Project by Yves Studer Course "Innovation and New Technologies" - University of Camerino (teacher C. Vaccari)

1. 04th(of(February(2014(|(Yves(Studer(
((((((((((((((((((((((Big(Data(in(Prac@ce(
Towards(a(procedure(model(for(
Big(Data(
Costa(&(Oliveira((2012)(
#1 You are able to identify
common hurdles when doing
Big Data.

2. #2 You know some strategies
and a procedure model to
overcome these.
Big(Data:(Agenda(
1. Why?(Mo@va@on(&(perspec@ves(on(
2’#
2. What?(Short(overview(of(challenges(
7’#
Data(in(an(enterprise(
3. How?(An(example(of(a(procedure(
model(
5’#

3. ?!(
?(?(
Why?
Your Motivation
New Paradigm: Big data streaming into
an enterprise means(not(just(to(capture(and(store(
data(but(to(put(it(into(the(hands(of(end users.
Aberdeen(Group((2012)(

4. What(type(of(Big(Data(to(exploit?(
External(
External(
(Data(
Linking(internal(
and(external(
informa@on(
Internal(
Dig(deeper(into(
the(current(pool(
Data(
which(can(already(
be(used(
Insurance(
Sector(
Extend(the(
Example(
territory(
Explore(the(black(
hole(
Analyse(dead(
storage(
exists(but(yet(
unused(
not(yet(
collected(
Based(on(Arthur(D.(LiXle((2013)(
Why(external(data?(
Insurance(
Sector(
Example(
Insurance(is(a(predic@on(business:(
(
“If(a(predic@on(model(is(only(4(to(5(
percent(beXer(than(a(naïve(predic@on,(
but(new,(external(data(can(help(improve(
that(by(a(couple(of(points(across(millions(of(
transac@ons,(that’s(huge.”(
Edward&Vandenberg,&Farmers&Insurance’s&Director&
of&Advanced&Analy=cs&in&Whi=ng&(2013)&

5. It(is(said(to(be(worth(the(trouble(
! (2.5(@mes(stock(apprecia@on.((
! (2(@mes(larger(EBITDA(growth.((
! (1.6(@mes(higher(revenue(growth.(
IBM&&&The&Economist&(2012)&&
Analy@cal(advantages((
of(external(Data(
Insurance(
Sector(
Example(
• BeXer(understanding(of(customer(habits(/(risks(
• Effec@ve(marke-ng:(judge(customer(reac@ons(
• Improved(tracking(of(insurance(markets(and(the(
overall1business1health1
• Development(of(new1products1and(programs(to(
capitalize(on(market(changes.(
• BeXer(fraud(detec-on1
Whi=ng&(2013)&

6. What?
YOUR1CHALLENGES1
Your Challenges
Challenges(
Insurance(
Sector(
Example(
New(data(choices(
Privacy(concerns(
Time(delays(
Increasing(data(volume:(Volumeecapable(
Infrastructure(
• Innova@on:(Cultural(Shif(
• Gegng(the(external(data(before(the(project(
• Human(Bias(
•
•
•
•
Whi@ng((2013)(

7. Selected(Challenges(
Challenge 1:
Value Issues
Challenge 2:
Legal Issues
Challenge 1: Value Issues

8. Where(Big(Data(creates(Value(
D.(LiXle((2012)(
#1(
Data(analysis(not(detailed(enough(
#3(
End(users(gegng(data(not(fast(enough(
#2(
Data(inaccessible(/(underused(
#4(
Data$too$fragmented$/$‘siloed’$not$
ac4onable
Based(on(survey(by(Aberdeen(Group((2012)(

9. Transla@ng(Big(Data(into(Value(
#1(
#2(
#3(
#4(
Schmarzo((2013)(
What(to(do?((I)(
! Iden@fy(key(business(ini@a@ves(
! Business(&(IT(stakeholder(collabora@on(
! Formalise(the(“envisioning(process”(
! Use(Prototpyes(
Schmarzo((2013)(

10. What(to(do?((II)(
! Understand(technology(&(architectural(
op@ons(
(
! Uncover(new(mone@sa@on(possibilites(
(
! Understand(the(organisa@onal(
implica@ons(
Schmarzo((2013)(
Challenge 2: Legal Issues

11. legal rules collide with
technological and
business realities
Tene(&(Polonetsky((2012)(
Selected(Legal(Issues(
• Obsolete(Concept(“Personally(
Iden@fiable(Informa@on”((PII)?(
• Data(Minimisa@on(and(Big(Data?(

12. Old(Roots(of(Privacy(Guidelines(
Closed(Networks(
Limited(
Limited(
numbers(
numbers(
of(Sources(
of(Sources(
1(Actor((
(“Data(Controller”)(
Organisa@on(for(Economic(Coeopera@on(and(
Development((2013)(
What(is(PII?(
• Defini-on:1“any(informa@on(rela@ng(to(an(
iden@fied(or(iden@fiable(individual”.(
Any(data(that(are(not1related1to(an(iden@fied(or(
iden@fiable(individual(are(therefore(none
personal(and(are(outside1the1scope1of1the1
Guidelines.(
Organisa@on(for(Economic(Coeopera@on(and(
Development((2013)(

13. “Nice,(if(we(
anonymise(it(is(
no(PII!”(
“Tradi@onally,(deGiden-fica-on1was(viewed(as(a(silver(
bullet(allowing(organisa@ons(to(reap(the(benefits(of(analy@cs(
while(preserving(individuals’(privacy.((
Tene(&(Polonetsky((2012)(
Well,(boss…(
(
[…](computer(scien@sts(have(
repeatedly(shown(that(even(
anonymised(data(can(typically(
be(reeiden@fied(and(
associated(with(specific(
individuals.”(
(
Tene(&(Polonetsky((2012)(
(

14. Solu@on?(
(
“Treat(all(data(as(PII(or(
you(play(whackeaemole”(
Paul(Ohm((2012)(from(University(of(Colorado(
Law(School(
Data(Minimisa@on(
• Organisa@ons(are(required(to(
– limit1the1collec-on1of1personal1data1to(the(
minimum(extent(necessary(to(obtain(their(
legi@mate(goals.((
– delete1old1data1that(is(no(longer(required(
Organisa@on(for(Economic(Coeopera@on(and(
Development((2013)(

15. Solu@on?(
• In(a(big(data(world,(the(principle(of(data(
minimiza@on(should(be(interpreted1
differently1
– requiring(organiza@ons(to(deeiden@fy(data,(
– implement(reasonable(security(measures,(
– limit(uses(of(data(for(acceptable(purposes(
(individual(&(societal(view).(
Tene(&(Polonetsky((2012)(
What’s(next?(
• The(EU(Data(Protec@on(Direc@ve(is(being(
finalised(by(this(year*((
• The(OECD(currently(assesses((
– employment(impact(of(dataedriven(automa-on,(
– issues(related(to(compe@@on(
– and(intellectual1property(rights.(
(
*(Some(vivid(discussions(are(currently(going(on,(
(follow(#EUDataP(on(TwiXer(

16. 3(
1(
2(
How?
A Procedure Model
The(Big(Data(Procedure(Model(
Con@nuous(
Improvement(
True(endetoeend(
integra@on(
7(
6(
Exploita@on(of(new(Data(
5(
Consolida@on(&(
Migra@on(
8(
Repor@ng(&(Predic@ve(
Analy@cs(
4(
3(
2(
1(
Implementa@on(
Prepara@on(of(IT(
Maturity(Assessment(
BITKOM((2012)(

17. Phase(1:(Maturity(Assessment(
51
BusinesseOp@misa@on(
41
Some(Processes(op@mised(
Applica@on(
31
Some(Project(in(endephase(
Strategy(
21
Some(Projects(started(
Centre(of(Excellence(
11
Some(Concepts(&(PoC(
Big(Data(Ini@a@ves(
01
Inexistent(
1(
Op@misa@on(
Legacy(Applica@ons(
Maturity(Assessment(
BITKOM((2012)(
Phase(2:(Prepare(IT(for(Big(Data(
(
ITegoals(
GapeAnalysis(
(
2(
1(
Prepara@on(of(IT(
Maturity(Assessment(
BITKOM((2012)(

18. Phase(3:(Implementa@on(
(
Integra@on(into(exis@ng(ITelandscape(
Consider(CloudeSolu@ons(
(
3(
2(
1(
Implementa@on(
Prepara@on(of(IT(
Maturity(Assessment(
BITKOM((2012)(
Cultural(Issue:(ParadigmeShif(
Tradi-onal1
Business(Unit(
defines(
ques@ons(
IT(structures(
data(to(
answer(the(
ques@ons(
Big1Data1
?
IT(enables(
(cloud)(
plauorm(for(
discovery(
?
Business(Units(
discover(informa@on(
in(data(
based(on(Hoge((2012)(

19. Phase(4:(Consolida@on(&(Migra@on(
(
Op@misa@on(of(exis@ng(infrastructure?(
New(data(sources?(
(
Ownership(of(Data!(
Consolida@on(&(
4(
(
Migra@on(
3(
2(
1(
Implementa@on(
Prepara@on(of(IT(
Maturity(Assessment(
BITKOM((2012)(
Phase(5:(Exploita@on(of(new(Data(
Exploita@on(of(new(Data(
5(
Consolida@on(&(
Migra@on(
4(
3(
2(
1(
Implementa@on(
Prepara@on(of(IT(
Maturity(Assessment(
BITKOM((2012)(

20. Phase(6:(Repor@ng(&(Predic@ve(
Analy@cs(
6(
Exploita@on(of(new(Data(
Repor@ng(&(Predic@ve(
Analy@cs(
5(
Consolida@on(&(
Migra@on(
4(
3(
2(
1(
Implementa@on(
Prepara@on(of(IT(
Maturity(Assessment(
BITKOM((2012)(
Phase(7:(Endetoeend(Integra@on(
True(endetoeend(
integra@on(
6(
Exploita@on(of(new(Data(
5(
Consolida@on(&(
Migra@on(
7(
Repor@ng(&(Predic@ve(
Analy@cs(
4(
3(
2(
1(
Implementa@on(
Prepara@on(of(IT(
Maturity(Assessment(
BITKOM((2012)(

21. Phase(8:(Con@nuous(Improvement((
Con@nuous(
Improvement(
8(
True(endetoeend(
integra@on(
6(
Exploita@on(of(new(Data(
5(
Consolida@on(&(
Migra@on(
7(
Repor@ng(&(Predic@ve(
Analy@cs(
4(
3(
2(
1(
Implementa@on(
Prepara@on(of(IT(
Maturity(Assessment(
BITKOM((2012)(
Key(Takeaways(
• (External)(Big(Data(can(create(a(compe@@ve(
advantage(
• To(achieve(it,(many(issues(have(to(be(
considered.(Our(focus(today(was(on:(
– Finding(the(business(value(
– An@cipa@ng(legal(changes(
• Procedure(models(allow(to(tackle(the(Big(Data(
challenges(step(by(step.(

22. We(can(do(it.(
Bibliographic,References,
,,
AberdeenGroup,,2012.,In#memory)Compu-ng :)Li2ing)the)Burden)of)Big)Data)Two)Birds)with)One)
Stone.)Available,at:,h?p://spoAire.Bbco.com/~/media/contentEcenter/arBcles/aberdeenEinE
memoryEanalyBcsEforEbigEdata.pdf,[Accessed,January,4th,,2014].,
BITKOM,,2012.,Management,von,BigEData,Projekten.,Available,at:,h?p://www.bitkom.org/files/
documents/LF_big_data2013_web.pdf,[Accessed,January,4th,,2014].,
BoozAllenHamilton,,2012.,Harnessing,Big,Data,to,Solve,Complex,Problems:,The,Cloud,AnalyBcs,
Reference,Architecture.,Available,at:,h?p://www.boozallen.com/media/file/theEcloudE
analyBcsEreferenceEarchitectureEvp.pdf,[Accessed,January,4th,,2014].,
Davenport,,T.H.,&,Harris,,J.G.,,2007.,Compe-ng)on)Analy-cs:)The)new)Science)of)Winning,
(eBook).,,Boston,,Massachuse?s:,Harvard,Business,School,Publishing.,Available,at:,h?p://
www.amazon.com,[Accessed,November,28,,2013].,
Davenport,,T.H.,,2012.,Enterprise)Analy-cs:)Op-mize)Performance,)Process,)and)Decisions)
Through)Big)Data,(eBook).,,New,Jersey:,FT,Press.,Available,at:,h?p://www.amazon.com,
[Accessed,November,28,,2013].,
,

23. Bibliographic,References,
Hoge,,W.,,Big,Value,from,Big,Data.,,,pp.1–19.,Available,at:,h?p://www.slideshare.net/
WilfriedHoge/ibmEbigEvalueEfromEbigEdata,[Accessed,January,5,,2014].,
IBM,Center,for,Applied,Insights,,2012.,Outperforming,in,a,dataErich,,hyperEconnected,world.,
Available,at:,h?p://wwwE01.ibm.com/common/ssi/cgiEbin/ssialias?
htmlfid=YTE03002USEN&appname=wwwsearch,[Accessed,January,16,,2014],
Lee,,E.,,2013.,Insurers,Tame,the,Challenge,of,Data,Through,Context.,PropertyCasualty360.,
Available,at:,h?p://www.propertycasualty360.com/2013/01/31/insurersEtameEtheE
challengeEofEdataEthroughEcontex?t=analyBcsEdata,[Accessed,January,28,,2014].,
Paul,M.,Schwartz,&,Daniel,J.,Solove,,The,PII,Problem:,Privacy,and,a,New,Concept,of,Personally,
IdenBfiable,InformaBon,,86,NYU,L.,REV.,1814,(2011),
Tene,,O.,&,Polonetsky,,J.,,2012.,Big,Data,for,All:,Privacy,and,User,Control,in,the,Age,of,AnalyBcs.,
Northwestern)Journal)of)Technology)and)Intellectual)Property,,11(5).,Available,at:,h?p://
papers.ssrn.com/sol3/papers.cfm?abstract_id=2149364,[Accessed,January,30,,2014].,
Tene,,O.,,2012.,The,ComplexiBes,of,Defining,PersonalData:,AnonymizaBon,,8,DATA,PROT.,L.,&,
POLICY,8,,6.,
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