Data Mining(1) (1).ppt

8/18/2019 Data Mining(1) (1).ppt

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UCD Quinn School of BusinessScoil Gnó Ui Chuinn UCD

1

Lecture 2

Business Intelligence

Data MiningBig Data


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UCD Quinn School of Business

Scoil Gnó Ui Chuinn UCDSlide 2

Figure 2.1 Levels of managerial decision taking


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Scoil Gnó Ui Chuinn UCDSlide 3

BUSINESS INTELLIGENCE

•Business intelligence –knowledge about customers,competitors, suppliers, your owninternal operations, etc

• ombined !orms o! in!ormation tocreate real knowledge

• "ncompasses e#eryt$ing t$ata!!ects your business

• %elps you make strategic businessdecisions

McGraw-Hill © 2007 The McGraw-Hill Companies, Inc. All rights reserve.


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Scoil Gnó Ui Chuinn UCDSlide &

BUSINESS INTELLIGENCE

McGraw-Hill © 2007 The McGraw-Hill Companies, Inc. All rights reserve.


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Scoil Gnó Ui Chuinn UCD

Data Mining


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What is Data Mining

• 'naly(ing and summari(ing

• 'naly(e data !rom many di!!erent dimensions orangles, categori(e it, and summari(e t$erelations$ips identi!ied)

• *inding correlations or patterns


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istor! of data mining


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• Data Mining sa#es +esco 3-. Million a

year– “What scares me about this is that you know

more about my customers after three monthsthan I know after 30 years”- Tescos then

Chairman Lord MacLaurin


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The Ur"an M!th


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• 'd#ertising on *acebook


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Multidimensional Analysis

and Data Mining

• Data mining – t$e process o! analy(ing data toe/tract in!ormation not o!!ered by t$e raw dataalone

• +o per!orm data mining users need data0miningtools

– Data-mining tool – uses a #ariety o! tec$niues to !indpatterns and relations$ips in large #olumes o!

in!ormation and in!ers rules t$at predict !uture be$a#iorand guide decision making


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Scoil Gnó Ui Chuinn UCD Slide 12

What Information do B#sinesses Need$

– eed to con#ert accumulated “ Data” intouse!ul “ Information” • +imely, 'ccurate, and onsistent in!ormation and

analysis%

– $o are our best customers4 5 How can we retain them?

5 What are their preferences?

– $o are our potential customers4

5 How can we develop and maintain those relationships?

– $at e#ents lead to customer loss4

– What are our problem areas? 5 Causes (Prevent future problems from occurring)

5 Solutions (Uncover previously unknown solutions)


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C#rrent State of E&Commerce

• +$e eed *or Business Intelligence 6 Data Mining7

– In#estments in network in!rastructure, $ardware, andoperational so!tware applications $a#e pro#ided a '!lood( o!customer data)

– +ransaction0generated, automated data on%

Customers

Partners Suliers

ProductsSer!ices

"n!entory Le!els

Per#ormance $istory


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Scoil Gnó Ui Chuinn UCD Slide 1&

So#rces of C#stomer Data

• %Dro&ning' in Customer Data #rom(

)ame Gender

*!ening Phone

*+Mail Address

Daytime Phone

Address

*mloyer

Mem,ershis$o,,ies

Purchases

-o, itle

"nterests

"ncome Le!el

CustomersCustomers

Web Site

Registration

Customers


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Scoil Gnó Ui Chuinn UCD Slide 1-



)ame Gender

*!ening Phone

*+Mail Address

Daytime Phone

Address

*mloyer

Mem,ershis$o,,iesPurchases

-o, itle

"nterests

"ncome Le!el

CustomersCustomers

Point of Purchase


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)ame Gender

*!ening Phone

*+Mail Address

Daytime Phone

Address

*mloyer

Mem,ershis$o,,iesPurchases

-o, itle

"nterests

"ncome Le!el

CustomersCustomers

Externally Purchased

Marketing Data


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B#siness Intelligence )BI*

• “Understanding The Past & Predicting The Future”

A /alue Solution

+ns,ers To Critical B#siness -#estions +re B#ried In This Data

Need to convert into #sa"le information

With S.eed%

'Data( can "e /#ickl! gathered0 organi1ed0 and formatted

%+nd Effectiveness 'Information( can then "e delivered to the right .eo.le at the right time


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• “Understanding The Past & Predicting The Future”

A /alue Solution

Critical Information Enhances B#siness 2erformance

Information gathered thro#gh BI leads to a "etter #nderstanding

of c#stomer "ehavior0 enhances o.erations0 and o.timi1es

strategic decision&making%

C#stomers )Contact Information0 Interests0 B#!ing a"its0 etc3*

Contact 2references )E&mail0 2hone0 Direct Mail*

Marketing )C#rrent 2lans0 Necessar! +d4#stments0 5#t#re 2lans*

6.erational Iss#es )Staffing0 Service Levels0 S#..lier 2erformance*


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Scoil Gnó Ui Chuinn UCD Slide 1:


Data ollection– Extracting customer data from various sources• +rans!ormation

– Organiing data into an understandable and usable format

• 'pplication o! 'nalytical 6 Data Mining +ools

– Comprehensive explanation of historical data“ !at "appene#$ …!en$ ”


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Scoil Gnó Ui Chuinn UCD Slide 2.


Data ollection– Extracting customer data from various sources• +rans!ormation

– Organiing data into an understandable and usable format

• 'pplication o! 'nalytical 6 Data Mining +ools

– Comprehensive explanation of historical data“ !at "appene#$ …!en$ ”

– !redict future performance and trends based on this historicaldata

Consumer Demand "orecasts# Staffing $eeds# etc(

“ Base# on t!e #ata we’ ve accumulate# from similar customers%

is t!is set of potential customers a cre#it risk$ ”

• Intelligent Strategy "/ecution– Develop# implement# and follo% a strateg& based on &our

predictions


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B#siness Intelligence )Basic 2rocess5lo,*

Demographic

Data

Psychographic

Data

Externally

Purchased

Sales

Data

Enterprise

Data

Collection

Organize

“Load”

Data Mining TestingReporting


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B#siness Intelligence )Com.rehensive5rame,ork*

Demographic

Data

Psychographic

Data

Externally

Purchased

Sales

DataEnterprise

DataExtraction, Transformation, Load

Decision Support Tools (Data Mining)

Business!ritical "nformation !onsolidated

#nalysis !onducted

$eports %enerated, Produced, and Deli&ered


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"n Summary0 A usiness "ntelligence ool

Discovers .revio#sl! #nkno,n .atterns ,ithin vast amo#nts of data

Similar to /#er!ing a data"ase )or data"ases*

Instead of gathering ra, data0 data mining retrieves 'information(

yes o# Data Mining(

!.othesis Testing

Undirected Data Mining

Directed Data Mining

Data Mining


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Scoil Gnó Ui Chuinn UCD Slide 2&

$yothesis esting

Data Mining

7 +ns,ers -#estions

7 E8am.le%

7 -9 Does the age of an ins#rance agent matter ,hen tr!ing

to sign. ne, c#stomers to ne, .olicies$

7 +9 The ass#m.tion might have "een that older0 more e8.erienced

agents ,ill have more s#ccess3 B#t0 the data ma! reveal that the age

difference "et,een the agent and the .olic! holder is most im.ortant3

):o#ng agents have more s#ccess ,ith !o#ng c#stomers ,hile older

agents have s#ccess ,ith older c#stomers3*


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Scoil Gnó Ui Chuinn UCD Slide 2-


Data Mining

7 Letting algorithms find '.atterns( in vast amo#nts of data

7 T!.es%

7 +#tomatic Cl#ster Detection

7 Market Basket +nal!sis

7 Se/#ential 2attern Matching

5inds 'cl#m.s( of c#stomers

,ho are similar "eca#se of

income level0 "#!ing ha"its0

geogra.hic location0 etc3


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Data Mining


7 T!.es%



7 Se/#ential 2attern Matching

Determines ,hat t!.es of

.rod#cts )and services*are .#rchased at the same

.oint in time


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Data Mining


7 T!.es%



7 Se/#ential 2attern Matching Determines events that

occ#r in a .artic#lar

se/#ence in time


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Scoil Gnó Ui Chuinn UCD Slide 2;

Directed Data Mining

Data Mining

7 ;no,n as '2redictive( or '2rofiling( Data Mining

7 +..l!ing data from the .ast to a similar "#siness sit#ation in the f#t#re

Customer Churn *amle(7 + c#stomer that has left in the .ast is similar to one ,ho ,ill leave

in the f#t#re3 So0 gather data on lost c#stomers in order to ho.ef#ll!

decrease the likelihood that c#rrent c#stomers ,ill leave3

Mar3eting *amle(7 C#stomers ,ho res.onded to an advertisement or .#rchased a

.rod#ct in the .ast are similar to those ,ho ,ill "#! in the f#t#re3


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eek &


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eek &


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eek &


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Slide 32

%-nventor& .dvantage'(,etailer ordersare placed / months prior to a shoppingseason0 'he& first contact 1-! customers

to bu& ne% products 2at a discount3 thenad4ust inventor& levels based on theirresponse


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• BI? DataBI? Data

– >roblems@ too muc$data

• eed to establis$relations$ips and

patterns• Big data is t$e

solution not t$eproblem


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E8am.le9 Carr9 The Big S,itch

•

Sunday, 'pril 1., 2.18


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Carr9 + S.ider?s We"

• $o is &&199&:4


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Search Logs

• In Auly 2..9, 'C released t$e keywordsentered into its searc$ engine by 8-9)...subscribers

• +o protect subscribers pri#acy, 'C $ad EanonymisedF t$e data, remo#ingidentities


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Who is @@A@$

• n 'ugust :, 2..9 +$emla 'rnold, a 82year old widow !rom ?eorgia, woke up to!ind $er picture on t$e national edition o!

+$e +imes


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• +$roug$ t$e searc$ we

make, t$e sites we #isit, we

disclose details about our Gobs, $obbies, !amilies,politics, $ealt$)

• ur secrets, !antasies,

obsessions 6 e#en ourcrimes

• ur sense o! anonymity islargely an illusion


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Who is @@A@$

• But t$e detailed recordso! searc$es conducted byMs) 'rnold and 8-9,...ot$er 'mericans, copieso! w$ic$ continue to

circulate online,underscore $ow muc$people unintentionallyre#eal about t$emsel#esw$en t$ey use searc$engines H and $ow riskyit can be !or companieslike 'C, ?oogle anda$oo to compile suc$data)


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4n the "nternet0 no,ody 3no&s you5re a

dog


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• In reality, not only is it known t$at you area dog, but w$at breed you are, your age,w$ere you li#e, and t$e kind o! treat your

pre!er


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• 's computer scientists continue to re!inedata0mining algorit$ms, t$ey areunco#ering new ways to predict $owpeople will react w$en t$ey arepresented wit$ in!ormation

• +$ey are learning not only $ow toidenti!y us but also $ow to manipulate us


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• +$e Internet puts

enormous power intot$e $and o! indi#iduals,but e#en greater powerinto t$e $ands o!

go#ernments, andot$er institutionsw$ose business it is tocontrol indi#iduals

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