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Let's make money from big data!

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Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a …

Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.

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  • 1. TRANSLATING TECHNOLOGY INTO BUSINESS Let’s make money from Big Data! JUNE, 2014
  • 2. About  “Transla.ng  Technology  into  Business”   June  2014   2   •  B  Spot  helps  clients  transform  technology  ideas  into  business  concepts.   •  As  part  of  our  on  going  effort  to  add  value,  we  publish  monthly  content  related  to  this   topic  on  our  website.  “Transla?ng  Technology  into  Business”  is  aimed  at  organisa?ons   and  individuals  who  want  to  understand  some  of  the  changes  and  impact  that  technology   developments  have  on  industries  and  business.     •  Our  short  presenta?ons  are  not  deep  technical  documents;  rather,  they  are  business-­‐ orientated,  analy?cal  opinion  pieces  and  perspec?ves  about  the  dynamics  surrounding   technology  developments  and,  most  importantly,  the  opportuni?es  that  these  create.     •  B  Spot’s  presenta?ons  are  free  to  download.             •  If  you  have  any  further  ques?ons,  sugges?ons  for  new  topics,  or  comments  please   contact  beatrice@bspotconsul?ng.com                Enjoy!              B  Spot                
  • 3. Content   June  2014   3   §  Explaining  Big  Data   §  Evolu?on     §  Market  segmenta?on   §  Market  size  and  forecast   §  Demand  analysis   §  Spot  on  …  what  you  need  to  take  away  
  • 4. Big  data  technologies  are  just  tools;  the  real  value  comes  from   what  we  make  out  of  it   Explaining  Big  Data       June  2014   4   Big  Data  is  data  that  is  too  large,  complex  and  dynamic  for  any  conven.onal  data  tools  to  capture,  store,   managed  and  analyse.     The  right  use  of  Big  Data  allows  analysts  to  spot  trends  and  gives  niche  insights  that  help  create  value  and   innova.ons  much  faster  than  conven.onal  methods.       Source:  Vipro   Volume     Velocity     Variety     Amount  of  data  stored  worldwide  (in  petabytes)       >  3,500  North  America         >  50  La?n  America         >  2,000  Europe   >  250  China   >  50  India  >  200  Middle  East   >  400  Japan   •  People  to  people:  Social  networks,  web   logs,  virtual  communi<es,  etc.     •  People  to  machines:  medical  devices,   archives,  digital  TV,  e-­‐commerce,  smart   cards,  bank  cards,  computers,  mobiles,   etc.     •  Machines  to  machines:  Sensors,  GPS   devices,  bar  code  scanners,  surveillance   cameras,  scien<fic  research,  etc.     The  speed  at  which     new  data  is  being  created  –  and  the  need  for  real-­‐<me  analy<cs  to  create     business  value  from  it  -­‐-­‐  is  increasing  thanks  to  digi<sa<on  of  transac<ons,   mobile  compu<ng  and  the  sheer  number  of  internet  and  mobile  device  users.  
  • 5. Big  data  is  far  from  new  but  has  only  in  recent  .mes  been   recognized  as  an  industry     Evolu.on     June  2014   5   Source:  Bspot  analysis   1989   2005   2011   2012   2013   Tim  Berners-­‐ Lee  invents   the  Web  and   mass  digital   data   collec.on   starts   Steve  Jobs  became   one  of  the  first   people  in  the  world   to  have  his  en.re   DNA  sequenced  as   well  as  that  of  his   tumor  –  first  person   to  use  Big  Data  to   try  to  safe  his  life.     The  open  source  Big  Data  framework  called  Hadoop   has  been  all  about  innova.ve  ways  to  process,  store,   and  eventually  analyze  huge  volumes  of  mul.-­‐ structured  data.  From  the  .me  of  its  incep.on  by   Doug  CuUng  at  Yahoo  un.l  2011  or  so,  the  majority   of  enhancements  to  the  plaZorm  have  been  mostly   focused  on  new  and  be[er  ways  to  accomplish  this   core  func.on.   The  amount  of  data   created  both  inside   corpora.ons  and  outside   the  firewall  via  the  web,   mobile  devices,  IT   infrastructure,  and  other   sources  is  increasing   exponen.ally  each  year.   From  2005  to  2020,  the   digital  universe  will  grow   by  a  factor  of  300,  from   130  exabytes  to  40,000   exabytes,  or  40  trillion.   Google  self-­‐drive  car   based  on  big  data   intelligence  is  being   developed     Further  development  of  visual   techniques  and  technologies  used   for  crea.ng  images,  diagrams,  or   anima.ons  to  communicate,   understand,  and  improve  the   results  of  big  data  analyses,  e.g.   tag  cloud,    clustergram,  history   flow,  spa.al  informa.on  flow,  etc.       Major  IT  vendors   aggressively  entered  the   big  data  space  despite   making  li[le  revenue   from  it  but  recognizing   future  poten.al  and   massive  impact  on  their   hardware,  sobware  and   other  services  impact.       Following  an  example   from  retail  and  stock   exchange    markets  other   industries  have  started   using  big  data    tools  for   their  internal  and  external   purposes.  Mainly  for   customer  segmenta.on   and  product   development.         2014   Ed  Snowden  exposes  mass  surveillance   and  big  data  abuse  by  the  US  and  the   UK  authori.es.    The  issue  of  privacy  and   correct  usage  of  big  data  became  an   urgent  issue.       Major  infrastructure  in   big  data  investments   taking  place.    
  • 6. The  market  is  s.ll  generally  very  fragmented     Market  segmenta.on   6   •  Storage   •  Servers   •  Networking   Vendors  include   Dell,  HP,  IBM,   Cisco     Hardware   Big  Data   Distribu.ons     Data   Management   Components   Analy.cs  and   Visualisa.on   Services     •  Community   Hadoop   distribu<ons     •  Enterprise   Hadoop   distribu<ons     •  Non-­‐Hadoop  Big   Data  framework   Vendors  include   Cloudera,  IBM,   MapR,  LexisNexis,   MicrosoW     •  NoSQL  databases   •  Data  integra<on   •  Data  quality  and   governance   Vendors  include   Data  Stax,  IBM,   Informa<ca,   Syncsort   •  Analy<c   development   pla[orms   •  Advanced   analy<cs   applica<ons   •  Data  visualisa<on   tools   •  Business   intelligence   applica<ons     Vendors  include   Karmasphere,   Tresata,  Datameer,   SAS  Ins<tute,   Tableau,  Revolu<on   Analy<cs   •  Consul<ng   •  Training   •  SoWware   maintenance   •  Hardware   maintenance   •  Hos<ng/cloud     Vendors  include   Think  Big  Analy<cs,   Amazon  Web   Services,  Accenture,   as  well  as  services   associated  with   enterprise   distribu<ons  (e.g.   Cloudera).     Next  Genera.on  Data  Warehouse     •  MPP,  columnar  data  warehouse   appliances   •  In-­‐memory  analy<cs  engines     Vendors  include  EMC  Greenplum,   HP  Ver<ca,  Teradata  Aster  Data,   IBM  Netezza,  SAP,  MicrosoW,   Kognito   Source:  Wikiban   June  2014  
  • 7. Almost  40%  of  the  market  is  held  by  8  companies  and  they   supply  mainly  hardware     Market  segmenta.on     7   Big  Data  revenue  split  by  type  compiled  by  Wikibon.org,  2012     Source:  Wikibon,  companies  data   0   500   1,000   1,500   2,000   2,500   IBM   HP   Teradata   Dell   Oracle   SAP   EMC   Cisco   MicrosoW   Accenture   Fusion-­‐io   PwC   SAS  Ins<tute   Splunk   Palan<r   Deloiee   Amazon   NetApp   Hitachi   Opera  Solu<ons   Mu  Sigma   TCS   Intel   MarkLogic   Booz  Allen  Hamilton   Cloudera   Ac<an   SGI   Capgemini   1010data   Orginal  Device  Manufacturers   Others     June  2014   Top  8  players  holding   40%  market  share   but  big  data   revenues  are  s<ll  1%   or  less  of  their  overall   annual  revenues     • Leading  IBM  offers  the  largest  product   and  services  por[olio  and  is  one  of  the   biggest  promoters  of  Big  Data.     • Second  revenue  generator  in  2012,  HP,   made  money  from  from  Big  Data-­‐related   services,  followed  by  sales  of  hardware  to   support  Big  Data  deployments.  HP  by  its   sheer  size  is  in  a  posi<on  to  impact  and   par<cipate  in  a  number  of  Big  Data   deployments.   • Others,  combina<on  of   hundreds  of  exis<ng  and   start-­‐ups,  will  be  the  most   dynamic  contributors   group  to  the  big  data   companies.       • The  mix  of  big  data   technology  developers  and   big  data  service  providers   will  be  changing.  Any   company  involved  in  data   gathering,  and  using  latest   analy<cal  tools  can  call   themselves  big  data   company.  That  will  have   an  impact  on  exis<ng   industry  of  market   research  which  will  be   under  pressure  to  either     transform  or  join  big  data   market.    
  • 8. 8   There  are  opportuni.es  for  different  type  of  players,  new   and  exis.ng,  to  make  inroads  into  big  data     Market  segmenta.on     Big  data  produc?on   Big  data   management   Big  data   consump?on   Source.  CM  Research     •  Social  media   •  Documents   •  Databases   •  Web  crawlers   •  Web  robots     •  Sensors   •  Voice   •  Music  &  video   •  Email   •  RFID   •  Call  records   •  Payment  details   •  GPS   Volume   Velocity   Variety   Storage   Big  Data   quality     Security   Analy.cs   Databases   Data  mining   Search   Digital  marke.ng   Re-­‐selling   June  2014  
  • 9. Big  data  is  the  fastest  growing  market  since  the  discovery  of  the   Internet   Market  size  and  forecast     9   0   10   20   30   40   50   60   2011   2012   2013   2014   2015   2016   2017   Source:  Wikiban,  IDC,  IBM;    Bspot  analysis   Market  revenues  and  forecast  for  Big  Data,  2011-­‐2017     USD  Billion     7.2   11.4   18.2   28.0   37.9   43.7   47.8   31%  growth  CAGR   61%  annual  growth   June  2014   An  es<mated  total  value  of  big  data  including   revenues  coming  from  the  sale  of  hardware,   soWware  and  services  but  also  revenues  coming   from  the  value  big  data  tools  have  been   genera<ng.       An  es<mated  l  value  of  big  data  including   revenues  coming  from  the  sale  of  hardware,   soWware  and  services.     Growth  driven  by  increasingly  more  adopters   beyond  Web    star<ng  using  big  data  tools  not   only  retailers  but  also  pharma,  energy,  financial   services.       More  investment  being  poured  into  big  data   technology  especially  by  larger  companies  like   Google,  Facebook  and  Amazon  driving  the   prices  dawn  and  allowing  the  access  to  big  data   tools  to  wider  customer  base.     The  technology  of  big  data  is  maturing,   especially  soWware  like    Hadoop,  NoSQL  data   stores,  in-­‐memory  analy<c  engines  and   analy<c  databases.    
  • 10. Key  growth  factors  include:  matura.on  of  sobware,  growing   awareness  of  benefits,  growth  in  investment     Market  size  and  forecast   10  June  2014     •  Increased    awareness  of  the  benefits  of  Big  Data  as   applied  to  industries  beyond  the  Web,  esp.  financial   services,  pharmaceu<cals,  and  retail.     •  Matura<on  of  Big  Data  soWware  such  as  Hadoop,   NoSQL  data  stores,  in-­‐memory  analy<c  engines,  and   massively  parallel  processing  analy<c  databases   •  Industries  will  start  using  big  data  analy<cs  more   frequently  and  they  will  increase  the  level  of   decision-­‐making  process  on  it  following  beeer   understanding  of  the  services  provided  by  big  data   vendors.     •  Following  first  wave  of  big  infrastructure   investments  coming  from  big  companies  and   organisa<ons  there  should  be  a  second  wave  of   investment  boost  coming  from  non-­‐IT  companies.       •  Smart  devices  including  computers,  smart  phones   but  also  smart  devices  used  by  industries  e.g.  smart   meters,  sensors,  etc.  will  drive  faster  adop<on  of  big   data  usage.     It  will  help  to  grow:   It  will  con?nue  to  be  a  challenge:   •  Data  is  moving  from  structured  to  unstructured  format,  raising   the  costs  of  analysis.  This  creates  a  highly  lucra<ve  market  for   analy<cal  search  engines  that  can  interpret  this  unstructured   data.   •  Proprietary  database  standards  are  giving  way  to  new,  open   source  big  data  technology  pla[orms  such  as  Hadoop.  This  means   that  barriers  to  entry  may  remain  low  for  some  <me.   •  Many  corpora<ons  are  op<ng  to  use  cloud  services  to  access  big   data  analy<cal  tools  instead  of  building  expensive  data   warehouses  themselves.  This  implies  that  most  of  the  money  in   big  data  will  be  made  from  selling  hybrid  cloud-­‐based  services   rather  than  selling  big  databases.   •  In  future,  a  growing  propor<on  of  big  data  will  be  generated  from   machine  to  machine  (M2M)  using  sensors.  M2M  data,  much  of   which  is  business-­‐cri<cal  and  <me-­‐sensi<ve,  could  give  telecom   operators  a  way  to  profit  from  the  big  data  boom.   •  Legisla<on    issues  including  privacy  concerns,  data  security  and     intellectual  property  rights  are  s<ll  unresolved  and  it  will  need  to   be  regulated  and  cross-­‐regional  and  global  standards  will  have  to   be  introduced.     Source:  Wikiban,  IDC,  IBM;    Bspot  analysis  
  • 11. Currently  hardware  suppliers  are  the  biggest  revenue  generators,   but  sobware  and  services  are  the  future  winners   Market  size  and  forecast   11   34%   22%  16%   8%   8%   5%   3%   2%   2%   Professional  services     Compute   Storage   SQL   Applica<ons   XaaS   Networking   NoSQL   Infrastructure  soWware   39%   41%   20%   Services     Hardware   SoWware   Big  Data  sobware  and  services  revenue  split,  2013     Big  Data  revenue  split  by  type,  2013     Source:  Wikiban,  IDC,   IBM;  2013   June  2014   Hardware  sales  will  con<nue  enjoying  good  market  condi<ons   in  the  short  to  medium  term.  Once  large  players  will  sa<sfied   their  needs  for  inves<ng  in  big  data  infrastructure,  there  will  be   smaller  players  and  companies  from  other  non-­‐IT  industries   needing  hardware  for  building  big  data  internal  capabili<es.       At  the  same  <me  soWware  and  services  providers  will  con<nue   to  grow  and  in  the  long  term  they  will  increase  in  its   significance  over  hardware  which  will  eventually  commodi<zed.   According  to  Wikibon  analysis,  vendors  will  con<nue  using   NoSQL  and  in-­‐memory  database  soWware,  streaming  analy<c   pla[orms,  ver<cally  focused  analy<cal  and  transac<onal   applica<ons  and  applica<on  development  pla[orms  (both  on-­‐ premise  and  Cloud-­‐based)  and  associated  consul<ng  and   professional  services  to  address  specific,  high-­‐value  business   problems  and  opportuni<es.  
  • 12. Industries  focusing  on  consumer  needs  like  retail,  banking,   telecoms  are  the  first  to  use  big  data  tools   Demand  analysis   12   1   10   5   2018   2012  2015   year   Electronics  and  computers   Telecommunica.on     Healthcare    U.li.es       Media     On-­‐line  services     Retail   Public  services   Professional  services     Financial  services     Defense  and  Police   Manufacturing     Transporta.on     Automo.ve       Educa.on       Travel       First  adopters   Laggards   Source:  Bspot  analysis   Natural  resources     Construc.on    Sport       Airline   June  2014   Level  of  adop.on    
  • 13. In  the  future,  it  will  be  industries  driving  the  big  data   development,  not  IT  companies  (1/3)   Demand  analysis   13   Financial   services   Healthcare   Retail   June  2014   •  About  70%  of  the  industry  is  already  using  big  data  and  analy<cs.  For  example  big  data  has  been  used  for  a  long  <me  in  the  trading  industry.   In  fact,  using  mathema<cal  algorithms  for  lots  of  data  analy<cs  is  traders  specialism  but  also  great  trading  secret.       •  Banks  and  financial  services  firms  are  also  turning  to  big  data,  using  insights  pulled  out  of  daily  transac<ons,  market  feeds,  customer  service   records,  loca<on  data,  and  click  streams  to  carve  out  new  business  models  and  services  and  transform  how  they  go  to  market.  They  also   using  big  data  to  focus  on  opera<onal  issues  –  risk,  efficiency,  compliance,  security  and  making  beeer  decisions.  Some  of  the  ideas  financial   services  firms  can  use  big  data  for:  personalised  services,  loan  decisions  support,  improve  customer  loyalty,  op<mize  return  on  equity,   combat  fraud  and  mi<gate  opera<onal  risk,  iden<fy  new  revenue  streams.     •  Walmart  pioneered  the  use  of  big  data  to  improve  opera<onal  efficiency  in  the  retail  industry  well  before  the  term  big  data  even  existed.   The  company  streamlined  its  complex  supply  chain  to  take  advantage  of  economies  of  scale,  thus  limi<ng  excess  inventory  and  reducing   associated  costs.    Than,  the  retailer  passed  on  some  of  these  big  data-­‐enabled  savings  to  customers  in  the  form  of  low  prices  undercut  the   retailer's  compe<<on.   •  Retailers,  service  companies  and  consumer  goods  producers  are  the  most  hungry  of  big  data  intelligence  on  their  customers.  Big  data   analysis  are  used  for  customers’  segmenta<on,  marke<ng  to  enhance  customers  reten<on  and  understanding  demand  for  new  products   and  services.  Dynamic  price  op<miza<on,  video-­‐enabled  store  layout  and  product  placement  analysis,  staffing  analysis  and  decision   support,  suppliers  analysis  and  op<miza<on  of  supply  <ming,  pricing  and  sourcing,  knowledge  of  customers'  buying  paeerns  and  behavior   are  addi<onal  ways  how  retails  can  capitalise  on  big  data  input.     • The  pharmaceu<cal  industry  began  mining  and  aggrega<ng  sales  and  prescrip<on  data  because  this  lever  helped  companies  improve  their   boeom  line  by  more  effec<vely  targe<ng  sales,  managing  sales  force  resources,  and  selec<ng  prime  areas  for  R&D.  A  number  of  pharma   companies    are  already  using  big  data,  among  them,  Bristol  Myers  Squibb.  BMS  has  spent  nearly  $46  billion  on  research  and  development   since  1997,  indexes  hundreds-­‐of-­‐thousands  of  clinical  documents  per  year  in  pursuit  of  insights  that  will  improve  the  drug  discovery   process.  BMS  is  using  soWware  from  HP    to  analyze  research  and  market  data  to  be  used  by  clinical  researchers  and  scien<sts.     • For  medical  devices  manufacturers  big  data  pla[orms  can  become  substan<ally  more  intelligent  by  including  modules  that  use  image   analysis  and  recogni<on  in  databases  of  medical  images  (X-­‐ray,  CT,  MRI)  for  pre-­‐diagnosis  or  that  automa<cally  mine  medical  literature  to   create  a  medical  exper<se  database  capable  of  sugges<ng  treatment  op<ons  to  physicians  based  on  pa<ents’  medical  records.  In  addi<on,   clinical  decision  support  systems  can  enable  a  larger  por<on  of  work  to  flow  to  nurse  prac<<oners  and  physician  assistants  by  automa<ng   and  facilita<ng  the  physician  advisory  role  and  thereby  improving  the  efficiency  of  pa<ent  care.     • Public  health  can    benefit  enormously  from  big  data.  Wider  variety  of  health  care  informa<on,  making  them  more  informed  consumers  of   the  medical  system.  Pa<ents  could  be  able  to  compare  not  only  the  prices  of  drugs,  treatments,  and  physicians  but  also  their  rela<ve   effec<veness,  enabling  them  to  choose  more  effec<ve,  beeer-­‐targeted  medicines,  many  customized  to  their  personal  gene<c  and   molecular  makeup.  Pa<ents  could  also  have  access  to  a  wider  range  of  informa<on  on  epidemics  and  other  public  health  informa<on   crucial  to  their  well-­‐being.    
  • 14. In  the  future,  it  will  be  industries  driving  the  big  data   development,  not  IT  companies  (2/3)   Demand  analysis   14   Public   sector     U?li?es     Educa?on   Telecos   June  2014   • Intelligent  use  of  smart  meter  data  will  allow  u<li<es  companies  to:  beeer  monitor  and  forecast  energy  consump<on  paeerns;  iden<fy   inefficient  energy  use  at  both  the  macro  and  household  levels;  accurately  predict  poten<al  power  outages  and  equipment  failures  before  they   occur;  improve  customer  segmenta<on  and  tailor  service  offerings  based  on  customer  behavior.     • Smart  grids  will  be  the  next  step  of  managing  energy  informa<on  but  start  grids  are  s<ll  not  common  yet,  IT  companies  need  to  get  started  to   collaborate  with  u<li<es  now.  The  level  of  sophis<ca<on  in  managing  and  analysing  data  from  smart  grids  is  even  higher.  Apart  from  smart   meters  data  there  will  also  will  be  grids  data,  energy  distribu<on  data,  IT  databases  data  and  others.     • Addi<onally,  u<li<es  are  already  able  to  use  data  about  their  customers  to  offer  beeer  or  new  services,  reduce  customers’  churn,  brand   monitoring    and  even  support  machine  performance  monitoring  and  supervision.     • EDF  Energy,  using  SAS  big  data  pla[orm,  has  created  a  dedicated  analy<cs  func<on  to  focus  on  key  areas  including  customer  segmenta<on,   churn  assessment,  probability  modeling  and  product  placement  modeling.     • Governments  have  lots  of  data  available  and  its  wise  usage  can  be  beneficial  for  the  administra<on  as  well  as  ci<zens.  Big  data  used  by   governments  will  enable  people  to  make  beeer  choices  about  the  public  services  they  use  and  to  hold  government  to  account  on  spending   and  outcomes.     • Big  Data  is  also  providing  the  raw  material  for  innova<ve  new  business  ventures  and  for  public  service  professionals.     • According  to  the  UK  free  market  think  thank  Policy  Exchange,  the  UK  government  could  save  up  to  £33  billion  a  year  by  using  public  big  data   more  effec<vely.  McKinsey  has  inves<gated  that  the  poten<al  annual  value  to  Europe’s  public  sector  thanks  to  big  data  is  250  billion  Euro.     • Educa<on  has  always  had  the  capacity  to  produce  a  tremendous  amount  of  data,  more  than  maybe  any  other  industry.  The  benefits  range   from  more  effec<ve  self-­‐paced  learning  to  tools  that  enable  instructors  to  pinpoint  interven<ons,  create  produc<ve  peer  groups,  and  free  up   class  <me  for  crea<vity  and  problem  solving.  Big  data  could  enable  customized  modules,  assignments,  feedback  and  learning  trees  in  the   curriculum  that  will  promote  beeer  and  richer  learning,  customise  courses  and  even    big  data  can  be  used  in  admissions,  budge<ng  and   student  services  to  ensure  transparency,  beeer  distribu<on  of  resources  and  iden<fica<on  of  at-­‐risk  students.   • Telcos  already  have  the  customer  profile  data  with  demographics  informa<on  (age,  income,  gender,  profession,  etc.),  subscriber  usage  and   loca<on.  The  simple  thing  is  to  put  together  the  knowledge  of  the  customer  and  proac<ve  customer  service:  offer  with  renewing  contract   ahead  of  expira<on,  roaming  discounts  ahead  of  foreign  travel,  etc.  Basically,  the  amount  of  data  hold  by  telcos  on  their  customers  is  a   marke<ng  goldmine  and  apart  from  helping  to  increase  revenues  it  will  also  support  to  reduce  subscribers’  churn,  control  cost  of  acquisi<on   simula<on  tools,  reduce  opera<ng  costs,  help  with  fraud  detec<on,  help  products  improvements  and  tailor  upon  customers’  needs  in  real   <me,  etc.    
  • 15. In  the  future,  it  will  be  industries  driving  the  big  data   development,  not  IT  companies  (3/3)   Demand  analysis   15   Manufacturing  Avia?on  Automo?ve  Professional   services   June  2014   • Thanks  to  advanced  analy<cs  of  all  customer  transac<onal  data  and  external  data  sources  (e.g.  social  media),  automakers  will  be  able  to   make  improvements  in  customer  acquisi<on,  customer  reten<on  and  manage  beeer  return  on  marke<ng  investment.  Addi<onally,  the   automo<ve  sector  is  able  to  use  big  data  for  op<mizing  supply  chains,  predict/an<cipate  maintenance;  connec<ng  data  from  the  vehicles,   or  the  devices  they  integrate  with,  to  relay  informa<on  from  vehicle  to  vehicle  (V2V),  and  vehicle  to  infrastructure  (V2I)  too;  GPS  and   Satellite  Naviga<on  systems  performing  in  real  <me,  etc.     • Big  data  offers  significant  inroads  for  making  cars  safer  –  mostly  through  its  ability  to  automate  func<onality.  On  board  vehicle  systems  can   now  inform  each  other  of  their  whereabouts  and  of  other  hazards  in  the  road  so  that  drivers  can  avoid  collisions.   • Google's  self-­‐drive  car  is  an  example  of  using  big  data  in  automo<ve  to  use  external  and  internal  data  for  this  inven<on.     • By  analysing  data  created  by  jet  engines  and  sensors  that  collect  data  on  the  surrounding  environment  (temperature,  humidity,  air   pressure,  etc.),  service  providers  are  able  to  predict  when  various  parts  are  likely  to  fail  and  take  preventa<ve  maintenance  ac<on.   Replacing  a  soon-­‐to-­‐fail  part  before  it  malfunc<ons  is  significantly  less  costly  than  doing  so  aWer  the  part  fails  during  opera<ons.  More   efficient  jet  engines  consume  less  fuel  and  emit  fewer  environmentally  contamina<ng  gasses.   • Other  advantages  of  using  big  data  tool  by  avia<on  are:  preventa<ve  maintenance  reduces  aircraW  “down  <me”  ,  improved  customer   sa<sfac<on,  <cket  pricing  predic<ons  and  others.     • New  revenue  genera<on  tools.  Bri<sh  Airways  for  its  new  personalized  service  and  offers  program,  Know  Me.  It  collects  and  tracks  an  usual   amount  of  data  on  individual  passengers,  their  preferences  and  travel  history.  Data  on  the  online  behavior  and  buying  habits  of  20  million   Bri<sh  Airways  customers,  crea<ng  hundreds  of  predic<ve  signals  that  suggest  a  person’s  “behavioral  DNA  to  offer  new  services.     • Big  data  can  help  manufacturers  reduce  product  development  <me  by  20  to  50  percent  and  eliminate  defects  prior  to  produc<on  through   simula<on  and  tes<ng.  That  a  massive  saving  for  the  R&D  process.     • Manufacturers  could  capture  a  significant  big  data  opportunity  to  create  more  value  by  ins<tu<ng  product  lifecycle  management.  Designers   and  manufacturing  engineers  can  share  data  and  quickly  and  cheaply  create  simula<ons  to  test  different  designs.  Big  data  can  help  with   further  improvements  in  product  quality,  use  real-­‐<me  data  from  sensors  to  track  parts,  monitor  machinery,  and  guide  actual  opera<ons.     • Taking  inputs  from  product  development  and  historical  produc<on  data  (e.g.,  order  data,  machine  performance),  manufacturers  can  apply   advanced  computa<onal  methods  to  create  a  digital  model  of  the  en<re  manufacturing  process.     • First  adopters  are  management  consultancy  and  market  research  companies  to  replace  manual  data  mining  to  speed  up  analyst  work  in   order  to  focus  more  on  analy<cs  and  value  to  the  clients  rather  than  data  provider.     • Legal  firms    and  accountancy  companies  are  known  to  be  tradi<onal  and  slow  with  implemen<ng  technologies.  On  the  other  hand  they   collect  and  store  massive  amount  of  data  and  their  services  are  also  based  on  finding  the  right  data  and  correctly  apply.  Introducing  big  data   tools  will  help  them  with  overall  performance,  speed  and  accuracy.    
  • 16. Spot  on…  what  you  need  to  take  away   16  June  2014   For  vendors:   §  To    mone<se  your  innova<ons  and  solu<ons,  transform  your  big  data  concepts  into  value  proposi<ons   that  are  based  on  ac<onable  insights  that  drive  revenue  and/  or  reduce  costs  for  your  customers.     §  Integrate  big  data  from  structured,  mul<-­‐structured  and  unstructured  data  from  various  (internal  and   external)  source  system  together  in  a  common  pla[orm.   §  Put  safeguards  in  place  to  address  public  concerns  about  big  data,  including,  but  not  limited  to,  privacy,   security,  intellectual  property,  and  liability.   For  companies:   §  Manage  big  data  as  a  corporate  asset  and  educate  employees  on  how  to  iden<fy  business  requirements   for  big  data  projects  and  effec<vely  communicate  insights  extracted  from  big  data  to  the  business.   §  Trust  big  data  input  and  make  analy<cs-­‐driven  decision  rather  than  follow  “gut  ins<nct”.     §  Protect  compe<<vely  sensi<ve  data  or  other  data  that  should  be  kept  private  or  corporate  secret.    
  • 17. www.bspotconsulting.com