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Finding	
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  Communica-ng	
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  –	
  Lesson	
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Ray	
  Poynter,	...
Finding	
  and	
  Communica-ng	
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  Story	
  –	
  Lesson	
  4	
  of	
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  Data	
  
Ray	
  Poynter,	...
Finding	
  and	
  Communica-ng	
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  –	
  Lesson	
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Ray	
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Finding	
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Ray	
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Finding	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
  Poynter,	...
Screenshot,	
  25	
  Feb,	
  2016	
  
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Basketball	
  
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25	
  Feb,	
  2016	
  
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  Lesson	
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Ray	
  Poynter,	...
The	
  POEM	
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Owned	
  Media	
  
From	
  #IPASocialWorks	
  
The	
  Interac-ons	
  in	
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From	
  #IPASocialWorks	
  
Finding	
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  –	
  Lesson	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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From	
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Finding	
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  –	
  Lesson	
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Ray	
  Poynter,	...
Finding	
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Ray	
  Poynter,	...
IBM’s	
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Finding	
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  Lesson	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Correla-on	
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Ne...
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Finding	
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Ray	
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Normalizing	
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Growth	
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Forbes:	
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Ray	
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Google	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  Story	
  –	
  Lesson	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
  the	
  Story	
  –	
  Lesson	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  –	
  Lesson	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  Lesson	
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Ray	
  Poynter,	...
Finding	
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  Lesson	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  Lesson	
  4	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  Story	
  –	
  Lesson	
  4	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  Story	
  –	
  Lesson	
  4	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  Story	
  –	
  Lesson	
  4	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  Story	
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  Lesson	
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Ray	
  Poynter,	...
Finding	
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  Communica-ng	
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  Story	
  –	
  Lesson	
  4	
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  –	
  Complex	
  Data	
  
Ray	
  Poynter,	...
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Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 1 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 2 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 3 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 4 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 5 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 6 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 7 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 8 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 9 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 10 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 11 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 12 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 13 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 14 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 15 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 16 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 17 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 18 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 19 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 20 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 21 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 22 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 23 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 24 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 25 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 26 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 27 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 28 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 29 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 30 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 31 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 32 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 33 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 34 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 35 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 36 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 37 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 38 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 39 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 40 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 41 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 42 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 43 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 44 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 45 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 46 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 47 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 48 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 49 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 50 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 51 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 52 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 53 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 54 Finding and communcating the story in complex data streams - Lesson 4 of 6 Slide 55
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Finding and communcating the story in complex data streams - Lesson 4 of 6

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Ray Poynter delivers the 4th lesson in the #NewMR webinar series on Finding and Communicating the Story in the Data. This lesson looks at Complex and Large Data Streams.

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Finding and communcating the story in complex data streams - Lesson 4 of 6

  1. 1. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Finding  and   Communica-ng  the  Story   Lesson  4  of  6   Working  with  Complex  Data  Streams   Ray  Poynter       July  2016  
  2. 2. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Series  Schedule   •  An  Introduc5on  and  Overview  -­‐  Feb  23     •  Working  with  Qualita5ve  Informa5on  –  Apr  5     •  Working  with  Quan5ta5ve  Informa5on    -­‐  May  26     •  Working  with  mul-ple  streams  &  big  data  -­‐  July  5     •  U5lizing  visualiza5on  –  Sep  13     •  Presen5ng  the  story  -­‐  Nov  8    
  3. 3. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Agenda   •  Brief  recap   •  Complex  data  and  its  implica5ons   •  Example  from  measuring  social  media   •  Working  with  big  and  complex  data   •  Strategies  for  finding  the  story  in  the  data  
  4. 4. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   The  Frameworks  Approach   1.  Define  and  frame  the  problem   –  A  problem  fully  defined  is  a  problem  half  solved   2.  Establish  what  is  already  known   –  Find  out  what  is  believed  and  what  the  expecta5ons  are   3.  Organise  the  data  to  be  analysed   –  Systema5c  checking  and  structural  procedures   4.  Apply  systema5c  analysis  processes   5.  Extract  and  create  the  story  
  5. 5. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Tradi-onal  MR  Data   ID   Q1   Q2   Q3   Q4   R1   1   2.5   01101   Fast     R2   1   3.5   11000   Green   R3   2   2.4   01110   Thursday  nights   R4   2   1.8   11011   Some5mes   R5   1   4.1   00001   In  the  net   Qualita-ve   Bricolage  
  6. 6. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016  
  7. 7. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Assembling  the  Evidence   •  Granularity?   •  Addi5ve,  complementary,  duplica5on?   •  What  is  being  missed?   •  Lags  in  availability?   •  Normalising?   •  Comparators?   •  Create  a  model  of  the  interac5ons  
  8. 8. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Examples  of  Data  Streams   •  Tracking  data  from  tradi5onal   surveys   •  Passive  behavioural  tracking   •  Google  Consumer  Surveys   •  Social  Media  analy5cs   •  Google  analy5cs   •  Web  analy5cs   •  Biometrics   •  News     •  Professional  reviews   •  Mystery  shopping   •  Leers,  calls,  emails  from   customers   •  Transac5onal  data   •  3rd  party  sources   •   Enterprise  feedback  systems  
  9. 9. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Characteris-cs  of  Data  Streams   •  Timelines  –  e.g.  monthly,  weekly,  daily,  con5nuous   •  Coverage  –  who  is  represented,  who  is  missed?   •  Richness  –  single  number,  range  of  measures,   quotes?   •  Veracity  –  e.g.  honesty,  accuracy,  persistence   •  Depth  –  one  measure  per  person  or  many   measures?  
  10. 10. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Nate  Silver  &  FiveThirtyEight  
  11. 11. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Nate  Silver  and  Elec-on  Predic-ons   •  Polling  data   –  Inclusive  approach   •  Weigh5ng   –  Recency   –  Sample  size   –  Pollster  ra5ng   –  House  effects   –  Likely  voter  adjustment   •  Trend  line  adjustment   •  Congressional  approval   •  Fundraising  totals   •  Highest  elected  office  held   •  Margin  of  win  in  most   recent  race   •  Ideology  and  State  leaning  
  12. 12. Screenshot,  25  Feb,  2016  
  13. 13. NBA   Basketball   Screenshot   25  Feb,  2016  
  14. 14. Oscars  –  Best  Actor   Screenshot,  25  Feb,  2016  
  15. 15. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Evalua-ng  SM  Campaigns  
  16. 16. The  POEM  Framework   Owned  Media   From  #IPASocialWorks  
  17. 17. The  Interac-ons  in  POEM   From  #IPASocialWorks  
  18. 18. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Key  Challenges   •  The  counter-­‐factual  –  what  would  have   happened  anyway   •  Influence,  how  to  measure  it,  does  it  exist?   •  Homophily  –  birds  of  a  feather  flock   together   •  Short  and  Long-­‐term  effects   •  Causa5on  and  Correla5on  
  19. 19. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Influence  and  Homophily   Type  of   Market   Influence     Target   influencers   Homphily   Target  people   like  buyers  
  20. 20. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Short  and  Long-­‐term  Effects   •  Social  is  very  good  at  measuring  short-­‐term  effects   •  The  micro-­‐objec5ves  are  oeen  ac5va5on  events:   –  Downloads,  registra5ons,  plays,  trial,  purchase  etc.   •  But,  long-­‐term  effects  are  oeen  more  important  to   brand  value  and  price  elas5city   •  Without  short-­‐term  effects  there  is  usually  no  long-­‐ term   –  But  long-­‐term  effects  are  not  just  the  sum  of  the  short-­‐ term  effects  
  21. 21. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Evalua-on  Methods  &  Approaches   From  #IPASocialWorks  
  22. 22. From  #IPASocialWorks  
  23. 23. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   What  is  the  impact  of  social?   Region  A   – T1  sales  =  100   – T2,  TV,  sales  =  110   – T3,  TV  &  Twier,  sales  =  130   Region  B   – T1,  sales  100   – T2,  Twier,  sales  =  110   – T3,  TV  &  Twiers,  sales  =  130  
  24. 24. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Lessons  from  Measuring  Social   1.  Plan  in  advance,  define  objec5ves,  bake   measurement  into  the  campaign   2.  Focus  on  a  core  set  of  relevant  metrics   3.  Try  to  include  experiments  /  experimental   design   4.  Have  access  to  advanced  analy5cs  –  but  be   pragma5c  
  25. 25. IBM’s  four  Vs  
  26. 26. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   What  is                                                                                ?   Instruc5on   Results  
  27. 27. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Target  
  28. 28. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Big  Data  Success   •  Nejlix,  what  sort  of  new  produc5ons  should   work  –  House  of  Cards   •  UPS  –  how  can  we  op5mize  routes   •  eBay  –  how  to  iden5fy  fraudulent  behaviour   •  WeatherSignal  –  use  data  from  smartphones   to  create  localised  weather  maps   •  Stockholmståg  Trains  –  what  events  predict   delays  in  the  next  2  hours   Check  out  Annie  Pelt’s  NewMR  webinar    
  29. 29. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Working  with  Big  Data   Most  successes  come  from  having  a  precise   and  narrow  ques5on:   •  What  paerns  indicate  fraudulent  ac5vity?   •  What  events  predict  churn?   •  Which  customers  are  pregnant?   •  How  many  types  of  customers  do  we  have?   –  What  best  predicts  membership  of  a  segment?  
  30. 30. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Correla-on  and  Causa-on   1.  Correla5on  predicts  the  past   –  Which  is  some5mes  enough   –  Especially  when  the  past  repeats  itself   2.  Causa5on  is  needed  to  predict  new  futures   –  But  causa5on  is  hard  to  establish  in  the  real   world   3.  Experiments  are  key  to  establishing   causa5on   –  Market  research  can  help  
  31. 31. Correla-on  Annual  Chocolate  Consump-on  &  Nobel   Prizes  per  10  Million  of  Popula-on   New  England  Journal  of  Medicine.  
  32. 32. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Iden-fy  the  Counterfactual   •  What  would  have  happened  without  the   campaign/ac5vity?   •  Projec5ons/forecasts   •  Year-­‐on-­‐year  figures   •  A/B  tests  
  33. 33. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Make  Predic-ons   Post  hoc  reasoning  when  supported  by   masses  of  data  can  support  the  crea5on  of   almost  any  point  of  view   Genera5ng  predic5ons  before  the  campaign   – As  well  as  targets   – Provides  a  framework  for  finding  out  why  the   predic5ons  were  wrong  (and  they  usually  are).  
  34. 34. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Using  Triangula-on   Triangula5on  means  using  mul5ple  sources  to   see  if  they  point  the  same  way   –  Helps  validate  findings   –  Helps  avoid  embarrassing  mistakes   Predic5on  can  be  used  with  triangula5on  to  avoid   simply  describing  paerns   –  For  example,  “If  this  finding  about  a  decline  in   sa3sfac3on  is  true  we  expect  churn  to  increase  over   the  next  three  months.”  
  35. 35. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Use  Benchmarks   Few  metrics  have  absolute  meaning   –  And  the  relevance  of  1  million  views  or  shares   changes  over  5me   So,  benchmarks  are  essen5al   –  Within  brand  benchmark   –  Within  plajorm  benchmark   –  Within  ver5cal  benchmark   –  Within  target  group  benchmark   Benchmarks  highlight  the  need  to  make  comparisons.  
  36. 36. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Organising  Complex  Data   •  Define  the  problem   –  What  success  looks  like,  a  5ghtly  defined  ques5on,  ac5ons  you   wish  to  take   •  Assess  the  characteris5cs  of  the  data  streams   –  Veracity,  Granularity,  What’s  missing,  Overlaps  etc   •  Filter,  clean  and  transform  the  data   •  Find  the  answer   –  Find  the  main  story  first  and  then  the  relevant  excep5ons  and  details   –  Simplify  models  as  much  as  possible,  but  no  further  (borrowing  from  Einstein)   –  Use  comparators  to  help  communicate  the  answers   –  Create  a  compelling  story  –  without  focusing  on  the  process  or  numbers  
  37. 37. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016  
  38. 38. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016  
  39. 39. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016  
  40. 40. Normalizing  by   Growth  Pa`erns   Forbes:  hp://bit.ly/NewMR_208  
  41. 41. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Normalizing  by  ‘Share  of’   •  Google  Trends  –  internet  use  is  growing,  Google  use  is  growing,   measures  must  be  normalized  to  be  compared.   •  Process   –  Collect  the  search  terms  and  count  men5ons  per  day  for  each  term   –  Express  them  as  percentages  of  all  searches  on  the  same  day   –  Find  the  biggest  number  for  the  search  terms  and  set  this  to  100  (or   100%)   –  Scale  all  of  the  other  items  by  the  same  factor   •  Note  the  only  meaning  the  numbers  have  is  in  the  context  of  the   set  of  items  being  measured  and  the  5me  frame  chosen.  
  42. 42. Google  Trends   Normalising  by  Share  of  …   Zika,  Worldwide,  last  90  days  
  43. 43. Comparators  
  44. 44. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Normalizing  by  Coding   •  Sen5ment  analysis,  open-­‐ended  comments   converted  to  Posi5ve,  Nega5ve  and  Neutral   •  Digi5zing  from  analogue  to  binary   •  Alloca5ng  to  segments   •  Scoring  different  elements   – (think  America  Football,  different  points  for   different  events,  leading  to  points  in  a  league)  
  45. 45. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Ben  Wellington,  TEDx,  How  we  found  the  worst  place  to  park   in  New  York  City  —  using  big  data    
  46. 46. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016  
  47. 47. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Use  the  Business  Ques-on  as  a  Lens   The  same  data  will  deliver  different  stories,  based   on  different  business  ques5ons   This  is  one  of  the  reasons  that  industry  reports  have   a  less  focused  story   –  They  have  many  readers,  with  different  needs  and   ques5ons   The  business  ques5on  defines  what  is  in,  what  is   out,  and  where  the  magnifica5on  should  be  
  48. 48. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Find  the  Relevant  Detail   Once  you  have  the  total  story:   – Are  there  people  who  have  a  different  story   (different  from  the  main  story)?   •  Who  are  these  people?   •  What  is  their  story?   •  Where  are  the  differences?   •  Why  are  they  different?   •  When  do  these  differences  maer,  come  into  play?  
  49. 49. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Different  Perspec-ves   ASK:   The  alterna3ve   explana3ons  for  this   data  are?  
  50. 50. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Findings  Need  a  Comparator   RFID  
  51. 51. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Bad  news  for  men  in  Eastern  Europe   Eurostat  -­‐  hp://goo.gl/r2q526   Amenable  Deaths  Per  100000  of  popula5on  -­‐  2012  
  52. 52. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   The  Big  Picture   •  Start  with  a  well  defined  ques5on   •  Assess  the  data  streams   –  Who  /  what  is  covered,  lags,  duplica5on,  veracity  etc   •  Bake  measurement  in  from  the  start  –  when  possible   –  Make  specific  predic5ons   •  Transform,  filter,  clean  the  data   •  Find  the  main  story   –  Considering  correla5on,  causa5on,  comparators  and  alterna5ve  models   (e.g.  influence  and  homophily)   •  Find  the  relevant  excep5ons  to  the  main  story   –  Who,  what,  why,  when  &  where  
  53. 53. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Thank  You!       Follow  me  on  Twi`er  @RayPoynter     Or  sign-­‐up  to  receive  our  weekly  mailing  at     h`p://NewMR.org      
  54. 54. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Schedule   •  An  Introduc5on  and  Overview  -­‐  Feb  23     •  Working  with  Qualita5ve  Informa5on  –  Apr  5     •  Working  with  Quan5ta5ve  Informa5on    -­‐  May  26     •  Working  with  mul5ple  streams  &  big  data  -­‐  July  5     •  U-lizing  visualiza-on  –  Sep  13     •  Presen5ng  the  story  -­‐  Nov  8    
  55. 55. Finding  and  Communica-ng  the  Story  –  Lesson  4  of  6  –  Complex  Data   Ray  Poynter,  2016   Q  &  A   Ray  Poynter   The  Future  Place  

Ray Poynter delivers the 4th lesson in the #NewMR webinar series on Finding and Communicating the Story in the Data. This lesson looks at Complex and Large Data Streams.

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