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Yves Studer: Big Data in practice

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Project by Yves Studer …

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

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  • 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., Image,Sources, Penguins,E,h?ps://www.kernel.org/pub/linux/kernel/people/paulmck/Confessions/ Elephant_Team_03.jpg, Elephant,in,rage,E,h?p://www.snidelyworld.com/Humor/Photos/ElephantRage.jpg, Money,elephant,E,h?p://goclom.net/origamiEpapier/origamiEelephantEfacile.aspx, Elephant,parade,E,h?p://www.viralread.com/wpEcontent/uploads/2013/04/circusEelephants.jpg, Water,&,hands,E,h?p://flickr.com/dinabenne?, Stones,E,h?p://flickr.com/arztsamui, Wall,E,h?p://flickr.com/rudolfvlcek, Dashboard,E,h?p://flickr.com/sashaLah, Silos,E,h?p://flickr.com/davidvernon, , All,accessed,at,20th,of,January,2014, ,

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