxxxxxx supply xxxxx decisions xxx improve xxxxxxxxxxxx Question xxxxxx OLAP xxx OLTP xxxxxxx the xxxxxxxxxxxx between xxx two xxxx is xx abbreviation xxx Online xxxxxxxxxxx Processing xxxxx OLTP xx an xxxxxxxxxxxx for xxxxxx Analytical xxxxxxxxxx OLAP xx used xx handling xxxx and xxxxx volume xx requests xxx transactions xxx instance xxxxxx order xxxx on xxx other xxxx are xxxxxxxx to xxxxxxx large xxxxxxxx for xxxxxxxx support xxxxxxxx intelligence xxx observe xxxxxx OLAP xxxxxxx leverage xx OLTP xxxxxxx it xxxxx OLAP xxxx while xxxx operates xx day-to-day xxxxxxxxxxx processing xxxx analyzes xxxx data xxx decision-making xxxxxxxx What xx the xxxxxx of xxx analysis xx data xxxx is xxxxxx in x data xxxxxxxxx The xxxxxx of xxxxxxxxx data xxxxxx in xxxx warehouse xx to xxxxxxxxx large xxxxxxx of xxxxxxxxxxxxx data xxxxxxx into xxxxxxxxxx knowledge xxxx is xxxxx at xxxxxxxxxx business xxxxxxx analyze xxxxxx and xxxxxxxxx decision-making xxxxxxxx Describe xxxxxxxxxxxx analytics xxxx are xxx problems xx solves xxxxxxxx it xxxx and xxxxxxxx it xxxxxxxxx Prescriptive xxxxxxxxx is x more xxxxxxxxxxxxx analytics xxxx suggests xxxx specific xxxxxxx leveraging xxxxxxxxxx modeling xxxx analytics xxx optimization xxxxxxxxxx Unlike xxxxxxxxxxx or xxxxxxxxxx analytics xxxxx tell xxxx happened xx what xx likely xx happen xxxxxxxxxxxx analytics xxxxxxxx the xxxx course xx action xx achieve xxxxxxx outcomes xxxxxxxxxxxx analytics xxxxxx problems xxxx evaluating xxxxxxxxx strategies xxx their xxxxxxxx impacts xx limit xxxxx risks xxxxx at xxxxxxx decisions xx difficult xxxxxxxxxx with xxxxxxxx variables xxx cause xxxxxxxxx in xxxxxx chain xxxxxxxxxxxx inventory xxxxxxx and xxxxxxxx allocationPrescriptive xxxxxxxx uses xxxxxxxx like xxxxxxxxx data xxxxxxxxxxx and xxxxxxxx support xxxxxx machine xxxxxxxx optimization xxxxxxxxx predictive xxxxxx and xxxxxxxxxxx Presriptive xxxxxxxx generates xxxxxxxx such xx high xxxxxxxxxxx advantage xxxxxxx revenue xxxxxxxx satisfaction xxxxxxxx strategic xxxxxxxx making xxxx reduction xxx improved xxxxxxxxxxx productivity xxxxxxxx Describe xxx define xxx Data xxx is x search xxxxxx a xxx Data xxxxxxxxxxx Big xxxx refers xx extremely xxxxx complex xxx fast-growing xxxxxxxx that xxxxxxxxxxx data xxxxxxxxxx methods xxxxxx efficiently xxxxxx It xx often xxxxxxx by xxx Vs xx more xxxxxxx Vs xxxxxx massive xxxxxxx of xxxx generated xxxx various xxxxxxx Velocity xxx high xxxxx at xxxxx data xx created xxxxxxxxx and xxxxxxxxx Variety xxx wide xxxxx of xxxxxxxxxx semi-structured xxx unstructured xxxx types x search xxxxxx is x Big xxxx application xxxxxxx it xxxxxxxxx and xxxxxxx billions xx web xxxxx in xxxx time xxxxxxx massive xxxxx volumes xxxx users xxxxxxxxx and xxxx advanced xxxxxxxxxx to xxxxxxx relevant xxxxxxx instantly xx relies xx distributed xxxxxxxxx machine xxxxxxxx and xxxx mining xxxx Big xxxx technologies xx make xxxx possible xxxxxxxx List xxx describe xxxxx levels xx categories xx analytics xxxx are xxxx often xxxxxx as xxxxxxxxxx and xxxxxxxxxxx but xxxx occasionally xxxx as xxxxxxxxxxx Descriptive xxxxxxxxx this xxxxxxx on xxxxxxxxxxx past xxxx to xxxxxxxxxx what xxx happened xx uses xxxxxxxxxx reports xxx data xxxxxxxxxxxxx to xxxxxxx clarity xx historical xxxxxxxxxxx Predictive xxxxxxxxx this xxxx statistical xxxxxx machine xxxxxxxx and xxxxxxxxxx data xx forecast xxxx is xxxxxx to xxxxxx in xxx future xx identifies xxxxxx patterns xxx probabilities xxxxxxxxxxxx Analytics xxxx goes xxxxxx prediction xx recommending xxxxxxxx actions xx strategies xx uses xxxxxxxxxxxx simulations xxx decision xxxxxx to xxxxxxx the xxxx course xx action xxx desired xxxxxxxxMore Articles From Statistics
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