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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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UCD Quinn School of Business
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCDSlide &
BUSINESS INTELLIGENCE
McGraw-Hill © 2007 The McGraw-Hill Companies, Inc. All rights reserve.
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
Data Mining
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
istor! of data mining
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
• 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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
The Ur"an M!th
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
• 'd#ertising on *acebook
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
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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UCD Quinn School of Business
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?
– $at e#ents lead to customer loss4
– What are our problem areas? 5 Causes (Prevent future problems from occurring)
5 Solutions (Uncover previously unknown solutions)
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 13
C#rrent State of E&Commerce
• +$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 $istory
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 1&
So#rces of C#stomer Data
• %Dro&ning' in Customer Data #rom(
)ame Gender
*!ening Phone
*+Mail Address
Daytime Phone
Address
*mloyer
Mem,ershis$o,,ies
Purchases
-o, itle
"nterests
"ncome Le!el
CustomersCustomers
Web Site
Registration
Customers
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UCD Quinn School of Business
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$o,,iesPurchases
-o, itle
"nterests
"ncome Le!el
CustomersCustomers
Point of Purchase
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 18
So#rces of C#stomer Data
• %Dro&ning' in Customer Data #rom(
)ame Gender
*!ening Phone
*+Mail Address
Daytime Phone
Address
*mloyer
Mem,ershis$o,,iesPurchases
-o, itle
"nterests
"ncome Le!el
CustomersCustomers
Externally Purchased
Marketing Data
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 19
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
B#siness Intelligence )BI*
• “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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 1:
B#siness Intelligence )BI*
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 2.
B#siness Intelligence )BI*
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 21
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 22
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 23
"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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UCD Quinn School of Business
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 2-
Undirected Data Mining
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 28
Undirected Data Mining
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
Determines ,hat t!.es of
.rod#cts )and services*are .#rchased at the same
.oint in time
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD Slide 29
Undirected Data Mining
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 Determines events that
occ#r in a .artic#lar
se/#ence in time
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UCD Quinn School of Business
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
eek &
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
eek &
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
eek &
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
• 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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
E8am.le9 Carr9 The Big S,itch
•
Sunday, 'pril 1., 2.18
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
Carr9 + S.ider?s We"
• $o is &&199&:4
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
Search Logs
• In Auly 2..9, 'C released t$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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
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!
+$e +imes
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
• +$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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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
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$data)
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
4n the "nternet0 no,ody 3no&s you5re a
dog
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
• In reality, not only is it known t$at you area dog, but w$at breed you are, your age,w$ere you li#e, and t$e kind o! treat your
pre!er
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UCD Quinn School of Business
Scoil Gnó Ui Chuinn UCD
• 's computer scientists continue to re!inedata0mining algorit$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 $ow to manipulate us
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UCD Quinn School of Business
• +$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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