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>	
  Small	
  vs.	
  Big	
  Data	
  <	
  
  What	
  the	
  heck?	
  What	
  does	
  it	
  all	
  
  mean	
  and	
  how	
  does	
  it	
  help	
  me?	
  
>	
  Smart	
  data	
  driven	
  marke5ng	
  
                        “Using	
  data	
  to	
  widen	
  the	
  funnel”	
  

                   Media	
  A8ribu5on	
  &	
  Modeling                        	
  

                       Op5mise	
  channel	
  mix,	
  predict	
  sales	
  

                    Targe5ng	
  &	
  Merchandising	
  	
  
                        Increase	
  relevance,	
  reduce	
  churn	
  

                       Tes5ng	
  &	
  Op5misa5on	
  
                           Remove	
  barriers,	
  drive	
  sales	
  

                                 Boos5ng	
  ROI	
  
June	
  2012	
                         ©	
  Datalicious	
  Pty	
  Ltd	
              2	
  
Twi8er	
  @datalicious	
  


June	
  2012	
              ©	
  Datalicious	
  Pty	
  Ltd	
     3	
  
>	
  Wikipedia:	
  Big	
  data	
  
In	
  informaAon	
  technology,	
  big	
  data	
  consists	
  of	
  datasets	
  
that	
  grow	
  so	
  large	
  that	
  they	
  become	
  awkward	
  to	
  
work	
  with	
  using	
  on-­‐hand	
  database	
  management	
  tools.	
  
DifficulAes	
  include	
  capture,	
  storage,	
  search,	
  sharing,	
  
analyAcs,	
  and	
  visualizing.	
  	
  
	
  
This	
  trend	
  conAnues	
  because	
  of	
  the	
  benefits	
  of	
  working	
  
with	
  larger	
  and	
  larger	
  datasets	
  allowing	
  analysts	
  to	
  
spot	
  business	
  trends,	
  prevent	
  diseases,	
  combat	
  crime.	
  	
  
	
  
Though	
  a	
  moving	
  target,	
  current	
  limits	
  are	
  on	
  the	
  order	
  
of	
  terabytes,	
  exabytes	
  and	
  zeMabytes	
  of	
  data.	
  
June	
  2012	
                     ©	
  Datalicious	
  Pty	
  Ltd	
                 4	
  
Big	
  data	
  =	
  bo8lenecks	
  


June	
  2012	
                  ©	
  Datalicious	
  Pty	
  Ltd	
     5	
  
>	
  Big	
  data	
  analy5cs	
  bo8lenecks	
  




   Fast	
  laptops	
  now	
  have	
  up	
  to	
  8GB	
  
   of	
  RAM,	
  that	
  means	
  you	
  can	
  
   compute	
  up	
  to	
  6GB	
  of	
  raw	
  data	
  
   very	
  fast	
  in	
  memory	
  thus	
  bypassing	
  
   the	
  biggest	
  boMleneck:	
  I/O	
  
June	
  2012	
                         ©	
  Datalicious	
  Pty	
  Ltd	
     6	
  
>	
  Power	
  vs.	
  distributed	
  compu5ng	
  




   Adding	
  more	
  supercomputers	
  is	
  
   difficult	
  as	
  they	
  are	
  complex	
  and	
  
   expensive	
  but	
  adding	
  machines	
  to	
  
   a	
  distributed	
  compuAng	
  network	
  	
  
   is	
  fairly	
  cheap	
  and	
  ‘easy’.	
  	
  
June	
  2012	
                        ©	
  Datalicious	
  Pty	
  Ltd	
     7	
  
Big	
  data	
  =	
  hype?	
  


June	
  2012	
               ©	
  Datalicious	
  Pty	
  Ltd	
     8	
  
>	
  Importance	
  of	
  research	
  experience	
  
 The	
  consumer	
  decision	
  process	
  is	
  changing	
  from	
  linear	
  to	
  circular.	
  




 Considera5on	
  	
  
 set	
  now	
  grows	
  
 during	
  (online)	
                                             (Online)	
  Research	
  	
  
 research	
  phase	
  
 which	
  increases	
  
 importance	
  of	
  
 user	
  experience	
  
 during	
  that	
  phase	
  

June	
  2012	
                               ©	
  Datalicious	
  Pty	
  Ltd	
                        9	
  
>	
  The	
  consumer	
  data	
  journey	
  
   To	
  transac5onal	
  data	
                                               To	
  reten5on	
  messages	
  




   From	
  suspect	
  to	
               prospect	
                                        To	
  customer	
  
                     Time   	
                                                          Time   	
  




   From	
  behavioural	
  data	
                                          From	
  awareness	
  messages	
  

June	
  2012	
                       ©	
  Datalicious	
  Pty	
  Ltd	
                                       10	
  
>	
  Single	
  customer	
  view	
  is	
  key	
  

            Website	
  behavioural	
  data	
  




             Campaign	
  response	
  data	
  
                                                            +	
                            The	
  whole	
  is	
  greater	
  	
  
                                                                                         than	
  the	
  sum	
  of	
  its	
  parts	
  




                   Customer	
  profile	
  data	
  



June	
  2012	
                                      ©	
  Datalicious	
  Pty	
  Ltd	
                                                    11	
  
>	
  Maximise	
  iden5fica5on	
  points	
  	
  
160%	
  

140%	
  

120%	
  

100%	
  

  80%	
  

  60%	
  
                                                             −−−	
  Probability	
  of	
  idenAficaAon	
  through	
  Cookies	
  
  40%	
  

  20%	
  
                   0	
     4	
     8	
     12	
     16	
         20	
          24	
         28	
     32	
     36	
     40	
     44	
     48	
  

                                                                             Weeks	
  

June	
  2012	
                                                ©	
  Datalicious	
  Pty	
  Ltd	
                                                    12	
  
>	
  Tradi5onal	
  single	
  customer	
  view	
  

           Website	
  	
            Vendor	
  	
                                                                Reports	
  and	
  
                                                                                      Repor5ng	
  
            data	
                data	
  feed	
  #1	
                                                          dashboards	
  
                                                                                   data	
  warehouse	
  




         Call	
  center	
  	
       Vendor	
  	
                                                                  Targeted	
  
                                                                                     Transac5on	
  	
  
            data	
                data	
  feed	
  #2	
                                                           campaigns	
  
                                                                                   data	
  warehouse	
  




          Customer	
  	
            Vendor	
  	
                                       Data	
  import	
  	
  
            data	
                data	
  feed	
  #3	
                                (ETL)	
  process	
  




June	
  2012	
                                         ©	
  Datalicious	
  Pty	
  Ltd	
                                              13	
  
>	
  Tradi5onal	
  single	
  customer	
  view	
  
 Challenge	
  #3:	
  	
  
 Increasing	
  number	
  
         Website	
  	
                  Vendor	
  	
                                                                Reports	
  and	
  
                                                                                          Repor5ng	
  
 of	
  (unstructured)	
  
          data	
                      data	
  feed	
  #1	
                             data	
  warehouse	
  
                                                                                                                    dashboards	
  

 data	
  sources	
  

                                                                                   Challenge	
  #1:	
  	
  
                                                                                   Rigid	
  database	
  
         Call	
  center	
  	
           Vendor	
  	
                                                                  Targeted	
  
            data	
                    data	
  feed	
  #2	
                         schema	
  requires	
  
                                                                                       Transac5on	
  	
  
                                                                                                                     campaigns	
  
                                                                                     data	
  warehouse	
  
                                                                                   extensive	
  planning	
  
                                                                                   and	
  maintenance	
  

                                  Challenge	
  #2:	
  	
  
          Customer	
  	
          Data	
  feeds	
  require	
  
                                         Vendor	
  	
                                      Data	
  import	
  	
  
            data	
                constant	
  u3	
  
                                    data	
  feed	
  # pdates	
                            (ETL)	
  process	
  

                                  and	
  maintenance	
  

June	
  2012	
                                             ©	
  Datalicious	
  Pty	
  Ltd	
                                              14	
  
>	
  Splunk	
  single	
  customer	
  view	
  
                                     SuperTag	
                                                                 Splunk	
  saved	
  
           Website	
  	
  
                                  integra5on	
  for	
                                                           searches	
  and	
  
            data	
  
                                  real-­‐5me	
  data	
                                                           dashboards	
  




                                     Splunk	
  	
                                                               Splunk	
  regex	
  
         Call	
  center	
  	
                                                      Splunk	
  instance	
  	
  
                                  Forwarder	
  for	
                                                             builder	
  and	
  	
  
            data	
                                                                  on	
  dedicated	
  	
  
                                   data	
  import	
                                                             data	
  exports	
  
                                                                                     AWS	
  server	
  




                                                                                    3rd	
  party	
  data	
         3rd	
  party	
  	
  
          Customer	
  	
  
                                                                                     mining	
  and	
              campaign	
  
            data	
  
                                                                                      repor5ng	
                  execu5on	
  




June	
  2012	
                                        ©	
  Datalicious	
  Pty	
  Ltd	
                                                    15	
  
>	
  Key	
  Splunk	
  advantages	
  
§  Powerful	
  data	
  mining	
  
           –  Structured	
  and	
  unstructured	
  data	
  
§  Easy	
  sharing	
  of	
  insights	
  
           –  Online	
  dashboards	
  and	
  reports	
  
§  Short	
  project	
  duraAon	
  
           –  Quick	
  implementaAon	
  and	
  1st	
  insights	
  
§  IntegraAon	
  with	
  other	
  plaeorms	
  
           –  Regex	
  builder	
  and	
  data	
  extracts	
  
§  Low	
  technology	
  and	
  resource	
  costs	
  
           –  ImplementaAon	
  and	
  maintenance	
  
June	
  2012	
                           ©	
  Datalicious	
  Pty	
  Ltd	
     25	
  
Contact	
  us	
  
                   cbartens@datalicious.com	
  
                              	
  
                        Learn	
  more	
  
                      blog.datalicious.com	
  
                                	
  
                          Follow	
  us	
  
                    twi8er.com/datalicious	
  
                              	
  
June	
  2012	
               ©	
  Datalicious	
  Pty	
  Ltd	
     26	
  
Data	
  >	
  Insights	
  >	
  Ac5on	
  

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Big Data Challenges

  • 1. >  Small  vs.  Big  Data  <   What  the  heck?  What  does  it  all   mean  and  how  does  it  help  me?  
  • 2. >  Smart  data  driven  marke5ng   “Using  data  to  widen  the  funnel”   Media  A8ribu5on  &  Modeling   Op5mise  channel  mix,  predict  sales   Targe5ng  &  Merchandising     Increase  relevance,  reduce  churn   Tes5ng  &  Op5misa5on   Remove  barriers,  drive  sales   Boos5ng  ROI   June  2012   ©  Datalicious  Pty  Ltd   2  
  • 3. Twi8er  @datalicious   June  2012   ©  Datalicious  Pty  Ltd   3  
  • 4. >  Wikipedia:  Big  data   In  informaAon  technology,  big  data  consists  of  datasets   that  grow  so  large  that  they  become  awkward  to   work  with  using  on-­‐hand  database  management  tools.   DifficulAes  include  capture,  storage,  search,  sharing,   analyAcs,  and  visualizing.       This  trend  conAnues  because  of  the  benefits  of  working   with  larger  and  larger  datasets  allowing  analysts  to   spot  business  trends,  prevent  diseases,  combat  crime.       Though  a  moving  target,  current  limits  are  on  the  order   of  terabytes,  exabytes  and  zeMabytes  of  data.   June  2012   ©  Datalicious  Pty  Ltd   4  
  • 5. Big  data  =  bo8lenecks   June  2012   ©  Datalicious  Pty  Ltd   5  
  • 6. >  Big  data  analy5cs  bo8lenecks   Fast  laptops  now  have  up  to  8GB   of  RAM,  that  means  you  can   compute  up  to  6GB  of  raw  data   very  fast  in  memory  thus  bypassing   the  biggest  boMleneck:  I/O   June  2012   ©  Datalicious  Pty  Ltd   6  
  • 7. >  Power  vs.  distributed  compu5ng   Adding  more  supercomputers  is   difficult  as  they  are  complex  and   expensive  but  adding  machines  to   a  distributed  compuAng  network     is  fairly  cheap  and  ‘easy’.     June  2012   ©  Datalicious  Pty  Ltd   7  
  • 8. Big  data  =  hype?   June  2012   ©  Datalicious  Pty  Ltd   8  
  • 9. >  Importance  of  research  experience   The  consumer  decision  process  is  changing  from  linear  to  circular.   Considera5on     set  now  grows   during  (online)   (Online)  Research     research  phase   which  increases   importance  of   user  experience   during  that  phase   June  2012   ©  Datalicious  Pty  Ltd   9  
  • 10. >  The  consumer  data  journey   To  transac5onal  data   To  reten5on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages   June  2012   ©  Datalicious  Pty  Ltd   10  
  • 11. >  Single  customer  view  is  key   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data   June  2012   ©  Datalicious  Pty  Ltd   11  
  • 12. >  Maximise  iden5fica5on  points     160%   140%   120%   100%   80%   60%   −−−  Probability  of  idenAficaAon  through  Cookies   40%   20%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks   June  2012   ©  Datalicious  Pty  Ltd   12  
  • 13. >  Tradi5onal  single  customer  view   Website     Vendor     Reports  and   Repor5ng   data   data  feed  #1   dashboards   data  warehouse   Call  center     Vendor     Targeted   Transac5on     data   data  feed  #2   campaigns   data  warehouse   Customer     Vendor     Data  import     data   data  feed  #3   (ETL)  process   June  2012   ©  Datalicious  Pty  Ltd   13  
  • 14. >  Tradi5onal  single  customer  view   Challenge  #3:     Increasing  number   Website     Vendor     Reports  and   Repor5ng   of  (unstructured)   data   data  feed  #1   data  warehouse   dashboards   data  sources   Challenge  #1:     Rigid  database   Call  center     Vendor     Targeted   data   data  feed  #2   schema  requires   Transac5on     campaigns   data  warehouse   extensive  planning   and  maintenance   Challenge  #2:     Customer     Data  feeds  require   Vendor     Data  import     data   constant  u3   data  feed  # pdates   (ETL)  process   and  maintenance   June  2012   ©  Datalicious  Pty  Ltd   14  
  • 15. >  Splunk  single  customer  view   SuperTag   Splunk  saved   Website     integra5on  for   searches  and   data   real-­‐5me  data   dashboards   Splunk     Splunk  regex   Call  center     Splunk  instance     Forwarder  for   builder  and     data   on  dedicated     data  import   data  exports   AWS  server   3rd  party  data   3rd  party     Customer     mining  and   campaign   data   repor5ng   execu5on   June  2012   ©  Datalicious  Pty  Ltd   15  
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  • 25. >  Key  Splunk  advantages   §  Powerful  data  mining   –  Structured  and  unstructured  data   §  Easy  sharing  of  insights   –  Online  dashboards  and  reports   §  Short  project  duraAon   –  Quick  implementaAon  and  1st  insights   §  IntegraAon  with  other  plaeorms   –  Regex  builder  and  data  extracts   §  Low  technology  and  resource  costs   –  ImplementaAon  and  maintenance   June  2012   ©  Datalicious  Pty  Ltd   25  
  • 26. Contact  us   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twi8er.com/datalicious     June  2012   ©  Datalicious  Pty  Ltd   26  
  • 27. Data  >  Insights  >  Ac5on