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CHAPTER
Overview of Business
lntelilligence, Analytics,,
and Data Scienice
LEARNING OBJECT IVES
■
Unckrstan<l Lhe m_'{_-<l for <.:ompu[(:r­
iz-c d support of managerial decision
making
■
Rc<.:ogniz-e the evoluLion of sud1
<.:ompuleriz-ed support Lo the currenL
sla Lc-ana lyLk.-/c..la w science
■
Describe I.he !business inLclligcncc
( Bl) methoc..lolo b'Y and conccpL~
■
■
ndeThland the d ifferent types of ana­
lyLics and sec sdcclcc..l a pplka Liom
nderstand the analytic..~ c<.:osystcm
identify variou.~ key players and
career opportunities
10
T
he bu.~incss environment (dima-Lc) is umMan-Lly <.:hanging , a_nd it b bec:oming
more and more complex . Organiza Lion.~ , bmh priva Lc and publk, arc unc..lcr pre.~­
surcs that forc:c Lhem Lo respond qukk ly [o changing umd.icions and lo be inno­
vative in the way I.hey opcr.Hc. Such a<.:livilics require organizations lO be agile an<l lO
make frequent and quick slmtcgic. ta<.:li.<.:al , and operntional <lccisiuns. some of which are
very complex. Making such decisions may require nmsic..lerable amounts of rdevan Ldata ,
information. and knowledge. Processing these, in Lhe fr.i.mework of Lhe necdcd decisions,
mu.~l be done qukkly, frec1uently in real time, and usually requires some compmeri.zed
supporL
This hook b about u..~ing business analytks as <.:ompulerizcd support for managerial
decision making. Il umn:nLr,Ue.~ on I.he lheorclical and <..un<..tpLual founc..laLion.~ of deci­
sion support , as well as on Lhe commercial tools and techniques Lhat arc available. This
book prcst:nL~ the fundamentals of the tcchniques and the manner in vvhich Lhest: sy ·
Lem~ arc umMm<.:te<l and uscd. V:'c follow an EEE approa<.:h lo introducing thcst: [opic..~ :
Expos-ure Experien ce, and Ex:ploration. 111e book prim:trily provides e.xposurt: Lo var­
iou.~ analytic..~ tt:chniqucs and Lhcir applkaLiom . The idC'.t b 1ha La sLuc..lcnL will be inspired
Lo learn from how other organization.~ have employee.I analytics to make dc<.:bions or
Lo gain a <.:ompeli Live t:dgt:. ~ e believe Lhat such exposure lo whal is being done with
analyli<.:s and how it can be achieved b Lhe key componcn[ of le:trning :tboul :,inalyli<.:s.
In describing thc Lechniqucs. we also give example.~ of specific software tools Lhat am be
3
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.
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
uS<..-cl for developing such applicalions. The book is no[ limiLed Lo any one software tool,
so sludcnls can experience theS€ Lechniqucs using any number of available soft,\ arc
tools. V:'c hope tha t Lhis exposure and experience enable and motiv.ate re,1ders to explore
the polcnlial of these techniques in their cmrn domain. To fadlit:ale sm:h exploration, we
indude exerdses Lhat direct lhe re<1der Lo Tcrndala Univcrsi1y Nc1,vurk (TUN) and other
sites lha Linclude team-oriented exercises v.1herc appropria te .
This inttcx..luctory chapter provides an intrcx..luction to analytic.'> a.'> ,veil as an overview
of Lhe book 111e chapter has Lhe following se<.:Lions:
1.1 Opening Vigncnc: SporL'S Analytics-An Exdling Fromier or Learning and
Underslan<ling Applicalions of Analytics
1.2 Changing Business Environmenls and Evolving · ceds for Decision. upport and
AnalyLks 11
1.3 Evolution of Computerized Decision . upporl lo Analytics/ DaLa Sden<.:c 13
1.4 A. Frnmework for Business [ntdligcncc
1.5 AnalyLks Overvie,v
15
22
1.6 AnalyLks Examples in Sdec[e<l Domains
1.7 A Brief Introduction
29
Big Data Analylics
35
1.8 An Overview of the AmtlyLks Ecosystem 37
1.9 Plan of the Book
6
1.10 Resources , Link.'>, and the Terada ta niversity clwork Conne<.:tion
NIM
lD
7
OPENING VIGNETTE: Sports Analytics-An Exciting
Frontier for Learning and Understanding Applications
of Ana ytics
The application of analytic.'> lo business problem.'> is a key skill, one that you ,vill leo1rn in
this book. J\fany of Lhese Lcchniques are now eing applied to improve decision making in
all aspc<.:ts of sports, a very hol arl'.a <.:ailed sports anal ytks. Sporl<; anal ytks b Lhe an and
sdence of ga[hering data abmJL aLhletes and [earns to cred.le insighl'S Lhal improve sporls
decision.'> , such as deciding which players [ o recmil , how much to pay them, who [o
play. how [o train Lhcm, how to keep them healthy. and when 1hcy should be traded or
n.:tired. For teams, it involves business <ledsions su<.:h as tkket pridng, as wdl as rosLcr
decisions, analysb of each compeli tor's strcnglhs and weaknesses. and many game-day
decisions.
Indeed, sporl'S analytks is becoming a special ly within analytks. It is an important area
becau.se sports is a big business , gcnernting aboul 1 5B in revenues t:ach year, plu.'S an
additional HIOB in legal and 300B in illegal gambling, ac<.:ording Lo Price Wa Lerhouse. 1
ln 201 , only 125M vvas spent on analytks (less than 0.1% of revenues). 111is i.'S exp<.-ctc<l
LO grow at a healthy rntc lo
.7B by 2021.z
•··Ch:mging the G::ime: Outlook for the Global . ports i\llanket to 2015," Price W:ilerfmuse Cooper.; Report, :ippe:u:s
:ii https://v,t\vw.pwc.com/gx/en/11ospj1::i 1ity-leisure/pd.f/dun •i %Lthe-g::ime-outlook-for-thc-gloh:i.l-sport.,;-m::i nket­
to-2015.pdf. Betting chu frnm hups://www.c::ipcredil.com/11ow-mud1-amcricms.,;.pend-on-sport.,;..e:ich-ye::1r/ .
' "Sports An:ilytk.,;. l\.f.a.nket \Vmth
.78 by 2021," \Vmtergreen Re.=.i.rch Pre.,;,,; Rele:ise, cm<ered ~' PR 1e,,v51vire
::ii http://v,t\,·w.pm e\•tswire.com/n=,s-rdea.sc. sports-:ina11tics--m:irket-\•torth--i •billion•b •-2021-50986987 I. html ,
June 25. 2015.
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1 • An Overview o f Bu. ·nes. Imelligeo , oalytics, and Data
ience
5
The use of analylics for .sports ~vas popularized by [he l\1one_yball book by Michael
Lewis in 2003 an<l Lhe movie .starring Brad Piu in 2011. TLshowca.'>cd Oaldam.l A's gcncrnl
manager Billy Beane am.I his u.'> e of d ai.a. and analylics lo turn a losing Learn inLo a win­
ner. In panicular, he hired an analys[ who use<l analytics Lo dmft players able lO gel on
base a.'> oppo1.'>cd to players who cxcx:-llcd a[ tra<l.itional measures like runs baned in or
.stolen ha.'>es. 111c.se in.sighL'> allowed Lhem Lo draft pn.>.spccls ovtc:rlookc<l by other teams
al reasonable srnning salaries. It workcc.1- thcy made it lo the playoffs in 2002 and 2003.
ow :malytic..'> are being usc<l in all pans of .sports. The analylics can he divided
belwcen Lhe from office and back office. A good description with 30 examples appears
in Tom Davcnpon' · .survey a nidc.j Fronl-ot K-e business analyLics include analyzing fan
behavior rnnging from predictive mode l. · for scaMm ticket rcncwab and regu lar Lickcl
sales. Lo .scoring tweeL'> by fan.'> regarding Lhe Learn , a Lhlctcs, coachc-s. and owners . 111is
is very similar to tradilional cuS[omer relationship management (CRl\4). Financial analysis
is also a key area, where salary caps (for pms) or scholarship HmiL · (colle •cs) arc pan of
the cquaLion.
Back-offk-e lL'>es include analysis of both inc.livic.lual athletes as well :as learn play. For
individual players. [he re b a focus on recruiLmen[ model· and scoulin • analytics, anaJytics
for strcngLh and ,1lness as well as development. and PMs for avoiding ovcrLraining and
injuries. Concu ·sion n:sc-<1rch b a hol field . Tl.'"'<1m analytics include .slra Lcgies and tactics.
compc liLivc a .·sc:.-smcnls, and optimal roste r chok-es unc.lc:r various on-fide.I or on-cou rt
si Luations.
The following rcpresc:nLativc cx.a mples illuM.:mlc hrn,v Lhrcc sports organi:rn Lion.'> use:
data and analylics lo impmvc .sporl'> opcra lions, in lhc .same \Vay analyLic..'> have improved
lrn.ditional inc.lus Lry dccbkm mak ing.
Example I: The Business Office
Dave \Vard. wo rk.'> as a business analyst for a maior pro ha.'>cball team, focusing on rev­
enue. He analyzes Lickct .sales, hoth from sca.'>on Lickc'L holders as \\tell as .single-Lickc l
buyers. ample quesLion..'> in his area of responsibility include "vhy sca.'>on LickcL holders
rern.."\V (or do not renew) thei.r Lickel.'> , a.'> wc:11 as ,vha[ factors drive la.- [-minultc: indiv idual
.seal ticket purchases. Another question is hrn,v Lo price the ticket'>.
Some of Lhe analytical techniques Dave u.'> es ind uc.lc .simple stalistics on fan behav­
ior like overnll auend.:ancc and am,-..vers to survey quesLion.'> about likdihooc.l Lo purchase
again . However. v.rhal fans say vc:rsus wha Llhey <lo can be di crenL Dave runs a sun1ey
of fam by Lickc:t sc:a L location (~lier~) a nd a.'>b abou[ their likelihood of renc\ving their
.season tickets. BuL when he compares what they .say versus what they do. he d iscovers
big d ifferences. (See Figure 1.1.) He found Lha L69% of fam i.n Tier 1 sc-.i.L'> who said on Lhc
Ti11r
Highly Uke ly
Likely
Maybe
P,rottably Nat
Ctlrtai 'ly Nat
92
88
75
69
45
2
88
81
70
65
38
3
80
76
68
55
36
4
77
72
65
45
25
5
75
70
60
35
25
FtCU,RE I.I
Season Tid<et Renewals---Survey Sen.res.
' lliama.~ Davenport, "Arni rtics in Sports: 11,e New • cience of Winning." lntemation:d lrt.~blute for Anal)1ics
W1hite paper, sponsored b • SAS, February 2014. On the SAS Web site .at: ht1p://,vww.s:is.com/content/tl:1111/. A /
en_us/ doc/ whiti=p:ipeir7 1ia-analytic.,;..in-stpmts-J06993.p<lf. (Acee. sed July 2016)
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6
Chapter l
• An Ove
iev,• of Busines...~ Intelligence, Analyti s, and Data Science
survcy Lim[ lhey wo11.1ld "pmbably not rem..w " actually di<l. 111is is 11.1sdul insighl tha Lk."<1ds
lo ac.1.ion-<.:ustomers in thc green cdls are Ll1c mc>..~l likely lo renew LickeL~, so require
fewer markeling Louches and dollars Lo convcn. for example , compared to cuSLomcr.s in
thc bl 11.1e cells.
Howcver. many fanors influcncc fan ticket p11.1rchasc behavior, especially price,
which <l1ives morc sophistical<..x.l .staLb1.ics and <lata analysis. For hoth arcas, but especially
singk-gamc tickcts, Dave is driving the use of dynamic pricing-moving the b11.1.si.ness
from .simplc sta Lic pricing by sedl location Lier lo day-by-day up-and-do,vn pricing of
individuaJ se<1ts. 111is is a rich rescarch ared for many spons teams and ha.~ hugc 11.1psi<lc
p otcnLial for revenue cnhancemenl. For example. h.is pricing wkes into account the Leam's
rccor<l, ·w ho they arc playing, game <latcs and times, \Vhich star a Lhl.cH:s p lay for <.:ach
LL"am, each fan's history of rencvving se<1son Licker:.~ or buy ing single LickcL~ , as \vell as fac­
tors like seal kx.:alion. numbcr of seals, and rcal-Lim c information like Lrn ic congcstion
hbtorically al gamc lime and evcn the we<1lhcr. See Figure L2.
~· hich of Lhese factors are imporlant? How much? Given his exLensivc :tatistics
background, Dave hui.lds regression mcxJels to pick out kcy facwrs driving lhcse historic
bL"haviors and cn."dlc PM.~ to idL"mify how to spend markcLing rcscmrcc.s [o drive revenues .
He builds churn modds for Sl."dSOll dckc[ holdcr.s lO creatc segments of cus[omcrs who
will rcncw, won't renc\v, or are fcncc-si ners, whi.c h then <lrivc.s more refirn..x.l markcting
campaigns.
In addiLion, hc docs sentiment scoring on fan <.."omments likc tweets that hdp him
segment fam inw different loyalty segmcnK Olher stu<lics a boul singk-gamc auendancc
drivcr.s hdp Lhe marketing department un<lersLand lhe impact of givca,vays like bobblc­
hcads or T-.shir[S, or .suggestions on where Lo make spot 1V ad buy:.
Beyon<l rcvcnuc.s, there arc many oLher analytical areas that DavL"·s Learn works on,
im.:l11.1ding mcrdIBndbing, TV and r.idio bmad<.:ast revcn11.1c.s, inputs [o the gcnernl manager
on salary ncgotiations. drnfl analytics espccially f:.'lVcn salary caps, pmmolion cffcc.tivcness
induding advcnising channeb , and hmnd a~varcness, as wdl a.~ panner analyLics. Hc '.s a
vcry bu.~ guy1
H ome Team PerformanOB in Past 1D Games
Seat
Location
Team
Performance
Oppooent Made Playotf'S Previous Yea r
(
Tma-Fllllatad
Variables
Game Start Time
)
Pa.rt of the Season
)
( Oppommt from Same Division)
Davs before the Game
( Which Pit cher?)
( W hat's His Earned Run Average? )
Individual Player
Reputations
( N uml)er of All Stars on
Opp□flE!nt's Roster )
Fl,G U,RE I .•l Dynamic Pricing Previous Work-Majo.- League Baseball. Source: Adapted from
C. Kemper and C. Breuer, "How Efficient is Dynamic Pricing for Sports E'l/e/1t:s1 Designing a Dynamic
Pricing Model fo.r Bayem Munich", Ind. jOL11Tim of 5po,rts fuiance, 11. pp. ◄ -25 . 20 H,.
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1 • An Overview of Bu. ·nes. Imelligeo
, oalytics, and Data
ience
7·
Total# of Plays: 540
Perc.entage, of Run: 46.48Cila
Percentage of Pass: 53.52Cb
If Off_P.ers. is
1 □. 1 1 . 21!l,
t
,
Total # of Plays: 155
Percent.age of Run: 79.35Gb
Percentage of Pass: 20. 65%
Total # of Plays: 385
Per centage, of Run: 33.25Gb
Percentage of Pass: 68. 75Cb
12. 21 . 30. 3 1. 32
22,
or Mi!i~ng
1,1 it .....
1st
c,-
2nd Dc:mn -
:rd er 41it Dtwn
Total If at Plays: 91
Percentage of Run: 15.38Clb
Pe.rcerrt:age of Pass: 84.62<lb
Total # of Plays: 294
Percentage of Run: 38. 78Gb
Percentage of Pass: 61 .22%
WeZll'll!.,.,dir,g
ori1tisaltie
Total # of Plays: 1 62
Percentage of Run: 50.62%
Percentage of Pa,ss: 49.3811b
FilGURIE 1. 3
I ~ distzmcz Ito a::hi~
the ne,:t dcr,-.n is
I•--w• ..-e bohin::t
Total # of Plays: 132
Percentage, of Ru.n: 24.24%
Percentage of Paass: 75. 6 7Qb
I.,.,.~
♦·
than 5 yords
Total # of Plays : 25
Percentage of Run: 44.00'b
Percentage, of Pas!;.: 56.0Dll:i
M-tmn51"'"ti,,
Total# of Plays: 66
Percen:tage of Ru.n: 4.55%
Peroe.ntaQei of Pass: 95.45%
Cascaded Decision Trne for Run or Pass Pl.rys.
Example l:The ·C oach
Bob Bn:cdlove is lhe foolball coach for a major c.-ullege team . For him, i1's all abouL wfo­
ning games. Hb areas of focus indu<le n:cruiLing lhe lx:st high schtx.>I playcr.s , <levdoping
them to fi L his offense and <ldcn.,;c .sysLems, an<l gelling maximum effort from them on
game <lay.s . ample 4ucs.tions in his area of responsibility include: \Vho do we recruiL?
\VhaL <lrilb hdp develop lhdr skills? How hard <lo [ pu.,;h o ur athleLes? When: are oppo•
nenls strong or weak, and hov.r do \Ve figure out Lheir play L<..nut:nde.s?
Fortunaldy. his team has hin.-<l a nt:\V i:eam opemtion.-; expert, D-.i.r BLT,mc-k, who spe­
cializes in helping lhc coaches make rac.t.ical derisions. She is working wilh a Leam of Slll.lULTil
interns who an: creating o pponent analytics. TI1ey used lhe coach's annorate<l game 1lm to
build a ca..,;ca<lt-<l decbion Ln:c- mo(kl (Figure J.3) Lo pn.-<lict whether the m::.x:L play ,viii bt: a
running play or pas: fog play. For I.he defensivt: coordinator, I.hey havt: built heat maps (Figun:
1. ) of each opponent's p a,ssing offense, illuslmLing their tendencies to th:ruw ldLor rlghLand
into which dcfensi vc c.-uvcmgc zones. Finally. they bu ill some Lime seric.,; analytics ( Figure 1. -)
on t::.x:plosive p lays (defim.-<l a..,; a gain of mon:- than 16 yard,; for a passing play or more than
'12 yam-; for a run p lay). For (.".i.Ch play, they compan: the outcome with their own ddcn,;ive
formations and Lhe other team's ofknl"ive formations. 'Vlrhich helps Co-ach Breedlove rl.".KL
mon:- quickly Lo formation .-hifL,; <luring a game. \-Xfe will explain lhc analytic.-al lcchniques that
b>enemte<l 1.hcSl.'. fig11.1n.'S in much mon:- depth tn Chapters 2-5 and Cha.pLer 7.
cw work tha L Darb fostering involves building ocller high school athleLe n:crui L­
ing mo<lek For t:xample. each y(.",U Lhe learn g ive.s scholar.ship.~ to Lhree s LuuenLs who an:
...vide receiver recruil,;. For Dar, picking ou L the bc..,;l playcr.s goes lx:yon<l simple me,1.sures
likt: hcrnr fast a[hle1..c.-s run. how high I.hey jump, or how long their arms are to ne\ver cri­
lelia like how quickly they can rol.ale Lhcir heads Lo cald1 a pass, what kinds of reaclion
Limes Lhey exhibit Lo mulLiplc stimuli , and ho\v accurately they nm pass route.s . omc of
her ideas illu sLmting LheSl.'. concepts appear on the T
'il ch siLe; look for I.he B. [ Case
of Precision Football. 1
'Bu.'>inc.,;s Scenario Im ·estigation B, I: 1l1e C:ise of PrecL~ion Footba:11 \'icleo). Fall W1 5). Ap~rs on http://
,vww.ter:idat:iun iversitynetworll::.cmn/About- L1. Wh::1ts-New/ BSl~'>port.~-An:ilrtic.,;.--J>reci!>ion-Footb:ill// _fa ll
2015. (Accc.-.scd Soeptemhc.- 2016)
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8,
Cha pter l
• An Ove iev,• of Busines...~ Intelligence, Ana lyti s, a nd Data Science
Offense
•
A
Complete: 35
Total: 48
C □mpletie:
C
Complete: 22
Tot-al: 27
B
Total: a
7.5 .Clrnb
Explosive: 5
78.D81'.!b
ExpltJsive: 4
a1 .46%
ExplosiYe: 2
Line o f Scrimmage
1
2
3
Complete: 25
Total: 35
71 .4~
C □mplelle: 12
ComplelE: 14
Total: 28
Elqll□ sive:
1
Total: 24
5[J(lti
Explosive: Cl
Si)(lb
Explosive: 0
,s
7
C □ mplet18:7
Co111J]lete: 13
otal: 2 1
Tat;al: 1Cl
7[J(lti
Explosive: 2
B
Complete: 7
Total: 1 0
7rnb
Explosive: B
Bl. 91'.!b
ExploS111e: 9
X
Complete: 1
Total: 1 3
4
CompletlB: a
Total: 14
57. 14Cll:i
Explosive: 0
5B.B1Clti
ExploSM!: 1
9
CompletlB: 1 5
Total: 27
55.55!li
E:xplosiYB: B
z
y
C □m pletie:
5
Co111J]lete: 25
Total: 44
7
Complete: 5
Total: 15
7.89!li
38.SB!li
33.33CM:i
Explbsive: 1
ExplosiYB: 7
ExplosiYE!: 8
Total: 1 B
De.fense
IF IGURE 1.4
f-teat Map ZOile Analysis for Passing Plays.
y
u::l_d_ 2 t ~ 2 '
...:l_d_=itl~:J
u::l__d_:ft.s-illl'
IFI GURIE I .S
Time Series Analy!1is of Exploo.ive fl'bys..
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1 • An Overview o f Bu. ·nes. Imelligeo
FIGURE I .6
, oalytics, and Data
ience
9
Soccer Injury Models.. 5
Example 3: The Trainer
Dr. Dan John.,;on is che Lrairn.:r fur a \Vomen's college soccer tt:am. His fob b lo hdp die
players sUJ.y healLhy and Lo a<lvis;:.: l11t: coaches on how much load lO put on playt:rs during
prn.ctices. Ht: a lso has an intt:rest in player wdl~hdng, including how much Lhey slt:t:p and
how m uch rt:st they gel bet\vcen h<..'"'avy an<l light pranice S<:ssions. The grnd is to en ·urc
r.hat 1.ht: players arc re ady to play on g'"dme <lays al maxim um efficiency.
Fonuna1dy, hccausc of wearables, there is much more data for Dr. Dan lo analy:z.c.
His player.· train u.,;ing vt:sLS thaL contain scn.,;ors Lhat can mca.,;ure internal loads like
heanbeats, body tempera Lure, an<l rc:p ira Lion rJ.tcs. The vests also indu<lt: accd.cromeLers
r.hat mca.,;ure e x[<..-rnal loads like rnnning distances and speed,; as \veil as accderalions
and <lccdcration.,;_ He knmvs which players arc , •iving maximal dfon during praniccs and
Lhos;:.: who arcn 't.
His focus al the momcnL is rcs<..'"'drch thaL predicts or pr<..-vcnL'> player in juries
(Figurt: J.6). S.ume simple ta.,;ks like a ingk Leg Squat Hold Test-standing on one foo l,
r.he n [he 0Lher-will1 score <l.iffcren Lia ls of more r.h:m 10% can provide useful insigh ts on
body core S[rcngths and wcaknessc.,; (Figure 1. 7). If an a chkte is hit hard <luring a match ,
a trainer can conduct a sideline lt:st, reacting Lo a .-cimulus on a mobile ue\•kc, which ad<ls
Lo traditional concussion protocols. kcp st:mors show who is gerung a<l.equat.c res[ (or
v.1ho parcie<l all nighL). He ha.,; Lhe J\.·1Rl lab o n campus <lo periodic brain sca ns to show
which athletes arc a l risk for brnin injury.
"'i ' omen's Soca:,,- Jnjm ie.~,- National Center for Catastrophic Sports Injury Res°"irch Report, CAA. NCAA port
lnjur,· fac1 :sheets are p roduced hr 1he 1)31:1.l)'s Cenler for . ports Jnju<)' Rese-arch and J>re,eenbon in col l::i'borabon
,vith th~ N:itional Collegia te Athleti.c A.,;,;ocialion, and STOP S_pons; Injuries. Appe-.1.rs :it http.~://ww"-·.nc:1:1.mg/
sites/default/file. . 'CAA_ W_Soccer_l njunes_ WEB.pdf. (Accessed November 2.016).
1
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Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
y
z
X
y
FI.GU RE I •7 Single Leg Squat Hold Test­
Care Body Strength Test
(SauKe: F"'&'-'re adapted from Gary Wilkerson
and Ashish Gupta).
QUESTIONS ABOUT THESE EXAMPLES
1 . \Vhal art: Lhn:t: fac10rs thal migh t
oc pan of a Pf,,.•1 for season Licke Lrene,vals?
2 . \'(7!-ial an: [wo lt:chniqut:s chat football teams can use lO c.lu opponent analysis?
3,. How can wearables improvt: playt:r hcah.h and safety? \Vha[ kimb of ncw analyLic.o;
can lrotincrs use?
4 . \'(i'hat oiber :mal ytics u.o;es can you envision in sport.o;?
What ·C an We Learn from These Vignettes?
Bcyonc.l the fronl-of ,1ce business analyse.-;, Lhc CtYotchcs, trnincrs, and performance expcrLo;,
1.hcre art: many oLher pcople in spons who use data. rnnging from golf groundskeepers
who mt.'asurc soil an<l turf umc.liLions for PGA l<mmamcnt.o; , lo 6'.iscba.11 anc.l b-otskcthall
rdcr<..'€s who arc r. lt:d on Lhc corrcc:'.l anc.l incorrec[ calls they makc. [n fact, il's har<l Lo
find an area of sport.o; lhat is n(J/ being impa.e1ec.l by [he availahiliLy of more c.lala, cspcdally
from sensors.
Skill.o; you will learn in this bcx>k for business analytics will apply lo sporL-;. If you
wanl to c.lig c.let:pcr into this art:a, we encourage you Lo look al Lhe . pons Analytics scc.1.ion
of the Temdata nivcrsiLy Nctwurk (TUN) a free resmffct: for Sluc.lents and acuhy. On Lhis
\'Veb silt:, you will find c.lcscriptions of what to read to find out. more about sport.,; analyt­
ics. compilations of places whcre you can find public-ally available data sets for analysis,
as wdl a.o; <.'.xamplcs of Slue.lent projel'.Lo; in spurLo; anal ytic.o; and interviews of sport.-; profcs­
siona ls who u.o;e <law and analyLics to do Lheir jobs. Good luck learning analytics!
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1 • An Overview o f Bu. ·nes. Imelligeo
, oalytics, and Data
ience
11
S01m::e and Omits: Contnbutoo by Dr. llive Schr:ider. \Vho retired afler 2-l years in adv-:mccd de\'dopment
and marketing a t Teradata. He ha..~ remained on the Boord of Ad,•L,;ors of the Ter:iclat:t niver:sily Network.
v.•here he- . p ends his retirement helping student s and facu lty !cam more about spCllrt.,; a.n:il}'tics. lne football
,i~als ( Figures 1.3-1.5) we-re constructed by Peter li:mg and _lacoh Pc-arson, g:r:iclu:ile studenL,; at OkJ:ahoma
tale Unin:rsity. :as part o f a student project in the spring o f 20 16. llie !mining ,•isua]s (Figures 1.6 and 1. ) :in=
ad:iptcd from tl-tc- im:iges prn\·iclcd b 7• Pmf. Gary Wil kerson o f the- ·ni\'er.~ly o f Te nne.~see at Clh:attanoog:i :ind
Prof. Ashi.1-t Gupi::i o f Auhum University.
■a
Changing Business Environments and Evolving
Needs for Decision Support and Analytics
The opening v igrn."Llc illusLr,ucs how an cnLirc im..lus1.ry can employ a nalytic· to develop
reporL~ on \vha Lb happening , prc<lin \Vhat is likely to happen , and t.hcn also make deci­
sions Lo make Lhe hc-sLuse of Lhe silu aLion al hand. These stc-ps require- an organization w
collc-c.1. and analyze vast slcm.:-s of d ata. mm Lr.id itional uses in payroll and hookkeeping
func:Lions, computerized sysLcms have now penetrated complex managerial areas ranging
from Lhe design anc..l management of automated factories Lo the application of analyLical
mc-Lhods for chc c-valualion of proposed mc-rgcrs a nc..l acquisilions. early all c-xcn.llives
knmv lhaL informar.ion [cchnology is vital Lo 1.hdr hu.~incss and cxlc-n.~ivdy use inform.a­
Lion technologies .
Computer applications have movt:d fro m tnmsaCLion processing and moniLoring
activi Lies lo problem analysb and solution applications, and much of Lhe activity is done
\Vith doud-based tc-chnologks, in many cases accessed through nmbile de-vices. AnaJytics
and Bl toob such a.~ dat:a \Yardmusing, data min ing , online analytical processing (OLAP ).
dashboards, and Lhc use of Lhe cloud-ha.~ed systems for decision support arc the cor•
ntc:rslonc-s of Louay's modc-rn managc-menl. :'iitanagers must ha:ve high-speed, rn.."l':\vorkc-d
informaLion sysLcms ( ,;\,irclinc or "virelc.-s) to asdsL Lhem .,,vith Lhcir mosL importanL Lask:
making dccbions. In many ca.~es, such dccbicms arc m u Linely bdng automa Lc<l , dim.iruil­
ing Lhe need for any manage-rial inlervc-nLion.
&sides the obvious growLh in hare.I, ·a rc, software-, and nc-t\vork capacities, some­
developments have dearly conrribut.ed to fod.litaling gmwLh of decision support and ana­
lytics in a numocr of ways, indudlng Lhe following:
• Group communication and col aboration. Many decisions arc made today by
groups whose members may be in difkrcnl loca Lion.~. Groups can collaboralc and
communicate rc-,1dily by using collabor.icion lOols as well a.~ t.hc u biquiLous sman­
phones. Collaboralion is c-specially imponanL along Lhe supply chain, where pan­
ncrs-all the way from vendors lo c usLo mcrs-must share in formation . As..~embling a
group of dccL-;ion makers, especially experL-;, in one place can be <.:mtly. [nformaLion
sysLcms can improve the collaboraLion process of a group and enable iL~ members lo
oc a l difkrcnl locations (saving t:ravel co..~u-). More- crilically, s uch supply chain col­
lahoration perm its manufanurcrs to know abouL [he changing pallems of demand
in near real Lime and Lhu · rcaCL lo marketplace change.· faste r.
• Improved data management. Many decision.~ involve complex compulaLion.~.
Data for Lhcsc can be stored in diffcren Ldatabases anywhere in the organ ization
and even po.-sihly oul.~ ide lhe organizaLion . The daLa may include Lext, sound,
grnphks , and vidc-o, a nc..l Lhcsc- can be in different languages. J\fany Limes it is
nc-ccssary lO Lransm it da La quickly from d istant locations . . yslc-ms today ca n
search. slorc, and Lransmil needed daLa q uickly, e conomically, securely, and
tra nspa ren Lly.
• Managjng giiarrt data warehouses and Big Data. Large data ware-house.~ (D\"X' s) ,
like the ones operated hy '\"alman. conLain hu mongous amounr.~ of data .. pedal
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12
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
•
•
•
•
m<.:Lho<ls. including parnlld computing. Hae.loop/ park. and .so on. are available lo
organize, se-<1.rch , and mine the data. Tht: l."OSls rdaLe<l Lo darn. Slornge and mlning
art: dt:dining mpid.ly. Tt:chnologks thal fall under Lhe broad ca.l<.:gory of Big Data
have <.:nable<l massive da.i:a coming from a variety of sources and in many <lifkren l
fom1.s, which allm,vs a very different vk-..v in Lo orga.niz-alional performance Lhat -..v<1.s
nol possible in Lhe pasL
Analytical support. ~· ilh mor<.: <la.ta and ana.lysb technologies, more a.l Lema Liv<.:s
can bt: evaluated. foocca.sL.s can be improvt:d, risk amdy.sb c:m be pt:rformt:d quickly,
and Lhe vk\v.s of <.:xpens (.some of whom mlily be in remme loc:Hions) can be l.ul­
lt:cled q uickly and at a reduct:d cosL ExJX.'.rLise c:m even be dt:rivt:<l din:ct.ly from
analytical .sy: lems. \1;' ilh such L<.x)ls, decision makers can JX.'.rfom1 compkx simula­
Lion.~, check many pos.~ ible scenario.~ , and a.~S<.:.~s div<.:r.se imp-.lc.1.s quickly and <.:co­
nomically. This, of course, is Lhe focu..~ of sevt:ral chaplers in the book.
Ovel'coming cognitive Hmits in processing and storing information. Acl.urding
Lo Simon (1977), the human mind ha.~ only a li mi Le<l ability lo process and .slOre
informa Lion. People .somelimes find il difficul L to occa.11 and us<.: information in an
l.'1Tor-free fashion d u<.: Lo thdr cogniLive limi s. The Lerm C(Jgnilif.ii! limits ind.ica.t<.:s
thlilt an ind.ividual'.s pmhlem-soh•ing capability is limited when a wide range of
diverse informal.ion and knowledge is r<.:quired. Computerized .sysLems t:nabk p eo­
ple to ovi:rcom<.: thdr cognitive limiL~ by quickly accessing and processing vasL
amounL~ of SLored infom1.alion .
Knowledge management. Organizations hav<.: gathered va.~l stores of information
abm.1[ their own opern Lions. cu.sLomers. internal procedures, <.:mployee internc.1.ion.s,
and so forth, Lhrough the un.~lruclure<l and SLrnctured l."ommunica Lions Laking plan:
among the various sLakeholders. Knowledgt: managemem syst<.:ms have become
.sourc<.:s of formal and informal support for decision making to managers, although
.sometimes lhi:y m ay nol i:ven be called KJWS. Technologies such as lex L an:aJyLics
and IBI\·1 ~ aL~on are making it possible Lo gener.He \'alue from such lmmvledb>e
stores.
Anywhet1e, anytime fflpport.
sing wirde.-s le<:hnology, managers can access
informa Lion anytime and fmm anypl:ac<.:, analyze and inlerpr<.:l it, and communica Le
" rich Lhos<.: involvt:d. 111is pe rhaps is Lht: biggesLchange lhaL h:as occurred in the lasL
few years. The speed al -..vhkh information ne<..-tls lO be processt:d and convened
into decis.ions ha.~ lrllly changed expec.1.alions for both consum<.:r.s and businesses.
Thes<.: and other capahililie.s h:ave been driving the use uf n>mpu Lerized decisicm
.su ppon since lhe la Le 1960s. b ut espt:cially .s ince the mid-1990s. The growth of
mobile [cchnuk>gies, social media pla tforms, and analytiad Lools has enabled a
d.iffer<.:nL level of information syst<.:llls (I.) support for managers. This growth in
providing data-driven supporL for any decision ex lends Lo nol ju..-;l th<.: mlilnagers hu L
a lso lo consumers . \Ve will first study an overview of technologies Lhal have been
bmadly rderocd to lilS BT. From there we w ill broadt:n our horizon.~ to incro<luce
v.irious types of analy Lics.
SECTION 1.2 REVIEW QUESTIONS
l . \Vhat are some of the key sysLem-orienled trends lha L 11.ave fosteocd IS-.su ppone<l
decision making Lo a new levd?
2 . Lbt .some capabilities of informa Llon syslem.~ Lhat can fadlitale managerial decb ion
making.
3 . How can a compu ter h dp overcome the cogn.iLiv<.: limiLS of humans?
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1 • An Overview of Bu. ·nes. Imelligeo
MIN
, oalytics, and Data
ience
13
Evo ution o,f Computerized Decision Support
to Analytics/Data Science
The limeline in Figure 1.8 shmv.s 1..he tt:rminology u.~ed to de.scril:x: analytics since Lhe
1970s. During 1.he 1970s, Ll1e primary focus of information systems support for decision
making focused on providing .slnKlure<l, periodic reports Lh:at a manager could use for
decision making (or ignore them). Bu.~ine.sse.s began to c:rra Le routine rrporL~ Lo inform
decision maker.s (managers) about \',-'hat had happene<l in the previous perio<l (e.g., day,
\veek , momh, quarter). Although it was u.sdul Lo know whal ha<l happened in Lhe pasL,
managers m:e<led more Lhan this: They needed a variel y of repons al different levels
of granularity lD heller understand an<l address changing need.~ and challenges of 1.he
business. The.se wcrr usually called managemenL informalion sy.stems (Ml ). In Lhc early
1970s, con-P.foncm 1rsLarticula1.ed the major com_-cpLS of D . He defined D s as "inter­
anive compu Ler-bas.<..-<l .sys[ems. ,vhich hdp c.ledsion makers utilize dala and models to
.solve unsLrucLl!.lrrd problem..~~ (Corry anc.l Scoll-Monon, 197 0. The follu,.ving is another
das.sic D. S de miLion. provic.led by Keen and Sctm•:\fonon 1978):
Decision supporr sy t m couple the intell rual resources of iodi\•iduals v.~th the capabilities
of the computer to improve the quality of de isions. [tis a romputer-hased support system
for rnanagem nt deci. ion mak rs who deal with semistructured problems.
ole LhaL Lhe lerrn decision supfJOrl ~:•,stem, like nwnagement infonnati(m system
and .several oLher terms in lh.e 1eld of IT, is a conlenL-frre cxpre.ssion (i.e .. iL means dif­
ferent things lo c.liffcrenl people). 111ercforr, Lhcrr is no l!.lniversally acl.tpLl.-<l ddini[ion
ofD S.
During the early days of analytics. data was often obtained from Lhe domain expe11s
ILISing manl!.lal proce ·se.- (Le .. imerviews anc.l surveys) to build mathemaLical or knowk-<lgc­
b-.i.sl.-<l mlx..lds lo solve consLtained optimization problem..~. The idea wa.~ Lo do Lhe best
,;viLh limited re.sources.. l!.lch decision .suppon models were typically called operallons
research (OR). TI1e problems Lhal \Vere [oo complex lO solve optimally (using Hnedr or
nonl.incar malhematka.l progrnmming Lechnique.s) were tackled. u.~ing heur.isLic melh<.x..ls
.such as .simulation mo<lek (\Ve ,;viii inlrodm.x: lhese as prescriptive analytics laLer in this
chapter and in a bit more detail i.n Chapter 6.)
In t:he late 1970s anc.l e,1rly 1980..~. in adc.liLion m the mature OR mcx..lels that were
being us.<..-<l in many induMri.es an<l govemmenl .sysLcm,s, a new and exciLing line of mod­
els had emerged: rulc-b-.i.s.<..-c.l expert .sysLems. These sy.stems promisc-<l lo capLure experts'
know·ledge in a formal Ll1aL computers coulc.l pr<.)Lt.ss (via a collection of if-then--eL~c rules
or h.eurisLics) so Lhal the.se could be used for consultation much lhe same way Lh:al one
-
-
....,__ 197 Ds - -- - ,saos - - - - 199Ds - - -1
Deci-sian Suppllf't Systems
Fl GUIRE 1.8
E:nterp.rise/E.Jcecutive IS
2000s - -- -
Business Intelligence
,2 0,1os, -
-►
Bi; D ata ...
Evolution of Deoision Support. Bus.lness Intelligence, and Analytics.
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1-4
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
would use domain experts [o i<lent.ify a slrn<.:tu.red problem and Lo presuibc lhe mo ·L
probahle soluLion. ESs allov,•ecl SQl.rcx: expc.mbe Lo be macle available where and when
neede<l., using an "inudligenL~ OS .
The 1980s saw a significanl d1.ange in Lhe \V.ty organizaLions rnpmre<l busine.-s­
related data. The old practice had been Lo have mullipk dbjoimed informal.ion sys[ems
lailored Lo c..,l.plure Lmm,acLional claLa of <lifferenl organization.al uniLs or funcLiom (e.g.,
accounLing, marketing and sales, ,man<.:e, manufacturing). [n L.he 1980s, Lhese sysLems
"\/Vere in[egrnted as enlerprise-k:vd information sysLems thaL we now commonly call enLer­
prise resource pl.anning (ERP) system.~. 111c ol<l mosLly sequenlial and nons.tand.ardiz-e<l
claLa rc:pn:sentation schemas were replacecl by relaLional daiabase 1nanagernenl (RDB/1.,1)
systems. These systems made il tx>s.-ible Lo imprcwe Lhe capture: an<l sLOrage of data, as
well as che rc:lationships between organiZalional data field~ \Vhile signi.ficant.ly reducing
the replication of information. The need for RDBM and ERP systems emerbred when clata
integrity and consistenqr became an i ·sue. signi. ,candy hindering Lhe effe<.:tiveness of
bu.~iness practices. \ViLl1 ERP, all Lhe dala from every comer of Lhe enLerprisc is collected
an<l inlegrah.:<l inLo a consisLent s,c:hema so LhaL every pan of Lhe organizaLion has access
lo lhe single \'ersion of lht: Lrulh when and wht:re needed. In addition Lo the emergence
of ERP system.~, or perhaps lxx:ause of i:hesc system.~. business reporting became an un­
clemand. as-needed busine.-s practice. Dedsion makers <.:ou.lcl dedde 1,vhen they needed
Lo or wanwc..l Lo cn_-,1le :ped:alizL-<l. reporL~ LO invesLigale organi.zactonaJ problems and
opponuniLies.
In Lhe 1990s. the need for more versatile reporting led to lhe c..le\'elopmenl of execu­
tive informaLion systems (EI s; D s designL-<l and developed spedfically for exc<.:ulives
an<l Lheir deds.icm-making needs). These sysLems \\'ere designed as gr.tphical dashboards
an<l s,c:cmx.:ards so LhaL Lhq• could serve as visually appt.-aling displays w·hile focusing on
the mo.t important fa.cw · for ded.~ion makers to keep lrnck o the key performance in<l:i­
calors. To make Lhis highly versaLile reporting po.-sible while keeping Lhe lr,m.~a<.:tional
inLegrity of Lht: busint:ss informal.ion sysCL'.ms inllic:l, iL was necessary Lo create a middle
daL.a Lier known as a D\V a.~ a repository lO specifically suppon business reporting an<l
dedsi.on making. In a very shon time , mosL large Lo medium-siZL-<l businesses adopted
daL.a warehousing as Lheir platform for enL<:rp1i.~e-""ide decision making. The dashboards
an<l score<..:ards goc their dala from a D\V, and by doing so, they \Vere noL hindering the
ef 1ciency of the bu.~iness trnnsaelion system.~ mostly referred lo as (ERP) syslem.~.
[n the 2000s, Lhe D\l:'-driven OS. s began Lo he called BJ syslem.~. As lhe amounL of
longitudinal daL.a accumulaLL-<l in the D\l:'s increasc-<l , so did Lhe capahiliLies of han.lw-.i.re
an<l soft,w·are Lo keep up l,\,'llh Lhe rapidly changing and evolving need~ of Lhe de<..:bion
makers. Because of Lhe globalized compeLitive marketplact:, decision makers needed cm­
rem informaLion in a very di •esLible forma[ lo address business problem.~ and to Lake
adv-.tntage of markel opix>rtuniLies in a timely manner. Be<.:au.~e the daL.a in a D\V is
updated periodically, iL does noL rdlecL Lhe l:alesL informaLion. To devaLe Lhis infonnalion
lalency problem , D\V vendors <levdoped a syscem to updale the daL:a more frequenLly,
which led to Lhe lenns real-lime data u_;arehvusing and, more realisctcally, right-lime data
warehuusing, which differs from lhe fom1er by adopLing a data-refreshing polky b-.tse<l on
the ne<..-<led freshnt:ss of the <lam items (i.e., noL all daL.a items need m be refreshed in real
time). D\Vs are veqr large and feature rich , and il became nect:s..~ary lO ~mine~ the corpo­
rnLe data Lo ~c..1L~nwer~ new and u.~eful knowledge nuggeL<; Lo improve business pro<.:esses
an<l prnc:tkes, hence Lhe terms d.ata mining and text mining. '\l 1th Lhe increasing volumes
an<l vari<..1.ks of data, tl1e net:ds for more storage and more processing power emerg<..-<l.
AILhough large corporal.ions had the means lo tackle this problem. small Lo m<..-<l.ium­
sized companies n<..'€ded more financially manageable business models. Thb m_..._-<l k-<l Lo
servke-orienLed archileCLure and software an<l infrasLruCLure-as-a-service analyLks bu:i­
ne:ss mo<leb. maller companies, therefore, gained acces · [o analytics capabilities on an
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1 • An Overview of Bu. ·nes. Imelligeo
, oalytics, and Data
ience
15
as-m:edcd hasb and pa id only for whaL lhey used , a.· opposed lo invesLing in financially
prohibitive hardware and soft wan: n:sources.
In the 2.010s, we an: seeing yet anothc-r parndigm shift in the vvay Lhat d:ala is
<:apL1.m:d and l!.lsc.J. Largely hecal!.lse of Lhe widcspn:ad use- of ilie Jntemc-l, new daw. gcn­
crnlion med11..1ms have emerged. Of all Lhe new claLa sources (e.g ., racl.io-freq1L1em.-y idcn­
Lifkation [RFJD] Lagi;, digital ener.1;,ry mt.'Lcrs, click.,;lr<.:am Web logs, smart home clevkes,
we-arable hc-allh monitoring equipmcnl) , perhaps Lhe most interesting and challenging is
social networking/social media. This un.truc.tured data is rich in informaLion conlenL, but
analysis of such dara sources poses significant challenges Lo computaLional syslem.,;, ,rom
both software and hardwa.re perspe<:lives. Recently. the Lerm Big Data ha.,; been coined lO
highlight 1..he challenges Lhal iliese new darn. streams have brought on us. J\fany advance­
mc-nrs in olh hard,vare (e.g., massively parallel processing wiLh very large compulalion:al
mc-mory and highly parallel mulLiproces.,;or computing systems) and soflwan:/ algorilhrns
(e.g ., Hadoop ,;vilh MapReduce and o. Ql) ha:ve been dcvclop<c-<l lo addn: ·s Lhe chal­
lenges of Big DaLa.
Il's hard lO preclkl what 1.he next decade will bring and \Vhal the new analytic.,;­
relalecl lcrm.,; \•,rill be. The lime be1.ween nc-w paradigm shift-. in in ormalion systems and
pankularly in analyLics has been shrinking, and Lh.is Ln:nd will <..-untinuc for the fon:M.-c­
able future. Even tholllgh analytic.,; b not new. Lhe explo-;ion
..
in ilS rx>pl!.llar.ity is very
new. Thank.,; Lo the r<..x.:enL explosion in Big D,Ha. ".'.tays Lo collecl and store lhb data , and
intuilive soft\varc- lOols, darn-driven insigh1.s are more accc-.-sible Lo busirn:ss professionals
Lhan e\•er before. Thc-rdore, i.n the midst of global compeLil.ion, Lherc- is a huge opponu­
nity to make beuer managerial decision.,; by u.,;ing data and analytics Lo increase revenue
while decn:a.,;ing co..,;lS by building heller procluc.1.s, improving u1Slomer experience , and
<:aLching fmud be-fore il happen.,;, improving cl!.lstomer engage-men[ through targeLing and
u.1.,;tomization all wiLh the rx>wc-r of analyLic.,; anc.l dala. More and more <.:ompanics are
now preparing Lheir employees V1ri1.h che know-how of bu.-ine.,;s analyLics lo drive dfec­
Liveness and dfidcncy in Lhdr clay-Lo- day decision-making processes.
111c nexL sec.t.ion focuses on a fr.imework for BI. Al1ho1L1gh most people would agn:e
Lhat BT ha.s evolved into analylics and data science, many vendors and researchers slill 1L1se
Lhal Lerm. o ec:Lion 1. p-.i.ys homage LO lhaL hi.-Lory by spcdfically ocusing on whaL has
been called BI. Follo"ving che nexl sec:lion. we introduce analytics and will use lhar a.~ the
label for das.,;ifying all n:latecl <..·on<.."CpL,; .
SECTION 1.3 REVIEW QUESTIONS
1 . Lbl Lhn:e of Lhe terms thal ha:ve been predecessors of analyLks.
2 . Wha l was Lhe primary difference between Lhe systems called MIS, D. S, and ExcrnLive
upporL . yslc-ms?
3 . Did D.
MIi
evolve into BI or vice versa?
A Framewor for Business Intelligence
The decision suprx>rt conccpL.,; presented in. ~lions 1.2 and 1.3 have lx:·c n implemenled
incn:mencally, l!.lnder diffen:nL names , by many vendors thal have ue.i.led tools and mcLh­
oclologies for decision sl!.lpport. As noLecl in • ec:Iion 1.3, as Lhe enterprise-, ·ide systems
grew, managers were able lo a<.:cess l!.lser•friendly n:porL,; Lhal enabled Lhem lo make deci­
sions 4uickly. These systems, which were generally called El s, Lhen began lo off'cr acldi­
Lional visuaHzaLion, alerts, and pcrfom1ance mc-a.,;uremenL capabilities. By 2006, Lhe major
C(Jmmercial prodl!.lcL,; and sc-rvices appc-<1n:d under lhe lerm business intelligence (BO.
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16
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
D·efi niti on s of BI
Business intelligence (Bl) is an umbrdla lt:rm that combines archiu..x:turc-s, Loob , daL:,t­
ha.se.·. analylical tools, applications. and mt."Lhoc..lologics. h l. ·. like D. S, a umLc-nt-frc-e
exprt: ·sion, so it means different things to different people. Part of Lhe <.:onfu.sion abcm L
BI lies in the flurry of auonyms anc..l buznvordo; Lhat arc assodalt:d wi Lh il {e.g ., business
performan<.:t: managc-mt:nt [BPM]) . BJ's major objecLive is to enable interanivt: a<.:cess
(sometimes in real time) Lo c..laLa , Lo enable- manipulation of data, anc..l to give business
managers anc..l analysLo; the ability to conduct appropriate analyses. By analyz ing historical
and <.:urrenL c..laLa , .situations, and pt:rformanct:.o; , d<.:x:bicm makt:r.s gel valuable- insighL-; tha L
enabk tht:m to make more- informed anti beut:r c..lt:cisiom . TI1c pmct: ·s of BI b b-.tsed on
l11t: lran1/urmatiun of data lo information , then to de<.:bions, anc..l finally to actiom .
A Brief Histo,ry o,f Bl
Tht: Lem1 Bl was coined by the Ganner Group in the mid- J99(lo;_ Hmvevcr, as Lhe history
in Lhe prt:vious St:ction poinLo; ou L, the- <.:o.n<.:cpl is much older; il ha.,; its moLo; i.n Lhe J\US
rcporLing .sysLcms of lhe 1970..o; . During that pt:riod. reporting sy.stt:ms vvert: srn dc, wt:rc
two c..limt:n.o; ional. and had no analytkal capabililit:.o; . In tht: t:arly 1980s, the umcept of
EI .- emergec..l. This com_~ pL cxp-.tndc<l lhe computerized support to lop-level managers
and execulivc-.s. ome of the- <.:apa 1ilitic.s inLrodu<.:x.-<l V11crc- dynam.ic multidimensional (ad
hoc or on -demand) reporting. forc-<.:aMing and prcdielion, trend analysis , drill-down Lo
dt:taib , sLatus a<.:ccss. and critkal .succt:ss fac Lors. Thest: fealUrcs appcarcd in doz.c-ns of
<.:ummt:r<.:ial pnxJm:"Lo; until the m id-1990s. TI1en lhe same capabilities and some new ones
appeared under Lhe name BT. Today, a good Bl-b-.i.sc<l enterprise information .sysL<.:'lTl cun­
ta ins all the information cxecuLive.s need. o , Lhe original con<.:cpc of EIS was u··,msfunn<.:-d
inLo BT. By 2005, BI .syslerm sLancd to include artificial intelligence <.:apahilitie.s as ,vcll as
powerfu l analyLical capabililie.s. Figure 1.9 illusLmtt:s tht: various tcx>ls and L<.:x:hniquc-.s tlrn L
may be included in a BT sy.slt:rn . Il illusLmtt: · lhe evolution of Bl as \Yell. TI1t: tools shown
in Figure I.9 provide lhe <.:apahilitie.s of BL The mos.I. .sophbtkaLc<l BT p mducLo; include
most of Lhesc capabilities; others .specialize in only some of Lhem.
The Architecture of Bl
A Bl sy.slt:m has four major <.:omponcnL-;: a D , wi Lh its source data; busi,u_>;_,,_~ analytics, a
colkx:tion of tools fo r manipula Ling, m ining. anti analyzing Lhe daLa in Lhe D\l;'; BPM for
muniloring and analyzing performance; and a 1€Ser interface (e.g. , a dashboard). The
relationship among the.st: components Lo; ill ustratec..l in Figure 1. IO.
The Origins and Drivers of Bl
\Where did m<.xJem appr<.Y.td1es lo daLa warehousing and BI come from? \VhaL arc Lhe ir
and hovv do Lhose roots aJkc.t lhe "vay organiza Lions arc managing chc-.se ini Lia livcs
today~ TcxJay's inve.suncnL'> in informaLion Lcchnology aoc undt:r in<.:n:ast:d .scnuiny in
ll"rms of their boLLom-lint: impact and poLenLiaL Tht: same is true of OW and the BT appli­
cations tha Lmake lhest: inilialive.s possible .
Organizations arc being compdlcd Lo capture-. undersl.and, anc..l hamc.-s their data Lo
support dt:dsion making to improve business ope-rations. Legislation and rcgula Lion (e.g. ,
tht: Sarbancs-Oxlc-y Acl of2002) now require business leaders Lo documenl their business
pmct::ses and to sign off on lht: kgilimacy of the information they rdy on and rcpon to
sLakeholders. :\1orcovcr, business cycle li.mt:s arc now ex.Lremdy compresS(_-<l; foster, more
informed , and beucr decision making is, therefore-. a c.·ompcLitive impera Livc. Managers
nt:t:d tht: rigbt infumu.Uion al Lhc right time and in tht: right place. This Lo; the mantra for
mo<lt:rn approaches lo BT.
rt)OlS,
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1 • An Overview of Bu. ·nes. Imelligeo
oalytics, and Data
ience
17
Querying and
reporting
□ ss
EIS/ESS
Financial
reporting
□ LAP
I
L
Scorecar-ds and
1
_ dashboanls
L
I
Digital cockpits
. and dasllooards
r,--------------...
•
Business *
lnte ligence -"'"'
w~
Workflow
min·ng
Broadcasting
tools
Portals
FilGURIE I. 9· Evolution of Busin.ess l.ntellrgence (Bl).
Data Warehouse
Business Analytics
Environm nt
P11rf0Nnance nd
Strategy
Data
Souroes
---
8 PM strategies
- Org8rwzi ri g
- SurnmlSUing
- Scmid..-diaig
Futu ~ armpcritSIL ;
lnt!!llig,,riL
~~
- Bruw2r
- Ptlrti,I
- 08"11baord
FilG U ,RE 1. 1O· A I-fish-Level Arrlhitec:wre of Bl. (Source: Based on W. Edcerson. Smart Companies .in rJie 21st
Century: The Secrets of Creating Soccessful 8usiness lntel~gem Solutiam. The Data Warehousing Institute. Seattle. WA.
20()3. p. 32. Illustration 5.)
Orga.niza Lion.,; have lo ·work smarL Paying can:ful auc.:mion Lo the managc.:mc.:m of BJ
ini.lia Livc.:s b a nc.:cc.:ss;,iry aspc.:cl. of doing businc.:.-s. IL is no s1.1rpd.,;c.:, then. Lhac organizations
arc increasingly <.:hampioning Bl and undc.:r iLs new incama Lion as analyLic:.,;_ ApplkaLion
Ca.,;e 1.1 il11.1slrJ Lc.:s one s1.1ch application of BJ Lhal has helped many airlines as \vc.:U as , of
umrsc.:. Lhc.: companies offering such services Lo Lhc.: airlines.
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18
Chapte r l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, a nd Data Scie nce
Sabre Helps Its Clients Through Dashboards and Analytics
. abn: b om: of dH.: worl<l k:a<l<:rs in thc Lravd indus­
Lry, providing 10Lh busine.·s-L<r<:onsumer scrvkes as
wdl as busincss-lo- businc.- s servkc.-. h scrves Lrm•d­
ers, lravd agenl.S, corporalion.~, and lravd suppliers
through iL~ four main companies: Travdocity, Sabre
Travd Networl<. Sabrc Airline oh.1tions, and . ahre
Ho..~piLaliLy "olt.1Lions. The currenL volaLile global
economic cnvironmcnL poscs significanL compcliLive
challenges Lo the airline indusuy. To slay ahl.".td of
thc compcliLion , abrc Airline olutiuns recogni:z:cd
thaL airline cxcculives nccde<l enhanccd Lools for
managing Lhdr busine:s decisions by diminaling
d1c trac.liLional, manual , Lime-consuming process of
aggn:gating financial an<l othcr inforrnalion neec.lcd
for acLionablc initiatives. 111is cnables n:al-Lime deci­
sion :upport al airlincs throughout Lhc world LO
maximize their (and in lmn abre's) return on infor­
mation by driving insights, actionable intelligence,
an<l \'all.le for c1.1slomcrs from the gro~ving <laLa.
abre c.levdopcd an Enterprise Tr.i.vel D.i.La
Warehousc (ETD\~' ) 1.1sing Tcrac.lala. Lo hol<l ils mas­
sivc rcservalions data. ETD\\'7 is updated in ncar­
n:al time \ViLh l-Y,uchcs LhaL nm evcry J5 minulc.~,
gathering c.l;,ita from all of. abre's bu.~inesscs. Sabre
uses iLS ETD~· to cn:aLe abn..: Excc.utive Dashboarc.ls
LhaL provide ncar n:al-timc excculive in.~ight.~ using
a Cognos Bl platform wilh Ornde D-.tla lnLegmlor
an<l Ornde Goldengale Lechnology i nfmsLniclun:s.
Thc Execulivc Dashboarc.ls offer Lhcir dienL airlines'
Lop-lcvcl manag<:rs and dcdsion makt:rs a Limdy,
auLomatec.1, user-friendly solution , aggregating criLi­
cal pcrformance mclrics in a succinc.t way and pro­
viding al a glancc a 360-<legree view of Lhe ovcraJI
hcalth of Lhe airline . Al one airline .. abre's E."i:cculive
D.i.shboarc.ls pmvic.lc senior management with a daily
an<l intr.i.day snapshol of key performance inc.licaLors
in a single application replacing Lhe oncc-a-wcck ,
8-hem r pmces..~ of gen<:rnling the samc report from
various <lala somces. 111e u.-e of <lashboarc.ls i.· noL
limited Lo d1c external cuMomers; Sabre also uses
Lhcm for their a.">scssmcnl of intcmal opcrational
performancc.
The dashboards help Sabre's c.usLomers Lo have
a dear undersrnnc.ling of the <law. Lhmugh thc visual
displays lhaL incorpomlc intcmcLivc <l.rill-dmvn capa­
biliLies. IL replacc: flat prescntalions an<l allows for a
more focuscd revicw of 1.hc data \l\,'iLh les..'> effon and
Lime. TI1is fadlila.tes Learn c.li,a log by making the c.laLa/
meLrics pcrwining lo sales performancc available LO
many stakeholders, including Lickcling. seal~ sold
an<l flmvn. opcrational performance inclu<l.ing thc
c.laLa on flighL movemcnL and tracking, cuMomer n:.·
ervaLions, invcnlory, and revenue acro:s an airlinc's
m1.11liple disLrihulion channels. 111c c.la... hboard sys­
tem.~ provide scalablc infrasLn1cture. grnphical user
inLerfac.."e support, daLa imegraLion. and aggn:gaLion
lhaL empowcr airline cxcntlive · to be more proac­
tive in wking actions lhal leac.l lo posiLive impacL~ on
thc o erall hc..-.tlth of Lhcir airline.
~-iLh iL'> ETDW, Sabre mule.I also <le\'Clop oLher
'l eb-l-Y.1.sed analyLical and n:p01ting sol1..1Lions that lcv­
er:.1ge <lala. Lo gain cuslom<:r insighl.S Lhruugh analysis
of customer pmftlc.~ and their sales intcractions to cal­
culatc customer value. This cnahlcs bcllcr c..usLomer
segmcntaLion and insighL~ for \,;alue-ac.ldc<l serviccs.
QUESTIO S FOR DISCUSSION
] . \Vhat L'> traditional n:porLing? Hu1iv b iL used in
lhe organization?
2. How can analyLics be used to transform Lhc tra­
diLional n:poning?
3. How can inrcracLivc reponing assist organiza­
Lions in decbion making'
What We Can Lea.ca from Th.is
Application Case
Thb casc shmvs Lhal organization.~ dmt earlicr 1..1scd
n:porLing only for tracking thcir intcmal business
ac.tiviLics and m<..'t!ting lhe complianc.."e requin:menls
SL'L oul by thc government arc no,,v moving to\\'ard
generming actionable inLelligence from their Lr.tn.~ac­
Lional busincss dala. Reponing has become bmac.lcr
as organizaLicm.~ are now Lrying Lo analyze thc
an::hivcd transactional <lata lo 1..1m.lcrslan<l Lhe undcr­
lying hie.Iden Lrenc.ls anc.l paucrm LhaL will cnablc
thcm lo make bcLLer decisions by gaining insighls
lnLo problematic a:rca.~ an<l resolving lhem LO pursuc
curn:nL anc.l fumre markeL opportunilies . Reponing
ha.~ ac.l\•ancc<l Lo intcraelivc online reports, v,,hkh
enablc Lhe uscrs Lo pull and build quick custom
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1 • An Overview of Bu. ·nes. Imelligeo
n:ports and even p rescnl tht: reports aickd by v i: u ­
;d i:zation Lools Lhat have Lhe ah ility to cormen lo the
dawha.~e, providing Uit: capabililks of digging deep
in Lo su mmarized daw .
S€J11P"Ce:
";S:,b«,
, oalytics, and Data
ience
19
Terndat::i.com. "S:,b«, Airline olulions, - Terry, D . t20J I).
trea.mlines Ded. ion M~king," http:/ J,,n,:v.•.1er:imt.:1m::iga
zi ne.com/\" I I nO➔/ Fe::i tu~e.<;/5'1bre-. I re:, mline.,;.-Decision-M~king,i
(Acces..sed July 20 I 6).
A Multimedia Exercise in Business Intelligence
TU includes vidt:os (similar [o Lhc [dcvbion show csn lO illustrn Lc concepts of analytk:s
in different industries. These are called "B. I Videos (Business ccnario [nvesligaLions).~
ot only arc Lhese entertaining, but tl1ey also pmvide th<: da.-s with some qut:sLions
for discussion. For starters. pkase go lo h LLp://w\vw.Lcrndataunivcrsitynet\vork .com
/Library/ hemdB. 1-Thc-Ca.~c-of-the-Mbconnccling-Passengers/ or w-.v..v.youtube .com
f,,ivatch?v=
ElrF _aKA. \XfaLch the video that appears on YouTubc. E.~sentially, you
have to assumt: tl1e role of a customer service center professional. An incoming flighl is
running laLc. and several pa.~sengerS arc likely to miss Lhdr conneeling flights. There are
seats on one outgoing flight Ulal can accommoda Lc L·wo of the four passenger.-. \X' hich
two pa.~sengcrs should be given priority~ You an: given information about customers·
profiles and relalion.~hips wiLh Lhc airline. Your decisions mighL change as you learn
more about Lhose customers' profiles.
\V.uch the video. p'.luse it as appropriate. and ans-.ver the quesLions cm \vhich pa.­
sengcrs should be given priority. Then n:sume lhe video to g~[ mon: information. AfLer
Lhe vidc·o is complete, you can see the slides related to Lhis video and how the analy­
sis \\~as pn:pare<l on a slide set al. ,vwvv.slide.~han:.ncL/[ern<laLa/bsi-how~,ve-did-it-tl,e
-ca.~e-of-lhe-misconnccting-p111,sscngers.
11-1.is multimedia excu rsion provides an <.:xamplt: of hmv addilional av-.i.ilable infor­
ma Lion thmugh an enterprise D\'7 can a ·sbt in decision making.
Alt.hough some peopl~ equate D. S with BI. these sys[ems arc noL. al present, [he
same. ILis inl£reM.ing to note that some people bdicve Lhat D.. is a p:'drt of BI-one of its
analytical tools. Others think UlaL BT is a special case of D S Lha[ deals mosLly with report­
ing, communication, and collaboraLion (a form of data -oriented D . ). A.noLher cxplana­
Lion (\'llalson, 2005) b tl1aL BI b a resul Lof a c..untinuous revolution, and as .-uch. D . is
one of Bl's original dcmenL~ . Further, a.~ noted in the nexl sccLion omvard, in many circles
Bl ha.~ bc£n suh~umed by lhe new Lcm1s analytics or data science.
Transaction Processing versus Analytic !P rocessing
To illu.~lra Le the major charaneri.~tic.~ of BI, 1rsL we ·will show what Bl b nOL- namdy.
rrJnsaccion pmn:-ssing. \Vc'n: all familiar wich Lhe informaLion systems thal suppon our
LrJnsaelions , like ATM withdrawals, hank <leposiL~ , ca.~h regis[cr scans a[ Lhc grocery store.
and so on. 111cse h ansactfon proce · ing sysLeu1.~ arc con.~tantly invol vcd in handling
upda[es to what we mighL call (Jpe-rali(Jnal databa&.'S. Fm example , in an ATM wiilidrawal
LrJnsaction. we need to reduce our b-,mk halanc..-c accordingly; a bank deposit add~ to an
acc..'<.>l!.lnt; and a groc..-cry stort: purchase is likely rdlcc.ted in the store's calcubLion of towl
sales for tl1e day, and iLshould reflt:cl an appropriate re<luc.tion in the store's inventory for
ilie items we bought, and so on . 111cse o.nline transactio.n processing ( OLTP) systems
handle a comp'-lny's rouLinc ongoing business. [n conlr.ist., a D'\l is [ypically a disLinc.t sys­
Lem LhaL provide · stornge for data Lhat ·will be u.~ed for ,inalysis. The intent of Lhat analysis
is to gi\•c management Lhe ahility Lo sc..uur daw for information ahol!.ll Lhe business, and
i[ can be u.~ed [o provide tactical or operational decbion support, \vhen:by. for example,
0
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Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
.line personnel can make quicker and/or more informed c..lecisions. We will provide a more
technical definition of D'I: in ChapL<:r 2. but suH-lcc iL lo say that DWs arc inlendec..l Lo work
with informalional data used for o.nline analytical proce-Ss:ing (OLAP) systems.
Most operational c..law. in enlerprise resouru:s planning {ERP) s1•slc-ms-anc..l in ics
(_·omplemi::nlary siblings like- supply chain numagemenl {SCM) or CR111--,m.:: storec..l in
an OlTP sysLem. which is a type- of computer procc-ssi.ng whc-ri:: Lhe compuLer rc-sponds
imme<l.iaLely Lo user n:.:qucsl.~. Each ret1uc-1>L is consic..lc-red lo be a tninsaction , which is a
n>mpu[erizc-d record of a discre[e evc-nL. such a.~ the- receipL of invenLory or a cuSU>mc-r
order. In mher words, a lrdnsaclion re-quires a scl of two or more darab-.tsc updates 1.haL
must be- (."ompleLec..l in an all-or-nothing fa.~hion.
The vc:ry design lhaL makes an OLTP systc-m efficknL for lrnnsaclion pnK-cssi.ng
makes it inefficient for end-user ad hoc re-pons , queries , anc..l analysis. In Lhe 1980s, many
business use-rs rcferrc-d Lo thdr mainframes as ~black holes ~ be-cause al.I the information
,vent imo them. hut none ever came back. All requesL~ for re-pons hac..l to be progrnmme<l
by the IT staff. whereas only "prccanm:<l~ reports coulc..l he- genc-raLec..l on a scheduled
ba.~b. anc..l ac..l hoc: real-time- querying w-a.· vinually impossibk. Although Lhe dienl/st:rvt-:r­
ba.~ed ERP sysLems of the- 1990s we-re somewha L more rc-porr-frienc..lly, iL h:as still bc-c-n
a far cry from a desirt:d usability by n:gul:ar, nonlcchnical, end u5'L"rs for things such as
opt:rnlional n:porLing, interactive analysis, and so on. To resolve these issut:s. the notion.·
of DW and BT \Vere credtec..l.
D\Vs contain a wic..lt: variely of d:ata LhaL prest:nt a cohert:nL piclure of hu.~iness con­
c..liLion.~ al a single puinL in Lime. The idea wa.~ Lo crcale a datab-.tsc infr.istruc.1.urc- that V1r.ts
always onlini:: and conLained all tht: information from thi:: OlTP sysLcrm, including histori­
cal c..laLa, huL rc-organiz~xJ anc..l slructure<l in such a way lhaL iL \\'as fasL and dficit:nL for
querying , analysis, and dt><.:bion support. t:paraling Lhe OlTP from analysis and dt><.:bion
support enables the- hendit.~ of Br Lhat we-re descril:x.-<l c-arlier.
Appropriate Plann ing and .A lignment with the Business Strategy
First anc..l foremosl, the func..lamc-nLal reasons for invesling in Bl musL be- aligned with the
(.-Ompany's businc-ss slrategy. BI cannot simply
a u~chnical c-xc-rcbe for Lhc- information
S")'Slt:ms di::partmi::nl. IL ha.- Lo 5'L"IVt: a.~ a ,;vay Lo changt: Lhe mannt:r in wh.ich the company
(.-OnducL'> busine:s by improving it.~ business processt:s an<l lr.dnsforming c..lecision-making
proc.:t: ·ses lo be- more d:ata c..lrivt:n. Many BT con ·uhants and practitioners involved in suc­
ce.sdul BI iniLiaU\'es advise [hat a framework for planning is a necess.ary pre-condition.
One fr.tmework, devdoped by Gartner, Inc. (200 ), decomposes planning and cxeculion
inLo business, organization, funcliunality, and infrtlS/ructure compont:nl.~. At the busi­
nt::s and organi:edtional levds, stralegic: and opt:rnlional obj(.tlivt:: musL be c..ldlne<l while
nmsic..lering the av.tilable organizaLional skills Lo achieve Lhc).~e obj(.-ctivc-s. Issues of organi­
z:alional culture surrounding BI inilialivcs and building enlhusia.~m for lhc).~e inilialivcs and
pn.x:t:durcs for the illlrn-organizational sharing of BT rn:.:st practk-cs must be con.~iden:d by
uppt:r managt:ment-wilh plans in plact: Lo prepare- Lhe organi.zaLion for change. Ont: of
tht: first step..~ in lhaL procc-.~s is Lo assess the L org-dnizalion, the skill selS of the- polc-nLial
classt:s of users, and wht:'Lher Lhe culture is amenable lo changt:. From this a:se:sm<:nt,
and a ·suming th<:re is jusLification ancJ the neec..l to move ahedc..l, a compRny can prepart: a
c..lc-w.Hed act.ion plan . Anmher criilical i ·sue- for BI implcmc-nllilion success b the integration
of several BJ pmjec.ts {mml enLerpri.sc-s use scvc-ra_l Bl projecL~) among tht:msdvt:s and
wiLh Lhe other 1T systerns in Lht: organization and iL~ business pannt:rs.
[f Lhe comp-.tny's slr.tlt.'gy is properly aligned , ith the redsons for D
and BI initia­
tives, and if the comp-any's L organization is or can be made ca~blc of playing it.~ role in
sud1 a pmjc-Cl, anc..l if the requbiLe usc-r community is in place and has Lhe pmpc-r rnoti.v.t­
tion, iL is wbe Lo stan BI anc..l esLablish a BI CompeLency Centt:r, iLhin the company. Thi::
(."enter could serve some or all of the following functions ( Ganner, 2.001):
oc
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• 111.e center L"an <lcmonslr'..ite how Bl is dearly linke<l lo sLr.tlcgy and execution of
SlrII Legy.
• A cenrcr can .serve to cnccmmge inLemnion between the potential btL-;ine.ss u.scr
communities and the IS organization.
• 111e center am serve as a repository and disseminator of bes[ BI pra<.:licc.,; beLwccn
and among Lhe different lines of bu.-inc ·s.
• . Land:ard,; of cxcdknce in BI pranice.s can c advoca Led and encourngcd lhmugh­
out Lhe company.
• 111e I orga.niz:alion can l<.."<1.m a great deal Lhrcmgh i.nternclion with the user com­
munilies, .-uch as knmvlcdgc abou L Lhe variety of types of analytical tools that arc
needed.
• 111.e business user community an<l L organiza Lion can bt:LLer under.stand why 1.hc DV:'
platform mu.,;l be flexible enough Lo provide for changing business requiremenL-;.
• It can help importam sLakehoklers like high-level e.xecll[ive · sec how BI can pl:ay
an imporlanLrole.
Another importan[ success facLor of BI is it.~ ability lo facilitate a real-Lime. on­
<lem.a nd agile environ.menl , introduced next.
Real -Time~ On-D·e mand Bl ls Attainable
The demand for inslant. on-demand acce ·s lO dbpersed information has grown as Lhe m:ed
Lo do..,;c Lhc gap between the opern Lional data and strategic obje-ctives has become more
pressing. As a re.sull, a ca Legory of produc.1.s called real-Um.e Bf applicaU<JnS has emerged.
The inlroducLion of new data-generating lcchnologi<:s, .such as RfID and other sensors
is only accelerating this gro,.vLh an<l lhe su bS<..'tlUenL need for real-Lime BL TrJ.dilional BJ
sysLem..~ use a large volume of . latic data that has hL-cn exlraCLc<.l, cleansed, and le.rd.tied
into a DW lo produce reports and analyscs. However. Lhe need is not ju.st reporting
because users need business monitoring , performance analysis, a.nc.l a.n understanding of
why Lhings arc happen ing. Thesc can assisL users, who need Lo know (virtually in r<.."<1.l
Lime) abouL changes in dala or the availability of relevant report.,;, a lerL,;, and noLificalions
regarding L"vcnlS and emerging Lrenc.ls in .social media application.,;. Tn ad<liLion, business
applicaLion.,; can be programmed Lo act on whal lhe.se rC"",d-1..ime BI systems dbcover. For
example, an SCM applicalion mighLau lomalically phK"C an on..lcr fo r more "widgets~ when
real-time inventory fall. · below a cenain threshold or when a CRM appli.caLion aulomaLi­
cally lriggcr.s a c:usLomer service represent.,Uivc and credit control dcrk Lo chcck a cu.,;­
Lo mer vvho has placed an online order larger 1.han 10.000.
One approach Lo r<..".il-time BI uses the D\'7 model of Lrn.ditional BI systems. In this
ca.~c, prod ucts from innovalive BI p la tform providers provide a service-oriented, ne-dr•
real-time solmion lhaL popula Le.s Lhe D'l much fas Lcr Lhan the typical nightly extract/
transferjluad batch update dc>l..'.s (.sec ChapLL'T 3). A .second approach , commonly calkd
bu sine. ticlivity management (BAM) , is adopted by pure-play BA\1 and/or hybrid BAM­
mi.<lc.llcware providers (.such as Savvion. hemlion oft"v.tre, Villi.a , web\ktho<ls, Quanlive.
Tihco, or Vineyard . ~ftw.lre) . II bypa ·se · the D'l cnLirdy and lL,;es Web services or olher
monitoring m<.."<1.ns lo discover key businc:s evcnL<;. These sofLware monitors (or intel li­
gent agents) can be placed on a .separaLc server in the network or on thc Lmn.~action.al
applicaLion daLab-J.se.s lhemselvcs, and Lhey can usc event- and procc.,;s-ba.,;ec.l appr<YJ.ches
Lo proac.1.ively and intelligently measure and moni lor opernLional process-L'S.
Developing or Acquiring Bl Systems
To<lay. many vendors offer diversified tools. some of wh ich arc complctdy prepro­
grammed (called shells); all you have lo do is imerl your numbers . Thc.~e tools c.--an be pur­
cb:ascd or leased. For a list of products, demo..,; , while papcr.s , and more curren Lproduct
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22
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
inforrn;,uion, sec pro<luct d.ire<.:tork:s al tdwi.org . Fn:e user rcgi.-n..,.uion is reql!.lire<l. Almost
all BT applicalions arc conslructed '-"'llh shells pmvidL-<l hy vendors who may themselves
cn:a Le a cu: Lom solulion for a diem or work with anoLhcr outsourcing provider. The issue
lhat l.Umpanies fact: b which alternative lo select: purchase. le<1se, or huild. Each of these
allemativcs ha.-; several opLions. One of the major criteria for making tl1c decision is ju.-;li­
fica[ion an<l cosL--hem:fiL analysb.
Justification .and Cost-Benefit Analysis
A'> Lhe numocr of potemial BT applica Lion.-; increase.-; , Lhe need lo justify and prionuze
them ari.-;es. This is not an easy ta.-;k due Lo the large numocr of inLangible benefiL-; . Bmh
direcl and intangible ocne .1lS nee<l lO be idcnlific<l. Of course. thb b where [he knmvl­
edge of similar applicalions i.n oLher organization· an<l case studies is ex tremely useful.
For example, The Data \"X'an:housing [n.-;tillllLc ( L<lv,d .org) provides a wealth of information
about pmdm.:ts an<l innovative applicaLion.-; and impkmentalion.-;. Such information can
he useful in csLimaling <lire-cl an<l in<lireel benefits.
Security and Protection of Privacy
Thb i..-; an extremely imp01tanL is.sue in the devdopmenL of any n>mpuLcri.zed system ,
especially BI that contain.-; data LhaL may po.-;S<."ss strategic \'alue. Also. Lhe privacy of
employees and cuslOmers nc-e<ls m be protected.
Integration o,f Systems and .A pplications
'l llh the excepLion of some small appli.ailiom , all BI applicaliom mu.-;t be inLegr.tK-<l \ViLh
other systems sud1 as databases, legacy systems. enterpri.-;e systems (panicularly ERP an<l
CRt\11), e-rnmmcn:.:e (sell sic.le. huy sic.Id. an<l many more. In ad<l.iLion , BT application.-; arc u:u•
ally ccmnecte<l Lo the lnLemot an<l many Limes to infomia.Lion systems of business partners.
furthermore. Bl t.oob sometimes need lo be integraLe<l among themselves, creating
synergy. 1h~ need for imegracion p11.1she<l sofrvvare vendors to continucmsly ad<l capabili­
ties Lo their pro<luc.ts. Cus[omers who buy an all-in-one software package <leal \Vith only
one vendor and <lo not have Lo <leal wiLh system connenivity. BuL. they may lo.se the
advantage of creating system-; composed from the "ocst-of-hrce<l~ l.UmponenlS.
SECTION 1.4 REVIEW QUESTIONS
1. De me Bl.
2. Lbt and describe the major components of BL
3. De me OlTP.
4. De ,me Of.AP
5. LlM some of the implemeni:alion topic.-; a<ldrcsse<l by GarLncr·s report.
6. LlM some other success factors of BL
IIW Analytics Overview
TI1e word analy lic has largely rcplacc-<l the previous individual components of compuLcr­
i.zed <lecbion suppon lechnologies LhaL have been available under various labeb in the
pa.-;L Indeed, many practiLioners and academics now use lhe wore.I ana[vtics in place of BL
Although many auLhors and ccmsultanls ha:ve defined il slighLly diffcn:ntly, one can vie,v
analytics as Lhe process of developing actionable decisions or recommenc.lalions for action.-;
ha.se<l on insight.-; genern Le<l from historical data. According Lo Lhe [nMilute for Operation.-;
Re.-;earch an<l Mamtbrcmcnt Science (I FOR\I. ) , analyLics represent'> the comhinaLion of
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1 • An Overview of Bu. ·nes. Imelligeo
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ience
13
<:ompuLer Lechnology, ma.nagemenl sdenu: Lcchni.q ues, an<l statisLic..~ Lo solvt: rc-.il problems. Of course, many other organizaLions have proposed their own inLerpreLalion.~ and
moLivalions for analytic..~. For example, S Tnslitu Le Inc proposed dghL lcvds of analytics
t.haL begin \ViLh slandardized reports fmm a computer systcm. The.~e repons cssencially
provide a sense of whaL is h appening wir.h an organiz:aLion. Additional tedmologies have
enabled us Lo crea Le more uJstomi.zed rcporL-; Lhal <:an be gencmLc<l on an a<l hoc basis.
The nexL extension of reponing takes u.~ Lo OL.\P-type q rn:ries that allow a u.~er to dig
deeper and <letcrmine specific sou rces of conu:m or o pportuniLies. Tcdmologies aV"'.lilabk
L.oday can also automa lically bsue a lcns for a decision maker ·w hen performance \V.t.rrnnL-;
.sud1 alerts. AL a umsumer level ,ve M.'C such alerts for weather or oLhcr i.-suc.s. But sim ilar
alcrL<; can abo be gcm..r.i.L<..-d in .specific seuings when sales fall above or belm.v a certain
levd "vithin a <:enain Lime period or when Lhe invencory for a spedJic prcx.luct b running
lmv. All of lhe.~e applications are made possible thmugh analysis an<l queries on data hdng
<:ollcuc-<l by an organization. 111c next kvcl of analysis mighLentail staListica.l analys is lO
beuer un<lersLan<l p,mcms. 111c.se can Lhen be Lakcn a step further Lo <levdop for<..'OISL~ or
mcx.lds for pred.icting how cuSLorncrs mighL respond LD a specific markc:Ling <:ampaign o r
onb•uing servicc/ pro<luu offcrings. \Vhen an organi.Za[ion has a 100d vie\v of what b happening and what is l.ikely Lo happen, it <:an also employ other lcx:lmiques lo make rhe best
<led ·ions under the cir<:umslan<:e ·. These eight levd · of analytics are descrihcd in more
detail in a whi le paper by .
(sas.<:orn/ new!S/sasrnm/analyti<: ·_Jevekpdf).
This idea of looking al all rhe data Lo un<lerstand \vhac b happening, "vhal ""ill happen.
an<l how to make the beM of iL ha.~ also heen encap.~ulaL<..-d hy TNFORt\4S in pro po.fog three
level~ of analytks. These Lhr<..'t! level.~ arc identified (informs.org/Communily/ AnalyLics) as
descrip Li ve , pn:<lktivc, an<l prcsc.TipLivc. Figure 1.11 prescnL~ a grnphka I view of Lhesc three:
levels of analytics. It suggcsL~ that lhcse three arc some\vhaLindepcn<lenL steps and one rype
of analytic..~ applicacion.,; leads lo anolhc:r. It also suggc:sts that there is a<:lually some: overlap
across Lhe.~e three Lypes of analyLic ·. In eiLher ca.~e, the inLerconncx:te<l na turc of difl'en:nt
types of analytks a pplications is c:videnL \"X'c ne..xt intm<lu<:c these three levels of analylics.
Pred ictive
~
Q What haPIJ@llBd?
I
What is happening?
What will happen?
Why w II t happen?
✓
✓
✓
✓
What shoold I do?
Why shou d I do it:?
0
Business reporting
Dashboards
ri:I ✓ Scorecards
C:
w ✓- Dat:a warehous·ng
~
~
.0
~
8
'5
0
Well-defined
business problems
and ,opportunities
FIGURE 1.1 I
✓
✓
Oat.El min ng
Text mining
Webfmedia min ng
Forecasting
Accurate projections
of future ovents and
autc:omes
Optimization
Simulation
✓ Decision modeling
✓ ExpElrt: systElms
✓
✓
Best possiol
lmsinoss dl!'cisions
and actions
Three Types of Analytics..
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2-1
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, a nd Data Science
D·escri ptive Analytics
D esc.riptive ( o .r reporting) analytics refers Lo knowing 'ivhal is happening in lhc
organizaLion and 1.mders1..and.ing some underlying Lrend.- and causes of such occur­
n:nces . Firsl, lhis invol ves Lhe consolidat ion of daLa sources and avail.ability of all
n:h:vanl daLa in a form lhat enables appropriale reporting and analysis. Usually, Lhc
devdopmcnt of Lhis da ta info1s1rucrnrc b pan of DWs. From lhis data i.nfraslnicl11.m:
we ca n develop appmpriaLe rcporL.o;, q ueries, alerts. and Lrends using various reporting
tools and techniques.
A signi ,cam technology lhaL h.as become a key player in Lhis area b visualization.
sing Lhe laLesl ,•buali:w1ion wols in the marketplace we can now develop powerful
imighL.-; in the opi:mlions of our organization . Application Ca.o;es 1.2 and 1.3 highlighL
some such application.,; . Color n:nderings of visualizalions d iscu.-sed in Lhesc applications
arc available online or tl,c book's companion 'l eh sile (dssbihook.com).
Silvaris Increases Business w ith Visual Ana lys is and Real-Time Repo r ting Capabilities
. ilvaris CorporaLion wa.,; founded in 2000 by a Learn
of foresL im..lusLry professionals to provide Ledmo­
logicaJ ad,•anccmenL in the lumber and building
material secLor. . ilvari.-; is Lhe firsL c--commL'"rcc plat­
form in the United • latcs specifically for on.:st prod­
ucls and is headquarlL'"rcd in Sealllc. \"X1ashington. TL
Lo; a leading wholesale provider of indu.,;lrial -.vood
producL.o; and surplu.-; building material ·.
Silvaris sells its producL.o; :.md provides intem.a­
tfonal logisLics serviu:s lo more than 3. -oo custom­
ers. To manage various pnx:e.-ses thaL a.re involved
in a u-.i.n.o;action. Lhey cri:ated a proprietary online
lrading platform lo lr.t.ck informalion t1ow relate<l to
lransactions between lr.t.ders, accounti.ng, credit, and
logistics. Thb allo,.ved ilvaris Lo sh:an: ils n:al-Lime
information -.vilh iL.-; cusLomers :and panners. BuL
due lo Lhe rapidly changing prices of material.,;, iL
became necessary for ilvaris Lo gel a real-time viL'"'l,V
of data wiLhoul moving data into a sepamlt:' report­
ing formal.
Silvaris sLarLed ll.o;ing Tableau hccau.-;e of it,;
ability Lo conned "vid1 and visualize live data. Due
lO dashboards crc-.Hcd by Tableau thaL arc c:asy
lo understand and explain, ilvaris started usin,
Tahled.u for reporLing purposes. This hdpi:d ilvaris
in pulling ouL lnform:alion quickly from Lhe daLa and
idcnllfying i.-sues thaL imp.an Lheir busine ·s. ilvaris
succeeded in managing onlinc versus offiine orders
wiLh Lhe help of reports generated by Tableau. ow,
ilvaris keeps track of online orders placed by cus-­
lomers and knows when Lo sen<l renew pushes lo
which customers Lo keep lhem purchasing online.
Al,;o, analyst-; of ihr.i.ris can fill.\"e Lime by generaling
da.-;hhoard,; in.-;tead of wriling hunclrc<ls of pages of
n:porL.-; by using Tahlc-.iu.
Q UESTIONS FOR D ISCUSSION
] . Wh:at was the challenge faced by . iJv:aris?
2. How did . ilvaiis solve ils problem using data
visualization with Tableau~
What We Can Learn from This
Application Case
Many industries need lO analyze data in real Lime.
Real-time analysis enables tl1c analysts lo identify
issues Lhal impact thdr busine:s. VisualizaLion is
sometimes the besL \Vay lo begin analyzing Ll1e live
data streams. Tableau is one such data visualiza­
tion tool Lhat has Lhc capability to amdyzc live data
wiLhoul bringing live daLa inlO a scp-.irnLe reporting
formal.
T:ible:iu_com,-. ih,.aris Aug,men~ Pmpriet:uy Tech,mlogy
Platform wilh T::ibleau's Rc:al-Time Reporting C:ip3bilitic-s,"
http://wv,..,,.•.tahleau.corn/sile.<;/defau It/files/ case-studies/ s.i l,•3 ris­
hus.i m:·s.,;-d.a.~hboards_0.pdf :iccc-s!<ed July 2016); "ilvaris..com,
"O\"en•iew,- h11pJ/v.•w"'·s.ilvarLuom/Abou1/ (:icce. sedJuly 2016).
So111"cl!'s:
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1 • An Overview o f Bu. ·nes. Imelligeo
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ience
25
Application Case I.
Siemens Reduces Cost w ith the Use of Data Visualization
iemcm is a Gcnnan company hc-.i.c.lq uarH:rc<l in
Berlin. Germany. IL i · one of the worl<l's largest com­
p,mies focusing on Lhe areas of dcctrific.uion, amo­
malion , anc.l digitalization. h has an annual revenue
of 76 hillion euros.
The vbual analytics group of Siemens is Lasked
wi Lh en<l-to-end reporting solutions an<l consulting
for all of icmens internal BT need,; . 111b group was
facing the challcnbre of providing reponing solutions
to Lhe cnlire Siemens organization aero ·s tliffi..:n:nL
c.leparLmcn Ls while maintaining a balanct: heLween
governance an<l st:lf-servicc capabilities. iemt:ns
needc<l a platform lha Lcould analyze Lheir mul Liplc
cases of cusLomcr saLisfunion surveys, logistic pro­
cesses, anc.l financial rcponing. Th is platform should
be easy to use for Lheir employees so drnl they can
use this c.laLa for analysb anc.l <ledsion making. In
addition, Lhc platform shoul<l be e.tsily intcgmtcd
,vi Lh exisLing icmens systems an<l give employees a
sc-,1mless u.,;er experience.
TI1ey SLarw<l u.,;ing Dun<las BJ, a leading glolY.d
provi<lcr of BI anc.l data vbuali:za.liun solutions. IL
allowed Siemens to create highly interacLive <lash­
ho.ard,; lha L c:nahlec.l . iemcns lo dc:L<..'t.'.L bsues early
anc.l Lhus s.avc a signiflcanL amount of money. The
dashboard'> developed by Dundas BI helped iemcns
global lugisLics organi:za.Lion answer qucsLion.,; like
how diffcrenL.-upply r.i.Lcs al <liffercnL locations affect
Lhc opL'mLion, Lhus helping them Lo rL-<lucc c.-ydc lime
by 12% and scrn p cost hy T %.
QuESTIONS FOR DISCUSSION
J. \"XThat challenges wen: facc<l hy
icmen.,; visual
analytics group ?
2. Hmv c.li<l Lhe data visualizaLion tool Dunc.las BT
hdp . iemcns in n_-<lucing cosL?
What We Can Learn from This
Applicatio n Case
Many organizaLions wanL tools LhaL can he usc-<l Lo
analyze data from multiple <livi.sions. These Loob can
hdp them improve performance anc.l make daLa c.lis­
covcry Lranspan:nL to their u.,;ers so Lhat Lht:y can
identify i.-sues wiL.hin the husiness easily.
Sourres: Dunch .corn, -How S:iemen~ Dr.:J~icilly Reduced Q):S"f.
with M:m::iged Bl Applic:aticm.~.- http://w\"\.•w.dundll.unm/re,;ource
/getca..-;cstudy?c::i:seStudrName=09-03-2016-. iemens%2FDu nd.as
-81-Siemcn,;,.C:J.se--. tudrpdf (acce.-.'-Cd July 2016); \~' ikipedi::i.org.
" l EMENS,"' hltps://en.wikipedi::i .org/wiki/ Siemens (acces. eel
July 2016}; iemens.com. ·Ahrn.Jt iemen.~.- h11p://www.siemens.
carn/abou en/ C:iccc-.s.<;edju!y 2016).
Predictive Analytics
Predictive anal.y tics aims Lo dcLcrmine what b likely Lo happen in Lhe fmure . 111is analy­
sis is based on sLalistkal Lt:chn.iqucs as wdl as other more recently <levdopec.l techniques
r.hat fall under Lhe generaJ calcgory of data IHining. 111c goal of these tcchniquc..,; is lO he
able lo prc-<lic:1 if the cusLomcr is li.kdy Lo swi[ch to a c...'umpeLilOr ("churn~). what r.hc cu.,;­
Lomcr v,rould likely buy next and how much , what promotions a cu.,;lomcr woul<l resp ond
Lo, whcLher Lhis customer is a creditworthy rbk, anc.l so fonh. A number of lechniques
arc u.,;ec.l in <levdoping prc-<licrivc .analytical applicalions , including various d.as.sifictlion
algorithms. For example, as described in ChapLcrs an<l ~, \Vt: can use da.,;sification lech­
n.iques such a.,; logistic regression, decision tree models, a n<l neural network.,; Lo predict
how •.vdl a motion pic.turc "viii c.lo a Lthe hox office. \':'c can a.bo u.,;c d usLcring .algorithms
for scgmenLing cusromC"rs inLo <liflcn:nl dusters lo be able to LargcLspecific promO[ions lO
Lhem . Finally, we can use a,.,;sodalion mining techniques Lo estimate relaLion.,;hips heLvveen
<lifkrcnL purcha.,;ing hehaviors. That is, if a cu.,;tomer huys one product. \\rhat cbe is d1e
cuMomer likely [o purcha.,;e? uch a.nalysb can a.,;sist a n:tailcr in rec..."Ommending o r pru­
moLing relaLc<l pro<luc.ts. For example. a n y product search on Amazon.com results in Lhe
retailer also suggesting oLher similar pro<luc:Ls t.haL a cu.-Lomcr may be inlercsLed in. We
,,viii study these techniques an<l their applica Lion.,; in Chapters 3 through 6. ApplicaLion
Ca.,;e 1. illusrr.atcs one such application in sporL,;_
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26
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
Application Case I.
~
- - - - - - ~
Analyzing Athlet ic Injuries
Any aLhktic activity is prone Lo injurit:s. If the inju­
ries art: not harn..llt:d propt:rly, then the lt:am sufft:rs .
sing analyLics Lo understand injuries rnn hdp in
ckriving valuable insighL-; Lhal would enable c<Y.tcht:s
an<l team doctors lo manage t.ht: learn <.:omposition,
un<lcrst.and player profiles, and uhimaLely aid in bet­
lt:r <lc<.:bion making con<.:t:rning which players migh1
bt: avaihtblc lo play al any given timt:.
In an explornlory study, Oklahoma Stale
nivt:rsiLy analyzed American football-rdaLe<l sports
lnfuries by using rt:porLing and prt:dinivt: analytics.
Tht: projt:cl follo\vetl Lhe CRT P-DM metho<lolot.'Y
(lo he <lescribc<l in Chapter ) Lo understand the
problt:m of making r<..x.:ommemlations on managin r
injuries , untlt:rslanding 1..ht: various <lat.a clement<;
coHe<.:tL-<l about injuries, dC'.i.ning tht: data, Ut:\•doping
vbualizations Lo ura\V various inft:rences, building
PMs 1.0 analyze the infmy healing limt: period, and
drawing S<..'qUt:ncc rules to prctlkl Llrt: rdationships
among t.ht: injurit:s and Lhe various body part part<;
afflicted with injurit:s.
The injury data set u>mistcd of mort: dr::m 560
fooLl-Y.t.11 injury rLx.:ords, which wt:re caLegorizt:d into
inf ury-speciflc \o:-t1riables-lxx..ly pa 111./ sitt:/la Lera Iit y,
action takt:n, scvt:rily, injury type, injury sta.n and
ht:aling dalt:S-'.t.nd playt:r/ sporL-sp<..'ciflc variables­
playt:r TD, position played, activity, onscL, and game
loc:aLion. HC'aling limt: ,,va.~ rnkulaLed for L"'ach
rt:cord. which -.vas c:la.ssiflt:d inw dHkrcnl. st:ls of
lime periods: 0-1 monLh , 1-2 monLhs. 2
month.~.
-6 months. and 6-2 months.
Various vbualizations wen: built Lo draw infer­
e nces from injury data sel informaLion dt:pkling
the hL".i.ling time period a.~soc:iated with players'
posilions. se\•erity of injurie.· and Lhe hC'aling lime
perioc..l, Lreatmenl offert:d an<l Lhe associatt:d hL-aling
lime period, major injuries afflicting body parts, and
so forLh .
eurnl nt:lwork models were buih Lo pre­
dkt e-.tch of t.ht: healing categories using 18:\:1 • P .
J\,todder. Some of tht: predktor variables \Vere
rnm:nL status of injury, sev<:riLy. bo<ly parL, body siLe.
type of injury, activity, evenL localion, action taken,
and position played. The success of classifying the
ht:aling category vvas quiLe gooc..l: A<.:curaq• \\'aS
79.6% p e rcenl. Ba.~t:d on the analy.-is, many busi­
nt: ·s recommendations were suggesK-<l. indutling
employin r more specialisL~' input from injury onset
in.~lead of letting lhe training room staff screen the
infured pl.ayers; Ir.tining players al dden.~h<e posi­
tions lo avoid being injured; and holding prauice lo
thoroughly s..ifety-<.:he<.:k mechani.~ms.
QuEsnoNS FOR D ISCUSSION
L \"X' h.at types of analytic.~ arc applied in 1.he injury
analysis?
2. How <lo \•isualizalion.- aid in understanding 1.he
daLa and ddivL"ring insights inl.o Lhe data?
3. What is a da ·slfka Lion problem?
\'<' hat can be derivt:d by performing .sequen<.:e
analy.si.-?
What We Can Lean1 from This
Application Case
For any analytic.~ pmj<-'Cl, iL is always important
Lo understand Lht: bu.~iness domain anti the <.:ur­
rt:nL stale of Lhe busines..~ problem through exLen­
sive analysis of t.ht: only rcs<.mrce-hbtorical data.
isualizations ofLen provide a great tool for gaining
the iniLial insights inLo data, which can be further
rt:fin<--<l hased on expcn opinions Lo identify the rt:la­
tive importance of Lhe data demt:nL~ related lo the
problem. VisualizaLiom abo aid in general..ing idC'aS
for oh.~curt: problems, which can be pursued in
building P l~ that could help organizations in deci­
sion making.
So111"ce: Shard:i. R., A. :imoah. D., & Ptmm, N . (2013). "Rese=irch
::ind l'eclagogy in Bu.~ine. s Analytic.~: Opportunilie.~ :ind lllu!<1.r::i.tin=
E-r.1mple:s.- Jour1111I of Compulif1R and !11/ormalion n·cbnofogy.
21(3). 171-Ul2_
Prescriptive Analytics
Tht: third cuegory of analytics is rcrmed prescripth.,e analytics. Tht: goal of pn:suiptive
analytic.~ is to rt:cognizc what b going on as wdl as the likely forec:aSL and makt: deci­
sions lo achieve Lhe hcsr performance po.~sible. Th.is group of l<-x.:hniques has hislorically
bt:t:n studied under the umbrella of OR or management sc:icn<.:es and are generally aim<--<l
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1 • An Overview o f Bu. ·nes. Imelligeo
, oalytics, and Data
ience
27·
al optimizing lhe pcrforrmi1.m.:e of a system . The goal here is to provide a decision or a
rel.um.mendation for a sped 1c action . 111.esc rccommendaLiorn; can tx: in the form of a
specific yes/ no dccbion fur a problem, a sp<..-ci 1c amounl (say, price for a specifk item or
airfare lD ch:uge), or a l.umplc[e set of pro<luclion plan.,;. The decisions may be presenled
Lo a decision m:iiker in a report or m:iiy be u.,;ed direcdy in an automa[c<l decision rules
sysLem (e.g., in airl.ine pricing system·). Thu.,;, these types of :iinaJytks c:m also tx: termed
decision or norm ative analytics. ApplicaLion Case 1.- gives an example of such prescriptive analylic applicalions. \X7e "viii lC'am abouL some aspecr.,; of prescripLivt:' analytics
in Chapter 6 .
Analytics Applied to, D·ifferent D·o mains
ApplkaLion · of analylics in variou.,; indu.,;rry sectors have sp:iiwned many related :trC'dS o r
al ka.,;l buzzworck It is almosl fashionable lo auach the word m1alytics lo :iiny spedJic
indl!.lstry or Lype of data. Besides the gcnt:'ral catt:'gory of tt:'xl analytics-aimed a[ getting
vall!.le out of LcxL ( [o be S[udic<l in Chap[cr -)-or Wt:"b an:iilytics-a.n:iilyzing Web data
A Specialty Stee l Bar Company Uses Analytics to Determine Avai lable-to-Promise Dates
Thb applicaLion case is ha.,;ed on a project LhaL
indl!.ldes one of u.,; . A company LhaL docs not wish
lo disclose iL,; name (or e\•cn I.he precise indusLry)
,va.,; facing a major problem of m:iiking decisions on
which inventory of mw materials lo use lo saLisfy
which customers. Thb company supplies cusLom
configured steel bars lo its customers. Thcst:' bars
may be cul inLo sped 1c shapes or sizes and may
have unique m:iiterial aml finishing n:quin:menLs.
The company procures rn.w maLcrials from around
tht:' world and sLores Lhcm in ils \Varehoust:'. \Vht:'n a
pro.spenh·t:' cuMomer calls tl,t:' company Lo rcql!.lesL
a quote for lhe specialty hars mccLing specific mate­
rial requircmt:nL,; (composition, orit:,ii.n of Lhe metal,
quality, shapt:'s, sizes, etc.), the salespt:'rson usually
ha.,; jusL a lhtk bit of Lime Lo submil such a quoLc
including the <late when the prtxJucL can tx: deliv­
ered and, of course, prices, and so on. h must make
available-Lo-promise (ATP) decbion.,;, whkh dctcr­
mint:' in real Lime Lhe dates wht:'n iL can promise
delivery of produ<..:Ls th:iit cuswmcrs requested dur­
ing the quoLalion stage. Previously, a salesper ·on
had Lo make sl!.lch dccbions by analyzing reports
on available inventory of r.l.w materials. . ome of
the avail.able nn;v malerial may h.avc already heen
commiLLc<l lo another customt:'r's order. Tims the
inventory in stock may noL really be Lht:' free inven­
tory available. On the other hand. lherc may he rnw
material LhaL L,; expected lo he delivered in the near
future that could also be used for satisfying tl1e or<ler
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from Lhis prospt:'c.U\'C customer. Finally. tht:"rc might
even be an opportunity lO ch:iir •c a premium for
a new order by repurpo..,;ing pr<..·v iousl:v commiued
invenLory lo satisfy Lhis new order while delaying
an already commilLcd order. Of course, such deci­
sions should be based cm lhe cost-benefit an.al}•ses
of delaying a previous order. The system should Lhus
he able Lo pull rl.".i.l·- lime <lala ahm.1L inventory, com­
mitted orders, incoming raw material, pro<lucLion
consLrainL,;, and so on.
To .-upporl these ATP decisions, a rC'<1l-timt:' DS
,va.~ devdoped Lo find an optima.I a.,;signmclll of tht:'
a.\·ailablc inventory and Lo supporL additional whaL-if
analysis. The D . uses a suite of mixed-integer pro­
gmmming mo<lels LhaL are solved using commerdal
soft\\1art:'. TI1t:' company ha.,; incorporaLcd the DS
inLo iL,; entcrprbe resource planning system lO seam­
le:sly fadlitaLe its use of hu:ine ·s analytics.
Q UESTIO S FOR D ISCUSSION
L \'X'hy would reallocation of inventory from one cus­
tomer Lo another tx: a major is.,;uc for disc.u,;sion?
2. Hmv could a DS help make these decisions?
So1ffce: P,..1.jouh Food, M., Xing, D., H::irih::u:m, ., Zhou, Y.,
Bab..~md:m:im, 8., Liu, T., & Sharcl:J, R. (1013). "Av::iil::ih!e--to­
Promise in Prncticc:: An Applic:ition of An::i.l)'lics in I.he • peci:iltr
ted Bar Prrx:luct... lndtL,;il)•.· Interfaces, 43(6), 503-517. hup://
cb:.. doi.org/10. J 287/inle.2013.0693 (accc.,;.s;ed jul)I 20 I6L
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28
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
strcams (also in Chapter 5)-many indusLry- or problem-specific analytics pmfes.-;ions/
stn.:-.1.m; have been devd.oped_ E.xamples of sud1 areas are marketing analytics, retail analytics.,
fraud analyLics , tmnsportation analyLics, hed.lth analytics , sporL'; analytics, wlenL analyLics ,
behavioral analytics. and so fcmh. For example. Scccion LI immduced Lhe phrase sport ·
a,u;llytic.s. Application Case 1.1 could abo be termed a case sLu<ly in airline amdylic.-;. Th<:
rn.:..xt seCLion ,viii introduce health analylics and market analytics broadly. Literally, any sys­
tematic analysis of dala in a spc:ci.fic sector b bdng bbekd as ~(fill-in-blanb)" analyLic.-;.
AILhough this may result in ovc:rsdling the concept of analyLics, the bcne 1t b lhaL more
people in sped 1c industries arc: awar<: of I.he: po.,ver and potential of analytic.-;. It also
provi<lc:s a focus lo professionals <levdoping and applying the: concepts of am1lylics in a
vc:nical sector. Although many of the techniques Lo dt!velop analytics applicalicms may
be: common. there are uni4ue bsues within e-J.ch vertical segmc:m chat influeno:: how the
<lala may be c:ollc:c.te<l, procc:ssc:d. analy:z.e<l, and the applicatiom irnplernc:nLe<l . Tims ,
the <liffc:rentiation of analytics ba.-;e<l on a \'enical font· b good for Lhe overall growth of
the: <li.-;cipline.
Analytics or Data Scie-nce?
Even as Lhe conu.:pL of analytics b n_x.:e1vmg more an<:nlion in in<lusLry and academic
cird.c:s, another Lerm ha.-; already been intm<luce<l and is bc:cuming popular. 1e nev.r
term b data cience. Thu.- . the prncticioners of data science are <law sden·LisLS. D. J- Patil
of Linke<lln is somelimes crc:ditc:d with cn:aling the: tc:rm data science. There have: bc.x.:n
some anempts lo describe Lhe differences be11.veen <la.ta analysts and data sdc:nList.-; (e.g.,
S<..t emc.com/<.:ollal<:raVaho11.ll/ ncws/ em<.:-<lata-scknu.:-sLu<ly-wp.ptlf)_ On<: view is thaL
data analyst is ju.-L another Ll.'TITI for pmfes.-ionals \\rho wc:re doing BI in the form of data
compilalion. dc:aning, n.:poning, and pc:rhaps sorn<: visualization. Their skill sels includ<:<l
Excel, some QL knowledge. and rcporting. You would recogniz.e those capabiliLic:s as
dc:scripli.v e or reporting analytics. In conu-asL, a tlaw. scicnti.-;t b respon ·ible for predic­
tive analysis, slaLislical analysis, and more a<lv.mcc<l analytical [cmb and algorilhm..-; . They
may have a <lc:c:pc:r knowledge of algorithms and may recogniz.e thc:m under various
labeb-<law mining, knowkdge discovery, or machi.ne led.ming. ome of these profes­
sionals may also need deeper pmgrnmming knowlc:dgc: Lo be able: Lo wriLe co<lc: for data
dc:aning/analysb i.n current \Veb-oriemec.l languagc:s such a.-; Java or Pylhon and slatbtical
Ian ruages such as R. Many analycics profe.-sionals abo need lO build significant expcrlise
in stalistical modeling, cxpcrimentaLion, and analysis. Again , our rcaders should ren>gnize
that Lhesc: fall u n<ler the: prc:dictivc: and prescriptive analytics umbrdla. Howc:ver, pre­
scripli \'e ana.lytics also includes more: significan[ expcnise in OR including oplimization,
simula[ion. decision analysb , and so on. Tho..-;e \vho cover these: ,1dd-; a.re more likely lo
he called data scknlislS lhan analyLic.-; profe ·sionals.
Our vie\\' is Lhat Lhe <lblinnion beL,vc:en analytics and data sci.c:mist is more of a
dc:grec of technical knowlcc.l,b>e and skill sels lhan funnion.-;. IL may also be more of
a db1.inc1ion acmss <lbcipli.nes. Compu[er sci.c:ncc.:, statis[ics, an<l applied malhemalics
programs appc-,ff to prefer th<: dala science label, reserving lhl" analytics labd for mor<:
business-orientt.-d profes.-;ionak A.-; anorher c:xamplc: of this, applied physics profession­
als have pmpo..-;e<l using nefwor-k science as Lhe Lerm for d<:scribing analylics lhal rcla[e
to 1,•:roup.-; of people-social network..-; , supply chain nc:tworks, and so fonh. Ect hup://
baraba.o;i.com/network.-;cienccbook/ for an cvolving Lextbook on this topic
Aside from a de-.u diffc.:rencc: in the skill seL'> of profc:ssiorn,1ls who only have to do
desc.Tiptiv<:/ rcponing analytic.-; versus tho..-;e who engage in a.11 rhree Lypes of analytic.-;, the
disLinc:Lion is fuzzy t.'Lwc..x.:n the two labels, al bc.~1.. \Ve observe tha[ graduate: · of our ana­
lytics pmgr.1.m. · Lend Lo be rcspon.-;i ble for task.-; whid1 are more in Iine v,riLh c.lata scic:nce
prufessiona.ls (as <ldine-d by some circles) tl-r,1.n jus.t rc:porting analytic.-;. Thb b<x>k b dearly
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1 • An Overview o f Bu. ·nes. Imelligeo
, oalytics, and Data
ience
29
aimed al inLro<ludng lhe <.:apabiliLies an<l fun<.:Lionali[y of all analylic:s (which include data
sdeno.:), nol just reporting analytics. From now on, ,m: will U.'><.: Lhc.,;e Lerms lnl.crchangeably.
SECTION 1.5 REVIEW ·Q UESTIONS
1 . Define
iina£vtics.
2 . \\"'i'ha[ is desuiptive analytic,? 'I; " ha1 arc Lhe various tools lha1 arc cmployed in dcsuipLive analytic.,;?
3 . How is dcsuiplive analyLic.,; dlffercnL from tr.tdit.ional reporting'
4. '\l hal i,s a DW? How can <la.la warchousing le<.:hnology help lO enable analylics?
5 . '\l hal i,s prcdklivc analyLlcs' How can organi.:ZaLions cmploy prc<llctive analyLic.,;?
6 . '\l haL i,s prescrlpLivc analytics? Whal kind · of problem,; can bc solvcd by prcscripLive
analy1..ks?
7 . Define mo<lding from lhe analy Llcs perspcuive.
8 . ls ii a goo<l ided lO follow a hicrarchy of dcscriptivc and predinive analytics beforc
applying prescripLivc analytics?
9 . How <.:an analyLic.,; aid in obje<.:live decision making?
•Iii
Analytics Examples ·n Se 1e cted Domains
You will see examplcs of analyLic.,; applications thrnughouL various chaplcrs . Tha[ ls onc
of the primary appr<xd<.:hes kxp<.k'Sure) of Lhi s book. In this sccLion, we highliglll Lwo
applkaLion are.is-he..tlth<.:arc and retail, wherc there have been Lhe most reported appli­
<.:aLion.,; and succe.'Sses.
Analytics Applications in Healthcare-Humana !E xamples.
AlLhough h<..".tllhcarc analyLics span a ,vi<le v.triety of appli<.:alion · from prevention lo <l.iag­
nosL'S to dfident opcrnLions and frnud prevention, \Ve focu.'> on some applk.t.Licms Lha Lhave
been developed al a major health in.surancc company, Humana. According lo I.he u>mpany
\Veb siLe, ~The company's slm[cgy integrales care delivery. 1he memher e xperien<.:e . an<l din­
ical an<l (.Dnsumer in:sighL'> 1.0 encourage engagement, behavior change. pro~<.:live clinical
ouLrea<.:h and wellness .. . ." Ac.1hieving these strdLe,1;,,i_c goals includes sign.lficanl investments
in informa Lion le<.:hnology in gcncral. an<l analytics in p-drticular. Brian Leclaire is senior
vice presi<lenL and CTO of Humana , a major h<..".ilth insurnnce provider in the United Sta[es.
He ha,; a PhD in ML from Oklahoma Sla[C niversily. He has diampioned analytic.,; a,; a
u>mpeLiLive ditTcrenliator a[ Humana-induding co,·pon.,;oring the crcalion of a <.:enLer for
excdlence in analytic.'>. He descrihcd the following projeCLS a,; examples of Humana's ana­
lylics lnlLiaLives, le<l by Humana's Chief Clinical Analytic.,; Officer, Vipin Gopal.
Example 1: Preventing Falls in a. Senior PopulatioB­
An Analytic App.roa.c h
A<.:ddental falls are a major health risk for adult'> age 6- ye.irs and older wilh
one-Lhir<l experiendng a fall every year. 1 Falls are also Lhe l<..".tding fa<.:tor for
both fatal and nonfatal injuries in older a<lulL'>, wilh lnJurious falls ln<.:redSing
Lhe risk of disability by up Lo 50%. 2 The <.:osL-; of fall· pose a significanL sLrain on
1
hu p://v,nvw.cdc.gcw/homcmdrccre:ation::ils:1fc1y/ falb;/adul1 f:ills..html.
l Gill, T . .\t .. Murph)·, T. E.• Gahb:iucr. E. A.. et :1!. (2013). A.•,sociation of injurious falls with disa.bil­
it}• oulcomcs :in.cl nursing home ::idmi sicms in commun it)t living oldc:,r pc:o::sons. A ml!!'l"ia.111 }mt nw/
of Epit:lemiolog►', 1 ~3). 4 lli-425-.
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30
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
the __ heallham.: sy.stem, ,v:iLh Lhe direcLcosts of falls e ·timated a l 31 billion
in 2013 alone_ 1 V:' ilh Lhe per-cenl of seniors in lhe __ populaLion on Lhe rise.
falb and associated cusLs are anlidpaled lO inCTea.~e- According Lo Lhe CenLers
for Disease Control and Prevention (CDC), ~Falls an: a public health problc:m
that is largely prevenLable _" 1
Humana is lhe nation's second-largest provider of Medicare A<lvanlage
bcndiL~ wiL.h appruxim.alely 3-2 million members, mc>..~l of whom are seniors_
Kee-ping lheir senior members wd l and helping them live safely at Lhdr homes
is a key business objective. of \vhi<.:h prevenLion of ,alb is an important LUm•
ponenL However, no rigomu.~ meLhodology wa.~ available lO identify individu­
al~ most likely lo fall, for whom falls prevenlion d'forL-; would be bendki:aL
Unlike chronic medical umdiLions such as d iabetes and cancer, a fall is noL a
"vdl-<le mc-d medical condilion. In addilion, falb are usually underrepone<l in
claims data a.~ physicians typically Lend to code Lhe comequ.ence of a fall such
as fractures and di~localicm..~ . Allhough many clinically administered a.~ses.·
menL~ Lo identify fallers exist, they have limited re,:1ch and lack sufficicnl pre­
dic.ti\'e power_:J As such. chere L· a nc-ed for a prospc-Clive and acc.1..1rale mc-Lho<l
lO identify indh•iduals al greaU:Sl risk of falling, so that they can be proac.ti.,•dy
managed for fall prcvenlion_ The Humana anal;•tic..~ team undertook the devel­
opment of a Falls PttdicLive Model in this LUnlexl. his the 1rsLcomprehensive
PJ\•1 reported thal u tilizes administr,ilive medical and pharmacy claims, clinical
data , temporal clinical palLerns. consumer informalion. and other daca Lo iden­
tify individuab al h.igh risk of falling over a lime horizon_
Today, Lhe Fa ll.~ PM is cenLral Lo Humana·s abiliLy Lo idenlify seniors who
could bcnefiL from fall miLigaLion imervenlions. An iniLial proof-of-concept
,vith Humana consumers. represenLing the Lop
of highest risk of falling,
demonsLrnLe<l lhal the consume-rs had increased uLiliZaLion of physical Lherapy
service-·, indicacing consumers an: Laking active steps IO reduce lheir risk for
falk A second iniLiaLive utilizes the Falls PM lo idemify high-risk individuab
for remoLe mon.iLoring program.~- Using the PM, Hu.man.a \\'a.~ able Lo idenlify
20,000 consumers al a high risk of folb, who bcnefiLed from Lhis program_
Identified consumers V1rear a device LhaL dctecL~ falls and alerts a 2 /7 service
for immc'<liale a.~sbcance_
Th.is work was recognized by lhe AnalyLks leadership A\vard by Indiana.
niversiLy Kelly chool of Bu.~iness in 2()15 , for innovaLive adopt.ion of analyl•
ics in a husinc-ss envimnmenL
'Gates •. -, milh, L. A_. Fisher, J D _. el :iL 2.008)_ Systematic l'"c=','ie\v of accuiracr of :screening inslru­
menls fo r predicting fall ri. k :imong independenti}' living older :idulL~- Jmmwf of Rt!babi/iration
Rt!S£arcb and Del 'f!lopmenr, 45(8). 1105-1116_
Co11rributors: Harpr-ect ingh, PhD; \ripin Gopal , PhD; Philip P:iinter, MD_
Example 2 : Humana•s Bold Goal- Application of Anal.y tics to
Define the Right Me,t rics
[n 201 , Humana. Inc_ announced iL~ org,mizaticm 's Bold Goal Lo improve the
healL.h of the communilics iL serves by 20% by 2020 hy making il c-a.~y for peo­
ple lo achieve Lhdr besl heallh_ The communilies LhaL Humana sc-rves can be
defined in many ways, including geograph.ically (slate, cily, neighhurhood) , by
product (Medicare Advantage, empluy"er•ha.~ed plans. individually purcha.~ed),
o r by clinical profile (priority condiLions including diabcLes, hypcnension,
CHF [congeslive hL"'drt failun:] , CAD [coronary artery dise,:1se], COPD [chronic
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31
obstrncLive pulmonary <l.ise-<1.se], or depre ·sion) . nderstanding L.he h<.."<1lth of
these comnmn ities and how Lhey track ewer Lime is critical noL only for Lhe
evalualion of the gcr,il, bu Labo in crafting Mr,uegics to improve the h<..",ilth of
the \vhok membership in ils enlirety.
A challenge before lhe analytics organi.Za Lion was [o idemify a rneLric lha L
captures Lhe cssenc..-e of Lhe Bold Grn1I. Objoclivcly mea.~ure<..I Lr.tditional h<.."<1lLh
insurance metric.~ .such as ho..~piLal admi.-sions or ER visits per 1,000 persons
wou ld nOl capn.ire lhe spirit of Lhis new mbsion. The goal wa.~ Lo idemify
a meLric thal captures health and iL'> imprcwemenL in a community, but ·w as
also rdevanl Lo Humana as a busine.-s . 1l1rough rigorous analytic evaluations,
Humana eventually .selected "Healthy Days; · a four-q ues Lion, quali Ly-of-li.fe
questionnaire originally developed by che CDC Lo track and mea.~ure Lheir
overall progress toward lhe Bold Goal.
IL was c rili.cal w make .sure thal the sdecled meLric vva.s highly corre­
la Led lo hcal Lh and bu.sine.ss melrics, .such t.ha Lany improvement in Heallhy
Days resulLe d in improvc..-<..I heahh and beuer b u s iness results. Some exam­
p les of hmv "H<..",1I Lhy Days" b corrda[ed Lo metric· of in Lerest inclu de Lhe
following:
• Jndh•iduals \Vith more unhealthy days (lJHDs) exhibic high<:r ucili.zaLion a nd
coM pa[[ems. For a 5-day incr<.."<1se in HD.s, Lhere L,; (a) an 82 incr<.."<1Se in
avernge monLhly medical and phammqr co..~lS. ( b) an inc.Tea.~c of 52 inpalienL
adm it.~ per 1000 paLienL~ . and (c) a 0.28-<lay increa.~e in average length of
Slay. 1
• Individuals who exhibi[ heal Lhy behaviors and have Lhe ir chronic condicions
,veil managed have fe,;ver L'HD.s . For example, vvhen ,ve look al individuals
wiLh diabetes. HD.s a rc lower if they obLained an LDL scree ning (- .3 lJHDs)
or a diabetic eye exam (-2.3 HD.s) . Likewise, if 1.hey have controlled blood
sugar levels m<..",isured by HhAK (- 1.8 lJHD.-) or LDL levels (- 1. 3 HD.s) .'
• Jndh•iduals wilh chronic condiLion.~ h ave more lJHDs lhan those v.rho do noL
have: (a) CHF (16.9 HD.s) , (b ) CAD (l . UHDs). (c) hypercension (13_3
HD.s), (d) diabeles (1 .7 UHDs), (e) COPD (17 .
HD.s), or (f) depression
(22 .
HD.s). I 3.•
Hum:ma 11.as since a<lopted H<..",dthy Da:ys as their meLric for Lhe measure­
ment of progre.-s Loward Bold Goal .'
Comributor.;: Tristan Gmtlietr. ~tPH; Gil Haugh, MS, _jormthan Pefi:1 , M ; Eriv Havens, M ; Vipin
Gopal. Ph D.
' H:iveru;, E., Pefi:1. j.,. l:ihaugh. S., Cornler, T.• Rend:1, A., & Gopa.1, V. ( 2015, October). Exploring
the relati.o nsh ip betw·een he:illh-rebted quality o f life :in.cl he:ilth concUlion.~, cos!! , resource uti­
liLition, and qu:1li1y m~1sures. Podium pre.sent:ition :it the ISOQOl 22nd Annual Confcn:nce,
V:incou\•e.-, Can:icb .
' H:iveru;, E•• . !ab.augh. L , Peii:1 J.• H:iugh G ., & Gop:il. V. 2.0 15. Februa ry). Are 1here differences in
l-11:."'.1.lthy lli)'S b:ised on compli:ince to pre--1<·enti\•e health screen ing me.asure.<;2 Poster p resentation
:it PrC'\,entive .\tedicine 2015, Atl:int.a. GA.
}Chiguturi, V.• Guthikoncb , K., Slabaugh, .• Ha\,en. , E.• Peii:1. J . & Cordi.er, T. ( 2015, june).
Rekttion.ship l,el"•·e-en d i.abctc:s complication.s :ind he.alth related qua lity of life among an elderl '
popul:ition in the nited Sl!Jles. Poste r presentation a t the Americ:m Diahctes As.';oc:i.ation 5111
Annual Scientif"ic Se. sion.s. Bo.~lon, ~tA.
"Cordier, T. , Slabaugh, L , H::mgh, G ., Gop:il, V .• Cus.ano, D. , Andre\\ . • G. , & Renda. A.
(2.01), eptember). Qualil)' of l ife clunges w ith pmgi-e...sing congestive he:i rt failme. Poster
pr-esent:ition :it the 191'1 Annual Scienl ifk Mi=cling of lhe l-leart Failure Society of Americ:i,
Wa.sh ington. DC.
' h1tp-J/popubtionhe.alth.hum:1m.mm/wp-content/uplb::icb 20 16/05/BolclGool.20 16Pmgre.'>SR.eport_ 1.pcf.
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32
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
Example 3 : Pl'edictive Mode ls to Identify the Highes-t Risk
Membership i n a H e.a.Ith ID~-urer
The 80/ 20 rule genernlly applies in heallhcare. 1.h.at b, roughly 20% of consum­
ers a<.:count for 80'% of heald1care resources due lo thdr <leteriornLing heal ll1
and chronic condi Lion.~. HealLh insurers like Humana have typically enrolled
Lhe highesL-risk enrollees in clinical and <l:iS<.."'<1SC managemenl progrnms lo hdp
manage lhe chroni.c con<l.i1.ions the members have.
[denLifkaLion of the right members .L.~ critical for thb exerc.ise , and
in the recent yeJrs, PMs have been developed to i<lenli y enrolk.-e.s with Lhe
high folure risk. Many of iliese P\1s were develop ed v,rith heavy reliance on
medical claim.~ daw., which result-. from Lhe medical services that the enrolk-e.s
use. Because of Ll1e lag Lhat exisLs in submilling an<l proce ·sing claim.~ data.
Lhere is a com:sponding lag in i<lenli ica Lion of high-risk members for clini­
cal program enmllmenL Thb bsue is especially relevant when new memher.s
join a heallh in.~urer. a.~ [hey would no[ have a claims hbtory with an insurer.
A claim.~-base<l PM could Lake on average of 9-12 rmmth.~ afrer enrollmen[ of
new members Lo idenLif'y them for referral to clinical programs.
ln Lhe early pan of Lhis decade, Humana aurncted large numbers of new
memher.s in ics Medicare Advantage product-. and needed a bener way lO clini­
cally manage Lhis membership . As such, i[ became extremely important thal
a different analytic apprrnach be developed Lo rnpidly and acrnrntdy ic.lenlify
high-rbk new members for clinical managemenl, lo k<..-ep this grou p healLhy
and cosL~ <lown .
Humana 's Clinical Analy1.ic.s Learn devdope<l lhe Nc'>lv Member PredicLivc
Model ( MPM) ha[ wou ld 4ukkly identify at-rbk indivi c.luab soon after
Lhdr ne\v p lan enrollmems \Vi Lh Humana, ralher limn wailing for suf.1dent claim hi.-tory lo become available for compiling clinical profiles and
prec.licl ing future health ri.sk . Designed lo address Lhe unique challenges
assod:a Lec.l with ne\V members. MPM de.,•dope<l a novel approach that
leverage d and inLegra[ed broader darn sets beyond me<lkal claims data
.such a.~ self- reported. he<1llh ri.sk a.~.se.s.smcnl data and early indi.rntor.s from
pharmacy <la. La , employed advanced <la. La mining techniques for pall.em
discovery, and scored every MA consumer daily based on Lhe mosL recent
daLa H umana ha.s to dale. The moc.ld ,...-as deployed \vilh a cro..~.s-fonctional
Learn of analytics, IT, and operations lO ensure seamless oper.i!lional and
bu.sine.ss integrati.on.
Ever since MPM wa.~ implemented in January 2013 , it has h<..-en rap idly
idemifying high-risk new members for enrollment in Humana ': clinical pro­
gram.~ . 11-ie posilive cmll.umes achieved through [hi: modd have heen high­
lighLed in mulliple .senior k."<1der commun icaLions from Humana. In ilie fir.st
quarLer 2013 e-.1.rnings rele-.ise pre.senw.Lion Lo inve.stor.s , Bruo: Brou ·sard , CEO
of Humana , sw.ted ilie .s ignificance of "improvement in new member PMs and
clinical assessmen l processes," ,;vhich resulted in 31,000 new members enrolled
in clinical programs, compared lo ,000 in Lhe same period a ye,1r earlier, a
67 - % incre<1se. In a<l<lition lo the incn:a.~ed volume of clinical program enroll­
menl.~, ou Lcome .studies .showed U1al ilie newly enrolled consumers idcnlified
by MPM were also rdem:d Lo clinical programs sooner, with over -oo/4 of the
referrals identified within the first 3 monlhs after new MA plan enrollmenls.
The consumers identified abo participated al a higher r.l.te and had longer
Lenu re i.n the program.~.
Comril~ulo rs: Sand7• Chiu, MS; Vi p in Gopal, PhD.
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ience
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111.cse examples illu s[mle hmv a.n organization explores anc.l implcmenrs analytics
applkai.lon.,; [o meet its SL:rategk g<.Y.l.b. Yem will sec several other example.- of heah..hcan.:
applka Lion.,; Lh:roughou r various chapLcrS in [he book.
Analytics in the RetailValue Chain
The retail scum is v.11cn:- you would perhaps see the most appliearion.,; of analydc.,;_ n 1is is d1e
domain •.vhcn: the volumes an: large but the margin.-; are usually thin.. Cus1.omers' La..'Sl<..'S and
pn.{en:n<.'L'S change fn:qucndy. Physical an<l online .ston.'S face many c.-halknge.'> in suc."t--eed­
ing. And marker dominance a l one time dcx.-s not b'l..ta.ntnLel'. conLinucc.l suc.u~ss. Su inve.sLing
in learning about your supplil'.rs. cui.tom<..Th, empk>)''L'eS and all the sLakeholders t.har enable a
reLail v.ilue chain lo sucn.-ed and u.-Ing Lhal information Lo make bctlt:r decisions ha.,; lx:cn a
goal of the analytics in<lu..,;try for a long Lime. Even Qlsual reac.lcrs of analytics probably know
alxm L Amazon"s enorrnotL'> lnvesuncnts in analytics lo power thdr value cha.in. Similarly,
\'i1<1lmarl. Tafb.rcl, an<l oLhl'.r major rerailers have invest:ed millions of dollars in analytic· for Lhdr
supply chains. Most of d1t.· analytics technology and scrvk.-e providers have a major pn:scnce
in retail analytics. Covernge of even a. small portion of those applicadom Lo ad1ieve our expo­
sun: goal could fill a •whole bcxJk So this sec.tion Just highlighL'> a ft:-·\v putenLi:al applical.ions.
Moi.t of thc.-sc havl'. lx.-en fid<ll'.d by many rc.1.ailL"rs and are :,ivailable Lhmugh many technology
pmvic.lcrs, so in d1is secdon we will take a ITllm.: general vic\v raLhcr than poinL to specific
cases. l11is genernl view ha.,; lx:en propos<..-<l by Abhishek Rachi. CEO of vCreaTek.c.·om. vCn.....
aTek, LLC is a boutique analyt.ic.,; so!hvare and service company that has offi<..'L'S in India , Lhe
Unil<..-c.l Stal<..-s. Lhc niLed Arab Emirnle.'S (UAE), a.nc.l Belgium. The company develops applka­
rion.,; in multiple c.lomaim, but n.1.a:il analyLics L'S o ne of their key focus an:-.d.~.
Figure 1.12 highlights sdecte<l componenL'S of a retail value chain. It slan.,; with
suppliers an<l nmdudes wiLh customers, but i.llustrates many in[crme<l iale .strate­
gic and opcrnl.ional planning decbion poini:.~ where analytic· descriptive. prcc.lktive.
or prescriptive-am play a role in making heller <la.ta-driven decisions. Table 1.1 abo
illusu·.ues some of Lhe imporLanl areas of analyLic.,; applications, examples of key que.­
Lion.,; [hat can be answered through analy[ics, a.nc.l of course , the potl'.nLial business value
derivcc.l from fide.ling such analyLic.~ . Some examples a.re <l:iscu.,;st.-<l next.
Critlcal n eeds at
• Shelf-space
optimization
• Location analysis
,, Shelf and lloa
plannr,g
,, Promotions
end markdoM'I
• Supply chan
management
• Inventory cast
aptimaati□n
• Inventory shortage
and e,rees.s
managernent
• L.es.s unwanted cost<B
FIGURE I . 11
Example of
Rl!l:lsill 'Vlllue Chain
touch pant of tt.e Retail Value Chain
~
• Trend anatys.,s
,, Category
management
,, Predicting
trigger events
far sales
•Bettierftrecasllsi
af demand
• Targeted promati□ ns
• Customized ll'IVl!nmf'Y
• Pra111111Jcns and
ince qrtnizatlcf1
• Deliver seamless
experience
• LJnder!ltand
relatNE performance
of dlannels
• Dpoo1i21e 11>3
stJ'at:egie:s
product
aYailability at low
., [ln.,IMJe
Cost-5
• Customized ~ i n g
experience
., Oniel' fulfillrnefllt
and dullbr,g
• Reduced
transportatioo
• BuMng r etention
and satisfaction
• Understanding
tt.e needs af the
rustcrner better
• Sel"'1ing high LTV
rustcrners better
Cost<B
Anatyoo Appl'ications i n a Retail
Value Oiain. Con.tributed
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by AbhiiShek Rat!h~ CEO. vCraaTek.com
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3-4
Chapter l
• An Ove
iev,• of Busines...~ Intelligence, Ana lyti s, a nd Data Science
TABLE 1.1
Exatnpies of Analytics Applications 1n the Retail Value Chain
Analytic
Appilc:atlM
Business Question
Business Value
Inventory
I. Whic:h produru haw! high
I. Fo=t tl'le consumption of fast-moving
products and order them with StJ fficient inveritory
Optimiz.ation
demand?
2 Whic:h produru ~ $low
movi"lg ot boo:iming obsolete?
ta avoid a stod:-out scenario.
2 PetfOf"m fast inventory tumover of slow-mOW'lg
products bf combining them with ooe in high
~mand.
Price Elastioty
I. How m.uc:h nct margin do I have
on the pmduct!
2 How m.uc:h dstol61t
cari
I give
I. What prod.Jets !ihould I combine
Analysis
to Ct'eate a bundle offer-?
2 Should I combine products
based an slow-moving and fast­
moving characteristics?
3. Should I create a
ndle frcm
the same category or different
category line?
Shopper Insight
2 Optimized price for the bundle of pmducts is
~ntified to save the margin dollat.
on this prad.uct1
Market Basket
I. Matk.down prices for each prod.Jct can ~
optimized to reduce the margin dollar !OS$.
I . \!Vhich customer- is buying what
pmduct at what location?
I. The affinity anal~s identifies the hidden
c:on-elatioM between the products, which cari
help in followirig vah.Jes:
a) Sttategize the product bund~ offering based
on focus on inventc:ry ot margin.
b) Increase arass-se.11 or up-sell by ~ating
bundle fr"om different categories or the same
categOf"ie,s. resr-ctivcly.
I. By oustamer segme11tatian, the busint!:SS owner
can create personal ired offers reStJlting in better
cl.l!/tomt'.!t apcrience and
Customer
I . Who are the oustamers who
will not retum?
ChumAnal~~
2 How muc:h business will I Jose?
3. How can I retain thetn?
4. What demogr-aphy of customei"
is my loyal customer?
retenfun of tlie customei:
I. Busirie:;ses can identify the customer and product
relatianS;hips that are not wor'king and show high
chum. ThLJ!. can h.ue better Foous on pmduct
quality al'ld reason for that chum.
2 Based on the customei" lifetime value (LTV), the
business can do tar'geted ~ting reStJlting in
retention of the oustamer.
Channel Analysis
I. Whic:h channel has lowet
customei" ~isition cost?
I. MaMting budget can be optimized based on
insight for bettf!I" tctum on investment.
2 Whic:h channel has bettei"
customei" retention?
3. vVhich channel is more profitable?
New Store
I. What location S;houfd I op ?
Analysis
2 . What and how much opening
inventory ~ould I ke:ep?
I. Best practices of other locations and channels can
be u:sed ta get a jump start.
2 Comparison with compcliw dru can help to
create a dil'l"etcntiatot/USP factot to attract the
new custorr.ers.
St.ore
Layout
I. How should I do stote layout
for bettet topline/
2 How can I inc= my in-st.ore
customei" experience?
I. Understand the association of products to decide
statt layout and
tter alignment with oustamer
needS;.
2 \Abricfor-ce deployment can be planned for
bettf!I" customei" intcractr.•ity and thus sati,sfying
customet experien~e.
I. What demography is entering the
stare during the peal<: period
of sales1
2
How can I identify a oustamer
with high LlV at the st.ore
I. lr\-store pr-omotioM and events can be plarined
based on the d=graphy of incoming tratf,c.
2 Targeted customei" engagement and instant
discount enhal'IU!s tl'le customer experience
resulting in h ighet retention.
entrance so that a better
personalized experience can be
provided to this oustamer?
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An o nline reLail she usually knows iLs c ustomer a.~ soon as the cus Lomer signs
in , and Lhus Lhey can offer cuslom ized pages/ offerings Lo enhance lhe experience. For
an y rela il store, know ing ils cu stomer a l lhe slore entrance is sLill a hug e d1allenge.
By combining lhe vi deo an alytics and information/badge issu ed throu gh thdr loyal ly
program. the More m ay be able Lo idemify Lhe customer at the entrance ilsdf a.nd
Lhus en.able an exLra opporLuniLy fur a cross-sell or up-sell . Moreover, a personalized
shop ping experience can be provi ded wilh more customi.zed engagemenL d uring Lhe
customer's lime in the store .
. tore retailers invesL lots of money in aurnclivc window d isplays, promotional e\<cnls.
cuMomized graphics, swre decornLions, primed ad~, and banners. To d iscern Lhe effective­
ness of these markeLing melh olb, Lhe team can use shoppe r analyLics by observing dosed­
circuit tdevbion (CCIV) images to figure oul the demogr.i.phic deLails of the in -SLo re fooL
rr.iffic Th e CCTV images can be analyzed using advanced algorilhms to derive demo­
graphic detalb such as age. gender, and mood of lhe person brows ing through the SLore.
FurLher, Lhe cu sLomer's in-slon: movement data when combined with shelf layouL
and planogram can g ive more insight lo Lhe slore manager Lo idemify lhe hoL-selling/
profitable a.reas \vithin lhe store. Moreover, the swrc man ager can u.~e thb informalion lO
abo plan the wurkforcc alloca lion for 1.hosc area.~ for peak pe riods.
Markel basket analysis ha.~ com monly been u sed by the ca Legor y manager · Lo
push the sak of Lhc slowly moving KUs . By using advancc:d analytics of d aLa avail­
able, thc p roduct affini Ly can be d o ne at the lowe st lcvd of KU Lo drive beucr ROis
on the bundle offers. Moreover, by using price da.~ Lici Ly tcchniques , lhc markdown o r
opLimum price of Lh e bu ndle offer can also e dedu ced , thus reducing any lo ·s .in [he
profit marg in.
11ms by using data analytics, a retailer can no[ only g c t in fo rmatio n on iLs current
oper.i.tions bul can abo gct furthe r insighL lo increase thc reven ue and dee.Tease Lhe
o p ernlional cosl for h.igher pmfiL A fa irly comprehensive lisL of currcnL and potential
retail a nalytics applica lions that a maior relailer such a.~ Amazon could use is proposed
by a b logger al D:ata ciencc CentrnL 11rn[ lisl b available al hup://www.d.atasdcnceccn­
L.ra.l.com/ profi Ies/ blog.· 20-da La-sciencc:-sysLcms-use<l-by-amazon-Lo-oper.t.tc:tils-businc:.·s .
•1\5 noLcd earlier, thc rc are loo many cxamples of Lhcse o pportuniLics Lo lisLhere , bul you
\\rill sec many examplcs of s uch applica tions throu ghou t Lhe book.
SECTION 1.6 REVIEW QUESTIONS
1 . 'thy would a hcalll1 insurance company invest in analytics ocyond fraud detection?
\Vhy is it in Lhcir bcsL interesl [o pn.-dic.t the likelihood of falls by patients?
2 . \VhaL other applica tion.~ s imil.ar lo predicLiun of falls can you envision?
3-. How would you conv ince: a ne,v h eallh in ·u rnnce cusLOmcr
Lo a dopt healLh icr life­
styles (Humana Example 3)?
4 . Identify at leas[ Lhrce other opportunities for applying ana.lylics in Lhe rcrnil ,,,..due
cha.in bcyond tho.s c covered in this sec.t .ion .
5. \Vhich retail stores [haL you know of employ some of the analytics applicalions identi­
fied in thb section.?
■
Iii
A Brief Introduction to Big Data Analytics
What Is Big Data.?
Any book on analytic.~ an<l d al.l. science has lo include significant covernge of whaL is
called B ig Data analytics. \'X'c "viii cover il in Chaptcr 7 but here is a very brief introdu c­
Lion. O ur bmins work exLrcmdy q uickly a.nd efficiently a nd arc versatile in proccss ing
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Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
.large amount.~ of all kin<ls of &1ta: images. text, .soun<ls, smelts, and video. \Ve process all
c.liff<.:renl forms uf cl.l.ta relatively easily. Computers , on the othcr ha.n<l , arc sLill finding il
harcJ to keep up wilh the p:-,1.cc al whkh <.Lua is gcncmled, kl a lone analyze it fast. This
b \vhy , c have the problem uf Big Dar.a . So, \vhat is Big Dar.a? imply put, Big D-,1.ta is
data t.haL cannot be stored in a .single .stomgc uniL Big Data typkaUy refers lo daLa thal
c.·omc.s in many <l.iffercn Lforms: sln1crnrcd. unstrucLured, in a strc-.im. and .so forlh. Major
sources of .such <la.ta arc dkkstn:arn.~ from '1:'cb .site.~, posLings on social media sites such
as Faccbook. and <la.ta from traffic, scmor.s , or wca Lher. A \1;'cb .search engine like Google
need~ lo sea.rch and in<lcx billions of ~ ch rabrcs Lo give you relevant sca.rd1 results in a
frac.1ion of a .second. AlLhough this is not <lone in real time. genera ling an index of all the
Web pages on the Inll:mcl is not an easy Lask. Lu<.:kily for Google, it was able Lo solve Lhis
problem. Among other lOols, il has employed Big Data analytical techniques.
There arc two aspcc.1.s lO managing <lar.a on Lhis scale: sLoring and processing. If we
c.x.)llld purcha.~c a.n cxt:rcmely cxpcn.~ivc slornge solulion to store all this a l one place on
one uni L, making this unit fault Lolcranl woul<l involve a major expense. An ingenious
solution was proposed thaL involved SLoring Lhb data in chunb on differcnL mad1incs
nmncc.1.ed by a ncr,,vork-puning a <.:opy or lwo of this chunk in <liffcrenl locations on
the neL'\vork, both logically and physkally. IL was originally use<l al Google (then <.:alle<l
the Google File System) and laLcr <lcvclope<l and released as an Apache projccl as the
Hadoop Distributed File. y.stcm (HDF ).
Hov.-cvcr. SLoring thi.· data is only half the prohkm. Data is wurthlcss if il docs noL
provide husincss value, an<l for it lo prcwi<lc business \~d.lue , it h;,i.~ lo be analyzed. How c:m
such va.~t amount.~ of data he analyzed? Pa.~sing all <.:omputadon to one rx.l\verful <computer
does not work; th.is scale would creaLc a huge ovcrhc-<1d on such a po'l;\rerful compuler.
Another ingen ious solution \Yas proposed: Push <.:omputaLion Lo Lhe <laLa. in.~tc-,1.<l of pu.-hing
data to a <.:omputing node. Thi~ \\rd..s a. nt."\V parn<ligm and gave rise lo a "'rholc nc\v way of
processing data. This is what we know today as Ll1c MapReducc programming par.i<ligm ,
·which made pm<:essing Big Data a re,1.lity. MapReducc- \\rd.<; originally developed at Google ,
a..n<l a. su sequenL version was rdcasc<l by lhe Apa.die pro~<.:t called Hadoop MapRc<luu:.
Today, when \\'t: talk about .st01ing, processing, or analyzing Big Data. HDFS and
!v1apReduce an: involvt"<-1 al some level. Other rdcva.nL .standards and .software soluliuns
ha\'C been proposed. AlLhough Lhc major toolkit is av,1ilahlc as an open sourc.x.:, sevcrJ.I com­
panies have been laund1cd lo provide lrnining or .spcdalized ana.lylica.l hardware or suft­
w-arc scrvkcs in th.is space. Some e.x:amplc.~ arc Horton~ orks. Cloudera. an<l Teradata Ast.er.
Over the past few years, whal was called Big Dala changed more an<l more as Big Data
a.pplicaLions appeJ.re<l. 111e nt·e<l lo pm<.: ss <laLa coming in a.La rnpi<l rnLe a.de.loo vdoc.:ity to
the equation. An example of fasL<la.ta processing b alb•urithmi<.: trading. TI1is uses elec.1mnic
platforms b-.t..~t."<-1 on a.lgoriLhms for trading shares on the finan<:ial mark(:[, whkh operates in
miuosecon<ls. The net-<l to process diffcrenL kind.~ of <la.ta ad<led variety lo the equation.
Another example of a \Vi<lc variety of data L~ senLimc::nl analy.sL-;, which u..~cs various forms
of <lata from .scx:ial media platforms and <.:ustomcr respon~cs to ga.ub>c senLirncnL~- Today, Big
D.Ha is as.scx:iawd with a.Imo.st any kind of large data thal has Lhe <.:ha.rncteristics of volume,
vdocity. and ,~,1.rlcty. Applica.lion Case ] .6 illu.str.tK'S an application of Big DaLa analytics in the
energy in<l.u.~try. We will ."lu<ly Big D.ita tedmologic.~ a.n<l a.pplica.dons in Chapter 7.
SECTION I . 7 REVIIEW QUESTIONS
l . \'V'hat is Big Data a.nalyli<.:s?
2 . What arc Lhe .sourc.x.:.s of Big Data.?
3 . \'V'hat arc Lhe diarncleri.stics of Big Data?
4. \'V'hat processing Ledmique is applied to process Big Data?
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1 • An Overview o f Bu. ·nes. Imelligeo , oalytics, and Data
ience
37·
CenterPoint Energy Uses Real-Time Big Data Analytics to Improve Customer Service
CcnterPoinL Energy i.· a FcJrJune ~oo energy c..ldivcry
company base<l in Hm.1sltm , Texas_ IL-; primary bu.-i­
ncss indu<les e lcc.trk lran.-;mi ·s ion anc..l <l is uibulion,
naLurnl gas c..lbuibution, an<l nauurnl gas sales and
service_ ll has over 1vc million meten.x.l cu.,;tomers in
lhc Unilec..l lates_
CenterPoint Energy u.-;es sma11 gri<ls to col­
le<.:L real-time informa Lion abou t Lhe health of vari­
ou.,; a.,;pec:t.,; of Lhe gri<l li kc meters, transformers, and
switches tha t arc 11.1sc<l in pmvic..ling decLriciLy_ This
real-time power usage information is analy:ze<l -r.vilh
Big Data analytics and allmvs for a m11.1ch quicker
c..liab'Tlosis an<l solu tion _ For example , the c..lata can
predict and p otenlially hdp prevent a power outage .
In a<lc..li tion. lhc l<.x>l collecL-; weather informa­
l.ion allowing hbtorical c.lala lo help predict. lhc mag­
ni Luc..le of an outage from a sLorm_ This insight will
act as a gui<le for pu tting the right resources ou L
before a storm <.x.:curs lo avoic..l an outage_
Second, lo b<_1ter unc..lerstan<l lhdr customers,
Ccn LerPoinL Energy utilizes sentiment analysis, which
exam ines a cu.,;tomer's opinion by way of crnoLion
(happine ·s, anger, sadness. cLc)_ The company sq,1mcms Lheir cu.,;tomers b-.tsc<l o n the scnLimcnl an<l is
able lO market lO these groups in a more pcrsonal­
ized way, providing a more valuable customer scr­
vkc experience_
As a re.-11.1h of 11.1sing Big 0,,1w analytic.-; ,
CcnLcrPo inL Energy ha.,; saved 600.000 gallons of
MI=•
fuel in the lasl 2 years by resolving sLx million ser­
vice rc-quesL-; remotely. In ac..ldilion , they have saved
2 million for their cu.- Lomcrs in this process.
QUESTIONS FOR DISCUSSION
L How can decuic companies prc<lk1 a po..-;sibk
outage at a local ion?
2_ \"X' hat is customer sentiment analysis?
3 - Hmv docs customer sentiment analysis help
companies provide a personalized service lo
their cuMomers?
What We Can Learn from This
Application Case
'\l iLh the use of Big Daw. analyLic.s, energy com1Y.tnies
can bctLcr solve cu.,;tomer issues like 011.1tages and
ek·cuic faults within a shorter span of lime cornp-.1rcd
lO Lhe earlier process. Abo senlimcn t analysis can
hdp lar.1;,ret Lhcir customers accorc..ling lo lhdr neec..ls_
Soun:E's: 3p.com, "A , mart' Approo.ch lo Big D::Jt3 in the Energr
Industry,- http://"-w"'- sap.con:Vhin/s.apcom/cs_cz,ldownload3. set
_20 13- I 0-oct-09-10 .a-sm3 rt-appro3ch-lo-big-d.at::i-in-th e-enugr­
indus1ry-pdf.h1m I (acces.';ed June 2016); cenlerpoinlenergy
_com, -Electric Trnnsmi. sion & Distribution (T&D).- hnp://v.•ww
_ce nterpoi ntenug1·-com/en-w;/Corp/ Pages./Comp3 nr-o\•eniew
_::i..~px (::icce. sed June 2016); YouTuhe.com. '"CenterPoint Energy
T::ilks Real Time Big 03La. An,aJrtics,- htlps://ww,v-}'OUtuhe_com./
w:ildi'\=7CzeSIIEfl (acce.-.,;cdjune 2016)_
An Overview of the Analytics Ecosystem
So you are excited abou1 Lhe potential o , analyLic.,; a n<l ,;vam Lo ioin this growing in<lu.- Lry_
Who arc the current players. and what to <lo they <lo~ \Vhe re m ight you flt in? The objec­
d vc of thL,; sec:tion is to identify variou.,; senors of Lhe analytics in<l11.1stry, provide a da.-;si­
ficaLion of <l.ifferenL Lyp es of in<lu ·[ry p:articip:anlS. an<l illust.rale the types of opporl11.1nlLies
Lhal exis t for a na lytks prnfc.ssionals_ Eleven differcnc typ es of players arc identified in an
anal)'tics eoosystem. An understanding of the ecosystem also gives the reac..ler a bmac..lcr
view of h ow Lhe various players c·omc together. A secondary purpo.s c of 11.1nderslanc..l lng
the analyti.c s ecosystem fo r the BI professional b also lo be aware of organiza Lions and
new offeringi,; and o pportunities in sec.1ors allied with analytic.-; . Th e section cond11.1<les
,;vith some obscrv.uions about the oppon11.1ni Lies for prok ·s ionab Lo move aero ·s Lhese
d11.1stcrs_
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38
Chapter l
• An Ove iev,• of Busines...~ Intelligence, Ana lyti s, a nd Data Science
Data
Gen!!'raoion
Infrastructure
Regulators a nd
Policy Makers
Data
Management
lnfrasoructure
Providers
Data
Warehouse
P.roviders
Application
□ BVel opers :
Industry Specific
or General
Academic
Institutions an d
C@rtificaliion
Agencies
Middl@wara
Providers
Data Service
Providers
fl,GU,RE l . ll
Analytics Ecosystem.
A.lLhough some researchers hm•c <l.is Linguishe<l busine.-s arutly1ks prok ·siunab from
data scientists (D-,1vcnpurt and Palil , 2012), as pointed out previously. fur the purpose of
un<lersLand ing the overall analylics e<.:o ·ys1em , we Lredt them a.,; one bma<l p mfe.,,;siun.
Clea rly, skill needs can vary between a slm ng m athematician lo a programmer Lu a mod­
der lO a communicator. anc..l we believe this issu e is resolved al a more micro/ individual
lcvd rather Lhan al a macro level of understanding the o ppor1lunily pool . \Ve also Lake Lhe
\Videst definition of analytics lO include all three type s a.,; ddlnc<l by I FOR\1. -<lcscrip­
live/ reporting/vbualization, pnx.l.icth•c , an<l prcscripciw as described earlier.
Figure I.13 illustrates one view of the analylics cco.s yslcm. 111e componen ls of Lhe
ecosystem arc represented by Lhe petals of an analytic.,,; flower. Eleven key sectors or dus­
lers in the analytics space arc i<lenli 1ec..l. The componcn Ls of the analytics L-cosystem are
grouped imo threr.: ca Legorics rcpresemed by the inner pL'Lals, o u ter petals, and Lhe scc<l
(mid<l.lr.: part) of chc tfowr.:r.
The outer six petab can he bmac..lly LcrrnL-<l as Lhe lr.:chnolo.1:,•y pmvic..lers . Their p ri­
mary revenue comes from p rovid ing technology. solu Lion.'S , and training lo analytics u ser
organiza Lion.,,; so I.hey can employ Lhesr.: lechnologir.:s in Lhe mosL dkctivc and efficient
manner. The inner pL'Lals can he gcnernlly ddim:d as the analylics accderators . The accd­
emtors work wil.h bmh technology pnwidcrs and u.,,;crs. Hnally, Lhc core oft.he ecosyMem
comprises [he analyLic.~ user organizations. This is the moM importanL l.-omponent, a.~
every analydcs in<luscry d u ster is driven by the uscT organizalions.
The metaphor of a flower is wdl-suitLx.l for the analydc.'> ecosystem as mulciplc com­
ponenL-; overlap r.:ach other. Similar to a living organism like a fl mver. all these pL'Lals grrn,v
and v.rither Logecher. '\! c use t.he Lerms cvmf.Kments, clusters, petal', and s,1cl01:~ interchange­
ably to describe the various playr.:rs in the analytics space. We immd ucc each of the ind usLry
scclors next and g ive some r.:xamples of players in each sector. The lisl of company mt.me.~
indudr.:d in any petal is not exhaustive. 111c repn:..'SCnlallve lisLof companir.:s in r.:ach du.'Slcr
is just lO illu stralr.: that dusLer"s unique offering lo dcscribr.: \Vherc analytics talent may be
u.,,;r.:d or hin:..x.l aw-ay. Also, mr.:nLion of a o:)mpany's name or iL'S capability in one spe<.:illc
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1 • An Overview of Bu. ·nes. Imelligeo
, oalytics, and Data
ience
39
gnmp c..loe.s not imply that it b the only activiLy/ offering of LhaL org:miwtion . The main goal
is Lo fcx.:u.~ on the difl"'ercnt analytic <.:apabilities within each compcmem of the analytics
.span:. Many companies play in multiple .sa:tors '1-ViLhin the anal 1,Lics induslry anc..l llms ofter
opponunitks for movement within the fide.I both horizuntally anc..l vc:rt:kally.
Mau Turd<, a venture: capitalist wiLh irsLMark has abo devdop;:..-<l anc..l upc..lat.es an
anal~•Lics ecosysLem ftx.:use<l on Big Dala. HL~ goal is lo keep track of new and established
players in various .sc:gmenL~ of the Big Data inc..lu.~try. A very nice visual image of his inler­
prer.alion of the ecosys[em an<l a comprehensive lisLing of cornpr..mic:.~ is available: through
hb Web site:: hLlp://mamt1rck.rnrn/ 2016/ 02/ 01/big-c..lalli-lambcapc:/ (ao..:c:sscd August 2016).
\Ve will also .sec: a .simHar eco.-ysLem in the context of the Internet of Things (IoT) in the
laM chapLer.
Data Generation lnfr.astructure Providers
Perhaps the fir.st place: [o begin idcn Lifyi.ng the: d usLers is by noting a new •roup of corn­
panics that enable gc:ncrnli.ng and collection of data thaL may be: u.sc:d for developing ana­
lytical in.~ighL'>. Although Lhls group could include all [he [raditional point-of-sale .sysLcm.- ,
inventory management systems, anc..l technology prov idcrS for every sLep in a comp,my's
.supply/ value: chain and operations, \Ve: mainly consider nc:\v players ,vhc:rc Lhe primary
focus has been on enabling an organiza Lion [o c..lc:vdop new insiglns inlo iL'> upc-r.itions as
opposed to running iL.~ core operations. Thus this group i.nduc..lc:s companies <.:rC'<1ling the:
infra.~trncture for collecting data from c..liffc:rcn Lsources.
One of the emerging components of .such an infmstruclure b the: "sc:n.~or.~ ensors
collect a ma.~sivc: amount of data al a fasLer rate a.nc..l have been a<loptc:d by va.rious senors
.such as healthcare . .sporl~. and energy. For example, hC'<1hh c..laLa collc:c.1ec..l by [he sensors
is generally used Lo Lr.tck the health stall! · of the uscrS. Some of the major players manu­
facturing semor.s Lo collect heal Lh inforrnaLion are AliveCor, Google. Shimmer, and fitbiL.
Like\vise, Lhe .sports inc..lus.t11' is using .sensors Lo collect c..laLa from the players anc..l field to
develop .slra Lcgies and improve Le-.tm play. Ex:amplc.s of the companies producing .sporLS­
relatec..l sensors induc..lc: pons Sensors, Zepp, hcx.:kbox , and oLhers. . imilar.ly, .s n.~ors are
usc:d for 1.rnffic managcmc:nL These help in taking rC'<1l-lime actions lo umtml traffic. Some
of Lhc providers arc AdV'.intech B+B mart\-Xforx, Garmin, and en.~ys etwork.
Sensors play a major mle in the InLcmel of TI1ings and arc an e ·sential pan of smart
objects. TI1c:se make machine-to-machine communication po.sible. The: le.iding players in
Lhe i.nfr.istruc.1urc of ToT arc Tmd, Micro.s oft , Google:, IBM. Cisco. Smanbi.n , SlKO Produces,
Omega Engineering. Apple, and . AP. Thb cluster is probably the most Lechnical group
in Lhe ec_,:isy.- Lem. We will review an ecosystem for IoT in Chapu..:r 8. Tnc..lec:d, there i.- an
cco.sysLcm around vinually C'.tch of the dusLcrs we identify here.
Data Management Infrastructure Providers
Thb group indu<les aJI of the: major organization.- that provide harc..lwarc anc..l .sofl'l-vare lar­
gcting the basic founc..ladon for all <lata management soludon'>. Obvious examples of these
include all maj~)r harc..l\\'<trc players that provide: the infras.tructmc for database compuling­
lBM. Ddl , HP, Oracle, and so on ; sLorngc solution providers like: EMC (recently bought by
Dell) and eLApp; companies providing indigenous harc..l\varc and software plaLforms .such
as TB!\·1, Orndc:, anc..l Teradata; and c..law .solution providers offering hardware: and platfr>rm
in<lepenc..lem database managemem .sy.slc:ms like the QL Server family of Microsoft and
.spedaliz.c:d inLegrmed software providers su ch a.~ SAP fall under thb group. This group abo
indudcs other organ.izacions such a.~ database: appliance providers . .service pmvic..lc:rs, inLe -­
gmtor.s , devdoper.s , and so on, that support each of Lhese companie ·' ec_usysu..:m.~.
SL·-vc:ml other compan ie.~ are emerging as maior players in a rdatc:d spa.cc:, Lhanks
Lo the network infrastructure enabling cloud compu Ling . Companies .such a.~ Amazon
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Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
(Amazon \-X'eb ervice.s) , IBM (Bluemix). an<l Salesforce.rnm pioneered lo offer full <lata
sLurn.ge and analytics soluLion.s lhnmgh Lhe cloud, which now have been adopted by .se\'­
t:ral comp,mies lbtt:d t:arlier.
A n:cent crop of companies in the Big Data spact: a.re also part of Lhis group.
Companies .such as Clouder.i, Hononworks, an<l many others <lo noL rn:_x:essarily ufft:r
their own hardware hut provide infr<L~lructure .servkt:s and lmining lo cn:aLe Lhe Big Data
plat.form. Thb would indu<le Haduop du.stt:r.s , ~apRe<luce, o. QL, p-,1.rk, Kafka , Flume,
and other rdatt:d technologies fur analyLic.s. 11uL~ Lhey could abo be gmupt:d under
indus"Lry consulcanL<; or trainers enabling lhe bask infr.tslnlcturt:. Full <..x:osystems of con­
sultants, sofo,vare intt:gralors, Lr.tining provider.s, an<l other value-a<l<led providers have
t:volved around many of the .large playt:r.s in I.he data managemen Linfrastructure duster.
Some of Lhe du.~ter.s lisLe<l below will i<lenLify these players because many of Lhem are
moving lO analyLics as the in<lu.~try shifL.s its fu<:u.~ from efficienl Lr.insanion processing lo
dt:riving analytical v.t.lue from the <lata .
Data Warehouse Providers
Companies with a data vvarehousing focus provide technology an<l se.-viccs aimed toward
inLcgmLing <la.ca from nmlciple sources, thus enabling org-,mi.zalion.~ lO derive an<l <ldiver
value from it.~ <laLa a.-sets. Many companies in this sp-.t.ce indu<le Lhdr own hardw.t.re lo
provi<lt: dfident data .slora •e, retrieval , and processing. Companies such as IBM, Oracle,
an<l Ter.idata arc major playt:r.s in Lhis arena. Re<..-enl <levdopmenlS in this sp-.1cc indu<lc
performing analylics on the <la.ta <lirccdy in memory. AnoLher major grm:vLh senor has
been <lala '>lva.rehou.-ing in the dou<l. Examples of such companies include Snmvflake and
Red~hifL Cump-.1nk.~ in lhb dus.ter dearly ,.vork with all Lhe mher M.'Ctor pl-ayer.s in provid­
ing D~· .solulions an<l service.s wiLhin their t:cosy.stt:m and hen<..'<..' becomt: tht: backbone
of the analytics in<lu.~try. h has been a major industry in it:.~ own right and, t.hu.~ . a supplier
an<l con.~umer of analyLics LalenL.
Middleware Providers
Data vvan:housing began with a focus on bringing all lhe data sLore.s inlO an enlerprise­
widc pblform. Making seme of this <lata h:a.s becomt: an indusL.-y in itself. The general
goal of Lhe mi<ldleware industry is lO provide t:asy-Lo-u.~e lools for rcporling or descrip­
tive analytic..~ . which forms a core parl u BT or analytic..~ employt:d al organi.zation.~.
Example.s of companie.s in thb sp'"1ce indu<le Mic.TostraLegy, Plum, an<l many oLher.s . A
few of lhe major players ch.al ,.vere independent mic.l<lleware players have been acquired
by companie.s in the firs[ two group.-. for example, Hypt:riun bee.amt: a part of Oracle ,
AP acquirt:<l Bu.sint: ·s Obj,t:crn , and IBM acquired Cognos. This .-t:clor h:a.s been largely
synonymous with tl1e Bl providers ofkring <lashboarding, reporting, an<l vbualization
services lo the in<lustty. building on lop of Lhe lr.l.nsaction proct: ·sing <larn and Lhc
<latabast: an<l D\'7 providers. Tims many compan ies have moved into lhis spact: over
the years, including general analyLics software vendors such a.s . AS or new vbualiza­
tion provi<ler.s such as Tableau, or many niche applicaLion pmvidt:rs. A pm<luc:1 direc­
tory al TD\Xi'T.org li.sts 201 vt:ndor.s jusl in Lh is caLt:gory (hnp ://www.tdwi<lircctory.com/
caLegory/ bu.siness-inLclligence-.services) as of June 2016, so lhe seclor ha.~ been robust.
This b ckarly also the st:Clur auempting Lu move lO a more <l:ata scienct: segmt:nl of Lhc
indu.stry.
Data Service-Providers
Much of the data an organization uses for analytic..~ is gt:nerat<..-<l internally Lhmugh ilS opt:ra­
Lions, but chert: a rc many c:.xlcmal <lata sources Lhat play a major role in any organization's
c.ledsiun making. Examples uf such <lata sources indu<le <lt:mogrnphic daia, vve:ather d:aia ,
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ience
41
<lara L"ollet:Led by Lhlrd parties LhaL could inform an organizaLion's c.Jccisi.on making, and
.so on. ~'<.T .'i.1 companii:.~ reali:ze<l t.hi: opportunity Lo develop .spLx:iali:ze<l <lara collec..tion,
. on a .sp:...-cifi.c
a~rcga.Lion, a.n<l distribution mt..x:han.bms . These u)mpanies typically fo<."l.LS
industry sector and build on their cxbling rd,1donshipi. in Lhal indus.try through 1.hdr niche
platfonns and services for data collection . For cxampli:. idscn provide.~ dara soun..i.:s lo Lh<:ir
dienLs on customer rerail purcha~ing behavior. Another example is Expi:rian . whid1 includes
<laLa. on e<1d1 household in I.he Linite<l . lale.s . Omniluri: has developed technok>t,,y to c·olle<.:l ~ eh dicks and share .such data wilh thdr dienl<; . Cmmcore is anoLhL'r major company
in Lhi~ space. GooglL" compili:.~ dalli for individual Web siLes and rrn.ki:s a summary availabli: through Google Analytics services . Other examples arc Equifax, Tmns nion , Acxiom.
RI . org, which
Merkle, Epsilon, and AvenLion . Thi~ can abo indudi: orga.nizaliun.~ such a.~
provi<li:s lornlion-orienre<l <la.La. Lo their cuslOmers . Thi:re an: hundred~ of other u)mpanics
lhat an:- dl..'vdoping nkhe plaLforms and scrvkes lo collec..t, aggrL"galL', an<l share such data
wiLh lh<:ir d.ienK A.s noted earlier, many indu.stry-sp:...'Cific data a~n:c-ga.L.or.s and disuibulors
exbt and an: moving Lo offer their mvn analytics services. Thus this .SLX:lor is also a growing
user and potential supplil.T of analylics LalL"nl., especially \\rilh sp:...-cifi.c niche expL'rtiSC .
Analytics-Focused Software Developers
Companies in Lhb category have developed analytic.~ softwan: for general use ·\vil.h data
Lhat has been collected in a D\'X' or is available lhrough one of th<: p.laLfonns idenLified
earlkr (including Big D:ata) . IL can :also include invenlor.s and resi:archers in univi:r.sities
and other organizations lhaL have dL"velop:......J algorithms for SpL'cific types of analytics
applica lion.~. We can idL"ntify maior indu..slry p.layL"r.s in Lhb spacl..' using Lhe lhrL"L' types of
:analytics: dcscripLivi:, predictive, and pre.scripLive analyLics .
RiEPO:RTI NGl DESC·R I PTIViE ANALYTICS Reporting or descriplive analytics is enabled
by I.hi: tools availabk from the middleware industry players idenl.ified earlii:r, or uniqui:
cap-<1bililies offered by fornsed providL"r.s. For i:xampk, Micmsoft'.s QL L"rvcr BT Loolkit
includes n:porLing as well as pn:dicLivL" analytics capabilities. On the other hand . .special­
ized .software is available from companies such as Tableau for vbualization . A abo offers
a Visual AnalyLics Looi with similar capacity. There arc many open source vbuali.zaLion
toob a.s well.. LlLer.i.lly hu.ndn:d~ of daLa vbualiz.ttion tools have been dcvdop:...-<l around
lhL' \Vorld, and ma.ny such lools focus on vbuaJization of data from a spL"cific industry or
domain . Bl..-causi: visu:ali:zalion .is 1.hi: primary 'ivay thu.s far fo r exploring analylics in indu.~­
Lry, this SL-'Clor ha· witne.ssL-<l Lhe most growth . ~any new companies are being formL-<l .
For example, ephi , a free and opi:n source .software, focu.ses on visualizing nelW<>rks. A
GooglL" .sl..'arch will show lhL' !ale.st Ii.st of such sofrwarc providers and lools .
Perhaps the big rest recent grov.t.h in analyLics has been in
Lhb caLcgory, an<l there an: a largi: numbi:r of companiL"s that focu.~ on pre<.lktive analylics .
Many .statistical software companie.s such as . A. an<l PS embrace-cl pn:dictive analyL­
ic.s i:arly on, and dl..'vdoped sofLware cap-<1bil.itie.s as well as .industry prac"Lices lo employ
<la.ta mining techniques and classical .sta LisLical Ledmique.s for analytics. IBM- PS \1odder
from lB~ and EnLerprisl..' Miner from. AS arc some of the L"xamplc.s of loob usl..'d for pre­
<lktive analyLic.~ . OLher players in this space include KXE , lalsoft (n:c..i.:ntly acquin:d by
Dell), alford ystems, and scori:.~ of othi:r companies that may sell their sofLw·arc broadly
or use iL for their own consulting praclicl..'s (next group of companies) .
111ree opL"n .soun:L' platforms (R, RapidMiner, and K TME) have abo emergL"d
as popular indu.~trfal-slrenglh .software tools for predictive analytics and have compa­
nies that .support lrdining and implemenLalion of these open source Lools. RevoluLion
Analytics b an example of a compr.m y focused on R <levdopmcnl :and training . R intcgm­
Lion is possibk \viLh moM analytic.~ .softwan: . A company calkd Alteryx uses R ex[L"mions
PRE,D ICT1IVE A'NALYTil CS
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Chapter l
• An Ove iev,• of Busines...~ Intelligence, Ana lyti s, a nd Data Science
for reponing an<l pn:diclive analyLics, bUL ib s[rcnglh 1s m shared c.ldivery of analytic;
solutions processes Lo u1sLomers an<l o ther users. imilarly, Rapi<lJi.-Uner and K TME an:
also example.~ of orx:n souru: providers. Companies like RulcquesL Lhat sell p roprietary
variants of Decision Tn:e softwan: and euroDimensions. a em.,i.l etv.rork sofcwan.:
(_·om pany, arc examples of companies thal have dcvdopcc.l specialized sofc--.vare around a
srx:dfk Lechnique of d aLa min ing.
oftwan: providers in this category offer modeling cools
an<l aJgori lhrm for oplimizaLion of o perntions usually rnlled management sciencc/orx:rn­
tions rese arch soft\vare. 111is fide.I has had ir.~ own set of maior sofrware providers. lB\1,
for example , has da:sk linear and m ixe d integer progr.l.mming soft--.vare. Several ye,1rs
ago, IBJi.•1 ab o acq uin:d a company called TlOG , ,,vhich provides prescriptive aruilysi.- sofl­
"\/Vare and scrvkcs Lo complemen t their other offe rings. Analytics providers su ch as . A.
h ave their own OR/MS Lools-- A. / O R_ FICO acquin:d another company called XPRE .
thaLoffers optim ization software. O ther major players in Lh is Joma.in include companies
such as AITM .. M ·1 Pl, Frontline, GAJvt., Gurobi, Lindo ystem~, Maximal , GD-ala , Ayala ,
an<l many others. A detailed d d incalion a nd descripLion of lhesc companies· offerings
b beyond lhc scope of our goab here. Suffice il lO say Lhal Lhis ind u s-u-y sector has sc-cn
m uch growth rcu:nLly.
Of course , then: arc many techniques Lha L fall under Lhc category of prescriptive
analyli.c s, an<l each has Lhd r own set of providers. For cx.i.mp!c, s imula Lion softwan:- is pro­
vided by ma/or companies like Rock.veil (ARE A) ancl . imio. Palisade provides lools Lha l
include many software ea Lcgorie.~. Similarly, runLline offers toob for oplimizalion with Excd
spreaclsheeLS, as well a.- pre<l.iclive arr,1JyLic.'>. Decision analysis in mulliobjecLive seLLings can
be performed using Lools .-u c.-h as Exix:n Choke. There arc abo Loots from companies such
a.~ Exsys. XperLRulc, a nd others for generating rules directly from <lat.a. or expert inpuL-;_
ome new companies arc evolving Lo combine mul Liplc analyLics mcxJds in the Big
DJ.ta .-pace including social network analysis an<l slre<1m mining. For example , Teradata
AsLcr include.~ iL~ ov.n pn.-dictivc a nd prescrip tive analytic· capabili1ies in prc>c.-cs.~ ing Big
D.i.La sLrcams. evcrn.l compan ies have dcvclorx:d complex evenL pro,ce.-sing (CEP) engines
tha L make decision.~ using streaming data , .-uch as IBM's lnfo.-phere . Lre.i.ms, MicmsofL's
tre.i.mln.~ight, .i.nd Oracle's Event Pr<K'Cssor. Other major comp anies LhaL have CEP pro<l­
u cts indu<lc Ap-.iche, Tibco, lnformalica , AP, ancl Hitachi . TL is wcmhwhile co note again
thaLLhe p rovider groups for all Lhrce calegori.e s of analytics are no[ mu Lually exclusive. In
mo ..-;t cases , a provider can play in mulliplc componcnL~ of analytic.~ .
\Ve next inLm<luce the win.~ide p<..1ab " of the analytics flower. These dusters cm be
called analyLic.~ acceleralOrs. Althou •h they may nol be invoh•cd in developing lhe te ch­
n ology direclly, Lhesc organizations ha\'C p layed a kcy rok in shaping Ll1e indusuy.
IP RIE SCRIPTIIVE ANALYTICS
Application Develo,pers.: Industry Specific or General
The organiza Liom in Lhis group use [heir incluMry kncnvlcdgc. an.i.lyLical expenise, solu­
tions available from the dala infr.i.sLruc.tu re. D\-Xf. middlc\varc, data aggregators , and an.i.­
ly Lic.~ sofrware p roviders co <levdop cu s[o m soluLions for a specific industry. Thus. Lhis
in<lusLry group makes it possible for analytics Lcdmology Lo be used in a specifi.c ind uslry.
Of course, such groups may also exist in sp <..-ciflc user or;•ani-2a Lions . Mc>.~l major analytic. ·
technology p roviders .like IB\1 , • A . and Tcradala dearly rccogniz-<.: Lhe opportunity Lo
nmnec.t lo a specific in<lu.-Lry or diem an<l offer analytic consulting scrvk'Cs. Companie·
Lim[ have [rn.ditionally provided application/ data soluLions Lo specific sec.tors arc now
developing industry•srx:dfic analyLic.~ offerings. For example. Cerner provides e lec.tronlc
mc<llcal records solmions to medical prov iders, and their offerings now indmk many
.i.nalytic.~ reports and visualizations. imilarly. IB\1 offers a fo.md dcLcclion engine for the
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ience
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health in.~uranl.--e industry, an<l is working with an in.~uranl.--e .::ompany Lo employ their
famous \VaL~on analylics platform in assisting mctlkal pmvitlcrs anti insuf"',mce ,::umpanks with diagno..~b and diS<.."<1sc management. AnoLher example of a \'<.:nical applkaLion
provider is abrc Technologies, whid1 provides :malytkal solUlions to the l.r.rvel industry
including fare pricing for revenue opllmiza Lion and dispaL.ch planning.
111.is cluM<.:r also inclutlcs companies Lhal have developed Lhcir own <lomain-spedfk
analyLics solulion.~ and mark<.:L them broa<ll~• to a diem ba.~c. For example, ike , IB\1, and
portvision develop applkaliom in sports analylics to improve t:hc play anti inuea~e 1.hc
vicwcrsh.ip. Acxiom has tlcvdopc<l duslers for vinually all hou.~dml<ls in Lhe United . laLc-s
b-d..~cd on Lhc <law. they u>llec:t about householtls from many differcnl .,;oun.:cs. Crc<l.it score
and classll'ication reporting companies (HCO, Experian, etc.) abo belong in thb group. IBM
and .,;evcral other .::ompanies offer pridng opLirnizalion solullom in the retail induMry.
111is field represents an cmreprcncurial opponuni.Ly I.O develop induslry-spcd 1c
applkaLions. Many emerging in \Veb/ .,;ocial mc<lia/lo,caLion analytks arc trying LU profile
user.,; for bcucr 1.arg<.:Ling of promotional .::ampaigns in re-al time. Examples of su.::h .::om­
pani<.:.s and 1.hdr activities include: VP.com employs local.ion daLa for developing user/
group profiles and targcdng mobile advcrlisc-mcnls, To,vcrdarn pro des users on the b-.isis
of e-mai.l u~ge, Qualia aim.~ to identify user.,; d1rough all device usage. and imuhmxlia
1.argc-L~ advcrliscmcnL~ on TV on the ba.~b of analy.,;i · o a user'· TV wa t.::hing habir.~.
The grm ·lh of smanphoncs has spawned a complete industry fo.::us<..-tl on spcdfk
analytks applications for con.-umcrs :ts well a.~ organizations. For example , smanphonc
apps su.::h as hazam, Soundhound. o r Mu.,;ixmatch arc able Lo idcmify a song on lhc b-.isis
of I.he first few notes and Lhcn lot 1.hc u.~er .,;elect iLfrom Lheir song base Lo play/ downkY.id
/ purdiasc. Waz-<.: uses real-time traffic informal.ion shared by users, in addition to Lhe 1001Lion daLa , for improving navigaLion. Voke recognition tools such as hi on Lhe iPhom:.
Google ow, and Amazun Alexa arc l<:<1din, to many more .,;pecializ.l.-tl analylics applica­
lion.~ for very specific purposes in analytic.,; applied Lo images, video: . audio. an<l other
data 1.hal can be .::apLurcd lhrough smanphones and/or .::onn<.:clc<l scn.~or.,;. manphones
have also devaux.l the .,;han.-tl economy providers such as Uber, Lyft , Curb, and Ola. Many
of these l."ompanics arc exemplars of analyLks l<:<1ding 1.0 new bu.-inc.,;s opportunities.
Online social media b another hot area in this duster. ndoubtl.-tlly, Facebook b Lhc
leading player in the space of cmlinc social networking follmvcd by TwiLLer and LlnkcdJn.
J\·1 orcovcr, Lhe public: access to I.heir data has given ri.~c 10 multiple mher .::ompanics lhat
analyze their data . For example , nmetric analyzes Twiuer data and provides solutions
lo their .::liems., imilarly, there arc seven.ii other l.Umpanics that focus on social network
analysis.
A lrcnding a.rea in the applicalion devdopmenL industry is lhc loT. evcral .::ompanies
arc building applicalions Lo make sma.n objec.ts. For example,. marrBin has developed intel­
ligent remote monitoring sy-sl<.:m.~ for the wa 1c and recycling SL'Clors. Several olhcr organi­
zalicm.~ arc working on building smart meters, smart grids. sman cities, conncc.ted .::ars,
smart home· , .,;man .,;upply chaim , conrnx:te<l heallh , smart retail. and mhcr sman objcc.ts.
This start-up ac"li\•iLy and SjYdl.'e b •rowing and b in major tmnsiLion due lO 1.cchnol­
Ot,')'/ vc-n1.urc- funding and s<..-curity/ privacy l.-suc-s. cverthdess, the applkalion developer
sector is perhaps 1.hc biggcsl gn:>\vLh imluMry within analytks al lhb point. This duster
provides a unique opportunity for analyLks professional.· looking for more entrepre­
neurial career opLion.~ .
Analytics Industry Analysts and Influencers
The next du.~tcr of I.he analyLics induMry includes Lhrcc- types of organizations or pro­
fessionals. The first group is the sel of profe ·sional organizaLion.~ lhaL provide advkc lo
lhe analyLics indusrry provider.,; and use-rs. Their .,;ervices indu<lc marketing analyses,
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Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
l.Dvcrage of new ucvtdopments, evaluaLion of spedfi.<.: [e<.:hnologies, and developmenL of
training/white pape
and so on. Examples of su<.:h players include organizations such
as the Ganner Group, The Darn \Va.rehousing InsLiluLc, Forre.stcr, M<.:Kin.~(.."y, and many
of the general and technical publka[ions and \"X'eb sites Lha[ cover the analyr.ics induMry.
Gartner Group's Magic Quadrants ar(.." highly influential and are based on induslry sm­
vcys . Similarly. ID\"Xl'Lorg prufc.-sionab provide excellent in<lusLry overviews and arc very
aware of <.:urrent and future Lrcnc:L~ of this indusLry.
The second group indudes profc ·sional sodelic.~ or organi::rn lions Lhat abo pro­
vide some of [he sam(.." servk(.."s but arc mem~rship based and organized. For example,
INFORM , a professional organiza Lion, ha.~ now focused on pmmoling analytic.~. pedal
I ntercst Group on D<.:ciskm upport and Analytics. a subgroup of Lhe A.~socialion for
Infmmation Systems. also focuses on analytics. Most of 1.he major vendors {e.g., TcmuaLa
and SA.) also have Lheir own mem~rship-base<l uscT groups. These enLiLies promote Lhe
use of analytics and enable sharing of the lessons lea.med through their publications and
confcren<.:es. They may a lso provide n:cruiLi.ng service.~. and an.: elms good sourn:s for
kx:aling talenl.
A lhird group of analytics inuusLty analysl~ is what W(.." call analytics ambassadors ,
i.nflu(.."n<.:ers, or e\'angdbts. These analysts have presented lhcir enLhusiasm for analytks
Lhrough their seminars. books. and o [her public:Hions. Illustrative examples indudc St(.."VC
Baker, Tom DavcnporL, Charles Duhigg, Wayne Eckerson , Bill Franb, Malcolm G!adwdl,
Claudia lmhoff, Bill Inman , and many others. Again , Lhe list is not indusive. All of these
amba.~sauors have wri LLen books (some of them b(.."sl.~dlersD anc.1/or given many prcs­
cnLalions Lo pmmoLe [he analyLks applka Lion.~ . Perhaps another group of evangdbts Lo
induuc here is rhe authors of LexLbook.~ on Bl/ analylics who aim lo a.~sist the nex LdusLer
to produce profc ·sionab for Lhe analylics inuuSl:ry. Clearly, it wi.11 wke some lime for an
analytic.~ MUU(.."n[ [o l:xx:ome a m(.."mb(.."r of this d11.1sl(.."r, bu[ they could ~ working w i[h
mcmocrs of Lhis clu.~ler as resear<.:hers or apprenlices.
Academic Institutions. and Certification Agencies
In any knowleuge-imensive indus[ry sud1 as analytic.~, U,(.." funuam(.."ntal strength <.:omes
from having sLudcnts who an.: inLere ·[Cu in Lhe technology and <.:hoo.-ing LhaL indusLry as
their profc ·sicm. nivcrsities play a key role in making this possible. This cluster. Lhcn,
represents Lhe arndemic programs Lhat prepare professionals for the indusLry. It induues
various component'> of busine.-s school.· such as information syst(.."ms. marketing , manage­
ment sden<.:(.."S, and so on . l[ abo c-1,::t(.."nc.Js far beyond bu.~ines..~ S<.:hoob Lo include compu[(.."r
S<.:iencc, MaLisLics. mathemaLics, and industrial engineering ucpartmenL'> aero ·s the \\'orlu.
TI1c duster also indudes graphics devdopcrs ·\ vho design ne\V ways of visualiz:ing informa­
tion. Univ(._"rsitics are offering undergrndua Le and graduate progr.lms i.n analytic.~ in all of
thes(.." <liS<.:iplines. Lhough they rrr,1y ~ labeled diffen.:nLly. A mafor growth fmmi(._"r ha.~ bctn
l.-crtificalc progrn.ms in analylics Lo enable current pmfes..~kmals to retra in and rcwol th(.."m­
selves for analytks can.-ers. C(.."rLilkaLe progrn.ms enable pr-,i<.:Lking analyst.~ Lo gain b-,J.sic
pmfidcnqr in specific sofL\vare by Laking a few cri lical courses from schools that offer Lhese
progr.1.ms. TU includes a lisL of analyLics programs. ILincludes almosL 150 pn.>grams, and
there are likely many more such p rogram.~, ,vith new one.~ being added daily.
Another group of play(.."rs a.~sists wilh uevdopi.ng competen<.:y in analyLics. 111(.."se a.re
l.'Crlifka[ion programs Lhat award a l.'Crlifka[e of expertise in specific softwan.:. Virtually
every major tedmolO!,'Y provider (IBJ\.•I, MkrosofL, Microsu.ucgy, Omcle, AS , Tah!C'.d.U ,
and Terndata) has Lheir own certification progrnms. These certifkaLes (.."nsurc LhaL fX>len­
li:al ne,v hin.:s hav(._" a certain level of tool skills. On Lhe odier hand, I FORJ'\1 ofkrs a
Cenified Analytics Professional <.:(.."nificate pr<.>gram that is aimed al Lesling an individual 's
general analytks <.:mnpetcnqr. Any of these C(.."rlifications give a college student additional
marketable skills.
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111.e growth of academic programs in :malytk:s is staggering. Only Lime will Lell if this
duster i.· overbuilding the capacity tha Lcan be cons1.1me<l by the o Lher dusters, h1.1t :u this
poinl, the demand a p pL"drs lO out.~lrip I.he s1.1pply of qm.i.lified analyLics gradu ates , and
this is I.he mc:i..~l olwious pl.aLx: to find a[ k-,:1s[ <:nLry-level analytics hires.
Regulators and Policy Makers
The phtyers in thb component an: responsible for defining rules and n..-g1.1laLion.~ for pro­
LeCLing employees, customers , and shareholders of the analytics organizations. 111<: collec­
Lion and sharing of the u.~ers' daLa n ..-q1.1ire sLric."l la·w s fur securing privacy . . ·ver.J.1 urgani­
za Lion.~ in lhb space reb'l.ilale Lhe daLa transfer and prolt:ct 1.1sers· right.-;_ Fur example,
Lhe Federal Communic.alions Comm.is.s ion (FCC) regulates inLersW.t<: and imemalion.al
communkaLion.~. imila.rly, the Fedcrnl Trade Commission (FTC) is respon ·iblc fo r prc­
venLing data-rdaLed unfair bu.~incss pracLkx:s . The lnlemaLional decomm1.1nkaLion
Union (ITU) regulate.~ the acce.-s lo informaLion a.nd cummunicaLion technologies ( [CTs)
[o undcrserved cummuni[ies worldwide. On Lhe mher hand. a nunn:gul.atory federal
agency named the a Lional In.-tilU Lc of Standard~ and Technology ( 1ST), hdps advance
Lhe Lechnology infraMructure. There a.re sever.ti other organi.za Lion.~ across the glohe Lha.t
re,6'1.Llate Lhe data security a.nd accelerate Lhe analytics indusuy This i.· a very important
component in the ccc:i..~ysLcm so 1.haLnu one can rnis1.1se consumers' informaLion.
For anyone devdoping o r u.~ing analytics application.~, iLis perhaps crucial Lu have
someone on the Learn who b aware of Lhe regulatory framework. These ab>cncie.- and
profe.-sionals who work with them dearly offer unique analyLics talent.-; and skills.
Analytics User Organizations
Clemty, this is Lhe eo:.mom.ic engine of the whole analytics industry, and therefore, we
represent this duster a.~ I.he con.: of the ana.lyLics flower. If there were no users, I.here
would be no analyLic.~ ind1.1stry. Organiz.:uions in every indu.-Lry, regardless of size, shape,
and location, arc 1.1sing or exploring Lhe use of analytics in their opcrntions. These include
Lhe prh•a[c sector. government. educati.o n , military, and so on. lt includes organ izations
anmnd the 'llvorld. Examples of uses of analytics in different industries abound. OLhers a.re
exploring similar oppon1.1niLies Lo try and gain/reLain a L"ompetilive advantage .. ped 1c
compan ies are not identified in Lhis senion; r.uhcr, the goal here is lO see vvha L type uf
roles analytics pmfessionals can play within a u.~er organization.
Of Luursc , the top leadership of an organi.Zalion, especially in the information lech­
nology group (chief informaLion offkx:r. eLc), is critically imponant in applying analytics
Lo it.~ opcrat.ion.~. Reponc-<lly, ForresL Mars of I.he \1a.rs Chocola Le Empire said lhaL all man­
agement huik-<l do,.vn [o applying ma[hemalics lO a company's opernlions and economics.
Although no[ crnmgh senior managers subscribe m thb view, Lhe awareness of applying
analytics within an orga.nizaLion is 1:,'Towing everywhere. A heallh insurnnce company
execl.lLivc once told us Lhal his boss (lhe CEO) viewt.-<l the company a.~ an IT-enabled
organiz.alion th.at collec1ed money from insured members a.nd distrihuLcd iL lo Lhe pro­
viders. 1111.1s efficiency in Lhb proce ·s ,.vas the premium they nmld earn over a compeli­
Lor. This led Lhe company Lu devdop sevcml ana.lyLics applications lO red1.1ce frn1.1d and
overpayment lo providers , promo[e '>vellnes.s among [hose insured so they '>Vould 1.1se 1.he
providers less ofren, generate more efficiency in pmcessing, and elms be more pmfitable.
inually all major orga.nizaLion.~ in every indu.~try Lha.t we a.re a.ware of arc hiring
analytical professiona ls under variou.~ Lilies . Figure 1.1 is a word duud of Lhe selected
[ides of our program graduates al Oklahoma late niversi Ly from 2013 to 2016. IL dearly
shm;vs Lhat Analytic.~ and Data Science are popular ti tles in Lhe organizations hiring grndu­
ates of such progrnm.s. Other key words appear lO include terms such as Risk, Da.tab-.i.sc,
Securi Ly, Revcn1.1e, MarkcLing , and so on.
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Chapter l
• An Ove iev,• of Busines...~ Intelligence, Analyti s, and Data Science
IFI GUR.E I. I 4
Won:I Cloud of Job Tillles, al Analytics,
Program Graduates..
Of course, user organizations include c:,m:.."Cr palhs for analylics profe.-sionals moving
into management posiliom . These Litles inch.1c.le project managers, senior man.agers. and
direnors, all Lhe way up Lo Lhe chief informalion officer or chid execuLive oftlcer. This
suggesL'> [hal 11.1ser organizaLion.~ exist as a key dusLer in the analytics ecosystem and lhus
can be a good source of talent. ll .is perhaps 1.he 1rs.1. pl.al.."<..' to find an.alyLic.o; pmfession.als
wilhin lhe verlical indusLry segmenL
The purpose of this S<..'Clion has ocen lo presenL a map of Lhc lando;capc of Lhc ana­
lytics inc.lusuy. Eleven different groups that play a key role in building anc.l fos.1.ering Lhb
inc.luSlry \·•,..-ere .identified. More petals/ components can be adc.led over Lime in Lhe analyLic.o;
flmver/ecosys.1.em. Beca11.1se data analytics require.,; a <lh•erse .skill seL. 11.1nc.lersrnnd:ing oflhb
ecosystem provides you v,rith more options than you may have imagined for careers in
analyLic.o; . Moreovt:r. il is rx-xo;sible for professionals lO move from one indusuy duster Lo
another lo take advantage of Lheir skills. For example, expert profession.al · from providers
can somelimes move lo con.o;ulLing po.o;itions, or c.lircnly lo 11.1ser organizaLions. Overall,
there is much lo be excilt:c.l about Lhe analyLics .industry al this poinL
SECT ION 1.8 REVIEW QUESTIONS
1 . Lis[ Lhe 11 caLcgories of players in lhe analytics ecosystem.
2 . G.ive examples of companies in c-.1.ch of the 11 Lypes of players.
3 . \"X' hich companies are dominant in more lhan one category?
4. Is iL ocLler lo be the strongest player in one caLegrn)' or be ac.tlve in m11.1ltiplt: caLegories?
NIM
Plan of the Book
The previou.o; S<..'Cliuns have given you an underslanc.ling of Lhe need for infonnal.ion
technology in dedsion making, the- evolution of BI, and now inLo analyLics and c.lata
sde-nce. ln Lhe l:ast several sec.tlons ,,ve have seen an overvie-w of various types of analytics
and thdr applications. Now '\.Ve arc ready for a more c.lctailed managerial excursion into
these Lopics, a.long wilh some deep hanclo;-,on e-xperience in some of Lhe Lcchnical topic.,;.
Figure 1. J5 pre-senlS a pl.an on Lhe rcSL of Lhe book.
In lhis chapLer. we have- provided an imrodu<.:lion, definitions. and overvie\\' of D . s,
BI, anc.l analytics, including Big 0-.!la an.alyLic.o; and data science-. \"X'c abo ga\'e- ·y ou an
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1 • An Overview of Bu. ·nes. Imelligeo
, oalytics, and Data
ience
4.7·
Business lntellige'.nc-e an d Ana lyt,lcs:
A Manage rial Perspec:ti11e
I
I
lntrodlJCtlon
'-
Cha?tel' 1
An (),Jarview of
Business
Intel igeru::e.
Analvtics. and
Oat.a Science
I
I IJes~lve Analytics I
-
Cha?te'.J' 2
Nature of Data.
Statistical
Modeling. and
Visualization
Cha?te'.J' 3
Bu!fness
._ lnl:.elligerme al'lld
Data
Warehousing
F;IGURIE I. U
I
I
I
Predictive Analyllics
-
Cha?llel' 4
Data M111.ii119
Processes.
Mel:.hods. and
AJgorit.lvns
,_
I
I I PNM:cnipti'lle Analyt:iic::s I I
-
Cha?t-er 6
Qptlmi.zatlon
and
Simulation
Cha?ller S
Text. Web.
al'lld Soci
Medfe
Analytfcs
I
FuttJre Tr-ends
-
Cha?ter 7
Big Data
Conc~ts
and Tools
-
Cha?ller B
Fut.ur e Trends.
Pl"ivacv. and
Manage.ria
Considerat:iiom;
In Analytics
Plan al the Boole.
overview of Lhe analylics ecosystem Lo have you appreda Lc Lhc bn:adLh and depth of I.he
ind11.1slry. Chapters 2 and 3 l.Uver <lescripLive analyLic.~ and data issues. D.tta de,niy form
Lhe foundalion for any analyLics applicalion . Thus we cover an inLrod11.1cLion Lo daLa ware­
housing issues. applications. and Lcchnologies. TI1is secLion abo covers busine ·s reporting
and vbualizaLion technologies and applicalions. This is followed by a brief overview of
BPM Lechniques and applicalions-a topic Lhal has been a key part of traditional BL
The nexl sel'.Lion covers predictive analytic.~ . Chapter
provides an inlro<lucLion
lO predictive analyLic.~ applications. TL indudes many of 1.he common d:ala mining lech­
n iq11.1es: dassi 1calion, clustering. a ·soc.ia[ion mining. and so for[h . Chapler - focu.~es on
lexL mining applications as wdl as \Veb analytics, inclu ding social me<l.ia analytks. senli­
men Lanalysis, and other n:~laL<cxl topic.~ . Chapter 6 covers prescriplive analyLic.~. Chapter 7
ind11.1des more detaib of Big Dal.a analyLics. Chapler 8 ind11.1des a disc.LJ.ssion of emerging
lrend~. The ubiquity of \\tireless and GP. devices and other .sensors is resulting in chc
creation of massive new databases and unique applications. A new brel.-<l of analytics
cornpanie.· is emerging Lo analyze these ne,v databases and create a much beuer and
deeper understanding of customc:rs' behaviors and movemenl.~. TL is leading lo Lhe auLo­
ma Lion of analylics and has also spannc<l a new area called 1.he ~Tnlemel of Things.p TI1c
chapter abo l.uvers doud-bascd analydcs, Final.ly, Chapler 8 also auempL.-; lO incegra Le all
lhe ma Lerial covered in 1.his book and concludes with a brief discussion of sccurity/ pdvacy
dimension.~ of analytic.~.
■iii•• Resources, Links. and the Teradata University
Network Connection
The u.~e of Lhb chapter and most other chapters in this book can he enhanced by the lools
described in I.he following Sl.'Clions.
Resources and Links
\Ve recommend the following major resources and link.~:
• The Data 'l ' arehousing InsLilute (1.dwi .org)
• Dar.a Sdenl."C Ccnlral (<l.ala.~cienccl."Cnlr.tl.com)
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Chapter l
• An Ove iev,• of Busines...~ Intelligence, Ana lyti s, a nd Data Science
• DS. Resrn..irl."e.S {<lssreSA:.>urccs.com)
• Micmsufl En lerprbe Comorliu m (enlcrp rise.wal l<mcollcgc.u ark.c<lu/mec.a.sp )
Vendors, Products, and D·e mos
Mo: l vcm..lors provide software demos of lhe ir prc><..lu cL~ and applications. Tnfonnalion
alxmt pmd u<.:Ls, a.rchiteclure, and .software is available al dssrcsources.com.
Periodicals
\\(Tc recomm cnc.l lhe following p erioc.lirnb:
• Decision Supporl J'Slems (w\V\v.joumab .dse\'ier.co
• CIO !might (cioinsigh L. com)
dedsion-sup pon-sysu.:-ms)
The Teradata University Network Connection
Th b book b tighlly conne cted wilh the free resources provide<l by TUN {see terndata
univers itynelwork.com). Th e TU portal is divided inLo two major pans: one for stu<lenls
an<l one for faculty. This book is connccted lo lhe TU p ori.al via a special section a l the
end of e<1ch chapter. That senion includes a.ppropria le links for Lhe sp ecific chapte r, point­
ing m rde\'anl resources. In adc.lilion, \ ·e provide hands-on exerci.ses. u sing software a n<l
other ma lerial (e.g., cases) ava ilable a l TUN .
The IB ooktsWeb Site
Thb bcx>k"s \Veb site, p ear.sonhigh erc<l.cor sh.arda, coma.in.~ supplemental lcxlu al mate­
rial organized a.~ '\l eb ch aplers that correspond to the printed b<.K)k's ch aplers. Th e topics
of Lhesc chaplc.:r.s are lbted in the cmline chapter table of conlents. 1
'A.~ this book wenl lo pre. s, we \•erifii~ th:11 :ill ciled \\~eh siJe.,; we-re acli\"e arxl \':llid. Ho\\<e\-ef", URL~ .:i.rc­
dyn:1mic. Wc-h s ites lo which we refet" in 1he text sometimes dungc- ra :ire discontinuro because comp:mie.,;
change names, :ire bought or sold. merge, or fail. • ometime:s Web sites :1re dmvn for m:1inlen:incc-, repo.ir, o r
rede. ign. M:1ny org;mizations have dropped the injli:il "wwv.•- design:11 ion for their. ites, bu! some .sail! me it. If
}'OU h:1,·e a problem connecting 1.0 :1 We:h site- that we mention, please be p:1tienl :1nd s impl}' run a \"'iTeb se:m:h
to try lo identif , the- po. sihlc- nc-,.v site. :\t05t time.~, you can quickly frnd the new site- through one of the poixrJa r
se:m:1-r engines. \Yle ap.ologi7..e in :im'!lnce for this incom,c,nic-ncc-.
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1 • An Overview of BusJ ness Imelligen e, Analytics, and Data
ience
49
Chapter Highlights
• 111e hu.~ iness environment is becoming more
complex anc.l is rap i<lly changing, making deci­
sion making more difficult.
• Businesses musl respond an<l ac.lapt lo Lhe chang­
ing environment rapidly by mak ing faster and
heller decisions.
• 111e l ime frame for mak ing decisions is shrink­
ing, whereas Lhe global naLure of c.ledsion making
is expanding, m..'Ce.~siw.1ing Lhe devdopmenl and
u.~e of compulerize<l D . s.
• D. Ss use <l:ata, models, and someLimes knowledge
m:magemenL lO find soluliom for semisLruelured
anc.l some unsLruelured problems.
• Bl methods utilize a cenLral n:po ·itory called a
D~· thaL enabk: efficient data mining, OL.\P,
BPM. and claw. vi:ualizalion.
• Bl architec.tun: includes a D'l , bus iness analyt­
ics Loob used by enc.I u sers, and a u.~er inLerfa(.-e
(such as a <la ·hboar<l).
• Many organiza Lions employ descripLive analyt­
ics 10 n:place 1hdr rraditional flaI reporting 1,vi1h
interactive reporting thaL provides insights, Lrend~ .
anc.l pauern.~ in lhe lr.J.nsac.tional claw..
• Pre<lic1ive analyLic..~ enable organizations Lo
establbh predictive ruks tha L drive the business
outcomes 1hrou gh historical data analysis of Lhe
existing behavior of Lhe cuMomers.
• Pn:scripth•e analytic: help in huil<ling mo<leb tha L
involve foreca ·Ling anc.l opLimization techni4ues
based on the principles of OR anc.l man:agcrru:n L
science Lo help organizations lO make hener
decision.·.
• Big Data anal~•Lic..~ focuses on unstrnclurec.1, large
data sets lruil may a lso include vasLly <lifferen L
types of data for analysis.
• AnalyLics a.~ a field is also knmvn hy induMry­
speciflc application names, such a.~ sports analyt­
ic..~ . IL b also known by mher relaLec.l names such
as c.laLa science or not,vork science.
• Healthcare and retail chains an: L"\vo a.reas where
analytics applica Lions abound. with much more
Lo come.
• The analylics ecosystem can be first viewe<l as a
collec.tion of prnvic.lers, users. and fadlirn Lors. IL
can he broken into 11 d uslers.
KeyTerms
analytic·
analytics ecosystem
Big darn analyLic..~
business intelligence
dashboard
data mining
decision or norma Live
analyLic..~
(BI)
c.lescriplive (or reporling)
analyLic..~
imdligem agents
online analytical
proce ·sing (OLAP)
online lr.J.nsac.tion
pmce ·sing (OLTP)
prec.liuive analytics
prcscripLive analytic..~
~ eh servk-es
Questions for Discussion
1.
urvey the literature from che past 6 momhs to find one
application ea h for D . Bl. and analyti s. ummarize
the applkacions on one page, and .submit it with the exact
sources.
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2. Distingui h B] from D .
3. Compare and concra.st pre<lictlve analytics with pres rip­
ti,·e and descriptive ;111;1lytics. se examples.
4. Di.'>CU. the major L-sues in implementing BI.
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50
Chapter l
• An Overviev,• of Busine. ~ Intelligence, Ana lyti s, and Dar.a
eoce
Exercises
Teradata Unh'e.rsity Network and Other Ha:od&-On
Exercises
1. Go to teradarau niversitynetwork.com. Using the site
pa. word your instructor provides, register for the site ff
you have om already previously regL-.tered. Log on and
leam the comem of che site. You will re eive assign­
ment.'> related to thi site. Prepare a 1ist of 20 items on the
ite that you think ould be benefi ial to you .
2 . Go to the TUJ\" site. E.a.::plore the Sports Anal lt s page,
and summarize at lea~t two applications of analytics in
any sport of your hoice.
3,. Enter the TIJ
ite, and select "Cases, Projects, and
Anignments." Then sele t the case srudy -Harrah'. High
Payoff from Cu.~tomer Information." Answer the fo llow­
ing questions about thi.~ ca e:
,a. 'I! hat inforrn.ation does the data mini ng generate?
b.. How i;.-. this information helpful co management in
decUon making? (Be speclfic .)
c. List che types of data that are mined .
d. ls this a D. . or BI applicati n? W11y?
4. Go to ceradatauniversitynern'Ork.com and find che paper
tided "Data Warehousing . upporL~ Corporate Strate y
at First Am rican Corporation~ (by 'I! ,acson, \'i/ iJ om, a n<l
Goodhue). Read the paper. and answer che following
questions:
a. What were the drivers for the DW/BI project in the
company?
b . What strategic advantages were rea lized?
c. What operational and ta cical advantages were
achieved?
d. 'I! hat were tht critical succe. fa tors for the imple­
mentation?
S. Go to http.//analytics-maga.zine.org/i. ~ues/digical-edi­
tions and find the Janua / February 20 12 edition citied
• peciia l 1.- ~ue. The Furure of Healthcare.- Read the arci­
de -predictive Analyri s---Saving Li\'es and lowering
Medical Bill~.~ Answer the followi ng que. tions.
a. What problem i.-. being addre sed by applying predi tive analyti ?
b . What is the FICO Medication Adherence ore?
c. How i.-. a prediccion m del trained to predi tthe FICO
Medicacion Adherence Score H H? Did the predi cion
model cla. sify the FICO Medication Adheren e Score?
d. Zoom in on Figure . and explain what kind of ce h­
nique is applied on the enerat d results.
e. List som of che actionable de isions th.at were ba ed
on the prediction resulL~.
6 . Go co http://analycics-magazine.org/i.;;: ues/digita l-editi n.-.,
and find the January/ February 20 13 edition titled
~work . odal.~ Read the arcide -mg Dar.a . Analyc:" sand
Election ,- and an wer the following que~cion.-.:
a. \Xthat kinds of Big Data were analyzed in the article
Coo? Comment on sorne of the sources of Big Data.
b. Explain the term fmegrt1/~yJ sysrem . What i the other
technteal term chat suib an inlegrmed SJ- tem?
c. What kinds f data analysi~ techniques are employed
in the project? Comment on s me inici:ati\<es that
resulted frorn data analysis.
d . What are the different prediccion problems an. wered
by the model.~
e. List some o f the actionable decisions taken chat were
ba~ed on the prediccion results.
f. Identify two applicati ns of Big Data analycic.s thac are
n t listed in the arti le.
7 . . earch che Intemet for material regarding che ,;1,1ork of
ma nagers and che role ana l~'li ~ plays. \'!:rhat kinds of
refer nces to consulting firms , academic deparcmenL.~,
and programs do you find? What major areas are repre­
sented? Select five • ite. that cover one area, and report
your findin
8 . Explore the publi areas of d1 sreS01.1rces.com. Prepare a
li~t of ic: major available re~ource . You might want co
refer to thi . ite a. you work through the b ok.
9 . Go to microstrategy.com. Find information on the five
styles of BI. Prepare a summary table for ea h style.
10. Go to oracle.con1. and lick ch.e Hyperion link under
Applicacions. Determine what the companj"s major
products are. Relate these to the supp n technologies
cited in this hapter.
11. Go to the T · · quescions site. Look for~ I videos. Reviev.•
the vidto of che -case of Rera ii Tweeters.~ Prepare a one­
page summary of t:he problem. prop s d solution, and
the reported resulL-.. You can a lso find a. ociated slides
on slideshare.oec.
12 .. Revi w the Analytics Ecosyst m section. Identify at least
two addicional companies in at leas1 fi\•e of the industry
clusters noted in the discussion .
13. The dis ussion for the analyti s ecosystem also included
several typical job cities for graduat s of anal}1i s and
data s ience pro rams. Research Web sites su h dar.a­
sdencecentr-J l. com and tdwi.or to locate at lea.~t three
additional similar job titles that you rnay find imere ling
for your career.
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1 • An Overview of BusJ ness Imelligen e, Analytics, and Data
ience
51
References
Capcredic.rom. (2015). -How c\Iuch Do Americans pend on
ports Each Year?- cap,,..--redit.com/hcw.•-much-american.<;­
spend-on-sports--each0year/ (acre: sedJuly 2016).
CDC.g v. (2015, eptember 21). "Imporrant Facts ahout Falk­
ck.gov/horneandrecreationalsafety/falls/adultfalls .html
(accessed Ju ly 2016).
CenterPointEnergy.com. -company Overview.- centerpoim
energy. om/en-us/Corp/Pages/Company-overview
.aspx (accessed June 2016).
Chiguluri. V., Guthikonda, K. , Slabaugh, ., Havens. E., Pena,
J-, & Cordier, T. (20 15, June). Relatio'1}. hip betu-een dlal>e­
w.- comph'calto11s and bealtb n. lared qualify of lifi- among
t:."111 elderly pojJlllatio11 in rhe f.I nited Statl'S. Po.: ter presen­
ration at the American Diabetes Association -th Annual
Scientifi Sessions. Boston, MA.
Cordier. T., labaugh. L. , Haugh, G., Gopal. V., Cu.-.ano, D ..
Andrev. ., G., & Renda, A. (20r , s eptember). Qiwli~)•
of life cbanges u.'ir/J progf'l!Sl tng congesUL>e hea/'1 fafhm
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52
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