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Big Ideas from Big (or Small) Data Book Summit Canada Pete McCarthy The Logical Marketing Agency

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Presented at Book Summit Canada, June 2014. How to identify, understand, and efficiently grow your audience by gathering and utilizing consumer data. Tools, techniques, and actionable insights in this presentation, which takes its focus a hypothetical challenge of growing the audience for Nate Silver's book The Signal and the Noise in Canada.

TRANSCRIPT

Page 1: Big Ideas from Big (or Small) Data

Big  Ideas  from  Big  (or  Small)  Data  Book  Summit  Canada      Pete  McCarthy  The  Logical  Marketing  Agency  

Page 2: Big Ideas from Big (or Small) Data

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   2  

Who  am  I  and  why  am  I  here?  

Page 3: Big Ideas from Big (or Small) Data

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   3  

What  are  we  talking  about  and  why  are  we  talking  about  it  (now)?  

Page 4: Big Ideas from Big (or Small) Data

We  are  talking  about  big  ideas.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     4  

Really,  a  process  which  may  yield  big  ideas.  Discussion  of  data  is  highly  probable.  

 

It  is  a  capital  mistake  to  theorize  

before  one  has  data.  Insensibly  one  

begins  to  twist  facts  to  suit  theories,  

instead  of  theories  to  suit  facts.  

–  Sherlock  Holmes,  A  Scandal  in  Bohemia  

Page 5: Big Ideas from Big (or Small) Data

This  is  a  big  idea!  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     5  

94%  accuracy  of  opening  weekend  box  office  up  to  4  weeks  pre-­‐release…  

2013  

Page 6: Big Ideas from Big (or Small) Data

So  was  this  and  seems  to  still  be.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     6  

97%  correlation  between  “Twitter  chatter”  and  opening  weekend  box  office.  

2010  

Page 7: Big Ideas from Big (or Small) Data

Especially  when  combined  with  this  work.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     7  

Which  adds  (a  little)  more  (seemingly  correct)  data  to  eliminate  bias.  

2012  

Page 8: Big Ideas from Big (or Small) Data

This  might  be  part  of  a  big  idea…  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     8  

77%  “predictive.”  Backward-­‐looking.  Reliability  of  data?  

2012  

Page 9: Big Ideas from Big (or Small) Data

2013  

1983  

These  were  big  ideas…and  some  still  are…  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     9  

Most  big  ideas  build  on  prior  big  ideas  –  successful  or  not.  

2010  

2010  

2002  

2000  

1994  

Page 10: Big Ideas from Big (or Small) Data

Why  we  are  here.    

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     10  

Because  of  what  Google  (and  others)  do.  Because  we  can  do  similar  things.  

ü  What  ü  When  ü  Where  ü  Which  

ü  Who  ü  How  ü  Even  a  plausible  

why!  

Page 11: Big Ideas from Big (or Small) Data

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   11  

What  we  talk  about  when  we  talk  about  consumer  data  

Page 12: Big Ideas from Big (or Small) Data

In  essence,  we  are  talking  about  useful  research.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     12  

Some  “types”  of  consumer  research  and  the  methods  used.  

Secondary  

Industry-­‐specific  

Qualitative  

Non-­‐transactional  

Snapshot  in  time  

Bricks  &  Mortar  

Unknown  People  

Unknown  Person  

 Primary  

“Whole  World”    

Quantitative  

Transactional  

Trended  

“Digital/Online”  

Known  People  

Known  Person            

|  

|  

|  

|  

|  

|  

|  

|  

Types  of  Research/Data  

Methods  of  acquiring  research  data  

 

1.  By  surveying  people  

2.  By  observing  them    

Page 13: Big Ideas from Big (or Small) Data

Research  that  yields  data  on  audiences  to  solve  below.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     13  

Big  data,  little  data  –generally  pretty  similar  data.  Just  scale  and  use  differ.  

Aware  &  Will  Buy.  

Aware  &  Will  Not.  

Unaware  &  Just  Might!  

Unaware  &  Just  Fine.  

This  is  the  gold  mine  of  readers.  It  is  the  crossover  hit.  Especially  true  for  niche  and  vertical  publishers.  

A  must.  

Page 14: Big Ideas from Big (or Small) Data

Content  created/consumed  by  consumers.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     14  

Mary  Meeker  referred  to  the  “data-­‐creating  consumer”  as  a  top  2014  trend.  

Page 15: Big Ideas from Big (or Small) Data

Major  social  platforms  total  registered  users.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     15  

0  

200  

400  

600  

800  

1,000  

1,200  

1,400  

2004   2005   2006   2007   2008   2009   2010   2011   2012   2013  

Millions

 

Facebook   Twittter   Google+  (Gmail)   Pinterest   Instagram  

Registered  users  as  of  May  2013.  Reported.  

Several,  culled  by  Search  Engine  Journal  

Page 16: Big Ideas from Big (or Small) Data

US  social  network  penetration  by  age  +  mobile.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     16  

As  of  May  2013.  Via  survey.  

Pew  Research:  Social  Media  Update  2013  via  Search  Engine  Journal  

Page 17: Big Ideas from Big (or Small) Data

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   17  

Canada-­‐specific  data.    

Search  Market  Share  June  2014  opt-­‐in  panel.  

June  2014.  

Top  Social  Media  Sites  Used  in  Last  Month  Canada  “Digital”  Snapshot  Data  

Source:  Experian  Hitwise  Canada  

§  86%  internet  penetration  §  76%  mobile  internet  penetration  

§  56%  smartphone  penetration  §  77%  of  owners  research  products  on  

phone,  27%  buy  on  phone  §  82%  Social  Media  penetration  

§  55%  Facebook  penetration  §  <2  hours/day  social  media  use  

0%   10%   20%   30%   40%   50%   60%  

Pinterest  

LinkedIn  

Google+  

Twitter  

Facebook  

Page 18: Big Ideas from Big (or Small) Data

Canada  and  the  U.S.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     18  

Sources:  PWC  Global  Media  Outlook,  Census  Data,  Global  Web  Index  Wave    

60  

7  

0   20   40   60   80  

U.S.  

Canada  

137  

17  

0   50   100   150  

U.S.  

Canada  

254  

30  

0   100   200   300  

U.S.  

Canada  

315  

35  

0   100   200   300   400  

U.S.  

Canada  

Population  (M)    Ratio:  1:9     Internet  Users  (M)  Ratio:  1:8.5    

Facebook  Users:  Last  Month  (M)    Ratio:  1:8    

Twitter  Users:  Last  Month  (M)    Ratio:  1:8.5    

Trade  Book  Sale  Ratios  

Range  from  1:15  to  1:10…    

No  “apples-­‐to-­‐apples”  

data  but  directionally  

these  provide  a  sense.  

A  sense  of  proportion.  

Page 19: Big Ideas from Big (or Small) Data

Canadian  book  consumers  and  retail.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     19  

2012−2013.  Primarily  via  survey.  (I’ve  focused  on  the  Business  category.)    

•  68%  Business  book  buyers  =  male    ! >  50%  awareness  =  online    ! Only  20%  purchase  impulsively.  

BookNet  Canada,  “The  Canadian  Book  Consumer  2013”    

Page 20: Big Ideas from Big (or Small) Data

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     20  

Some  really  useful  places  to  gather  consumer  data.  

§  Social  Graph  They  know  consumers.  Online  and  offline.  360-­‐degree  view.    

§  Ad  Platform    Open  (APIs,  Tools),  app  development,  Oauth  site  sign  on.  

§  Constant  A/B  testing  Fail  fast,  fix.  

 §  Result:  Happy  Users/Advertisers  

Despite  incredible  concerns  over  privacy.  Relevance  trumps  it.  

§  Search  (&  lots  else)  Massive  share.  YouTube.  

 §  Ad  Platform  

Targeted  inventory  at  an  all  time  high.  

§  Literally  Building  a  Brain  Yes.  All  products  data-­‐driven.  Predictive.  

.    §  Open  

APIs  and  tools.  Oauth  site  sign  on.  

§  Massive  growth  Wild  adoption  and  usage.  

 §  Ad  Platform  

Targeting.  

§  Timely  Almost  “now.”  Predictive.  

 §  Open  (for  now)  

Can  get  at  the  data.    Oauth  site  sign  on.  

Page 21: Big Ideas from Big (or Small) Data

A  sampling  of  useful  tools.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     21  

Social  Analytics  §  Simply  Measured  §  SproutSocial  §  Social  Bakers  §  Followerwonk  §  Commmun.it  §  Bit.ly  §  Topsy  §  Social  Mention  

§  Facebook  Ad  Interface  §  Facebook  PowerEditor  §  EdgeRank  Checker  §  SimplyMeasured  §  Twitter  Ad  Interface  §  Radian  6/Crimson  

Hexagon  §  HootSuite  

 §  Facebook  Insights  §  LinkedIn  Analytics  §  Instagram  Analytics  §  Etc.  

Web/Email  Analytics  

Web/SEO  §  Raven  §  Compete  §  Quantcast  §  SEO  Quake  §  SEM  Rush    §  Google  universal  analytics  

§  WordTracker  §  WordStream  §  Amazon  comp  authors  §  Librarything  tags/

comps  §  Etc.  

§  Google  Analytics  §  Omniture  §  ExactTarget  §  MailChimp  

Mostly  not  huge,  costly  a  la    Adobe  or  Salesforce  

§  Optimizely  §  Etc.  

And  many,  many  more  to  fit  nearly  any  use  case  

§  Google  Trends  §  Google  AdWords  §  Moz  §  Soovle  (autocompletes  

in  general)  §  Seorch  

Page 22: Big Ideas from Big (or Small) Data

I  like  how  this  guy  talks  about  research  and  data.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     22  

Nate  Silver.  (I  like  others,  also).  

…if  the  quantity  of  information  is  increasing  by  2.5  quintillion  bytes  per  day,  the  amount  of  useful  information  almost  certainly  isn't.  Most  of  it  is  just  noise,  and  the  noise  is  increasing  faster  than  the  signal.  There  are  so  many  hypotheses  to  test,  so  many  data  sets  to  mine—but  a  relatively  constant  amount  of  objective  truth.  

Photo:  Marius  Bugge  

Bayes’ Theorem

Page 23: Big Ideas from Big (or Small) Data

Foxes  gather  “big  ideas”…quickly.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     23  

Photo:  Marius  Bugge  

“The  fox  knows  many  little  things,  but  the  hedgehog  knows  one  big  thing.”  

Hedgehogs  are  Type  A  personalities  who  believe  in  Big  Ideas—in  governing  principles  about  the  world  that  behave  as  though  they  were  physical  laws  and  undergird  virtually  every  interaction  in  society.    

Foxes,  on  the  other  hand,  are  scrappy  creatures  who  believe  in  a  plethora  of  little  ideas  and  in  taking  a  multitude  of  approaches  toward  a  problem.  They  tend  to  be  more  tolerant  of  nuance,  uncertainty  ,  complexity,  and  dissenting  opinion.  If  hedgehogs  are  hunters,  always  

looking  out  for  the  big  kill,  then  foxes  are  gatherers.  

Page 24: Big Ideas from Big (or Small) Data

One  second  on  Bayesian  statistics.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     24  

No  test  (I  wouldn’t  pass).  The  governing  principle  is  the  thing.  

»  Bayesian  statistics  is  a  subset  of  the  field  of  statistics  in  which  the  evidence  about  the  true  state  of  the  world  is  expressed  in  terms  of  degrees  of  belief  or,  more  specifically,  Bayesian  probabilities.  

   

»  Bayesian  statistics  (if  only  practiced  in  spirit)  sets  one  up  to:  

 

§  Statistical  inferences  

§  Statistical  modeling  

§  Design  of  experiments  

§  Statistical  graphics  

§  Be  human  (encouraged)  

§  Move  quickly,  get  lots  of  data  

§  Admit  bias  but  try  to  verify  

§  Change  tack  as  indicated  

§  Becoming  “less  wrong”  (testing)  

§  Becoming  even  less  “less  wrong,”  over  

time  

§  Demonstrating/validating  

We  verify  or  discover  the  big  ideas,  as  opposed  to  just  having  them.  

Page 25: Big Ideas from Big (or Small) Data

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   25  

Identifying  and  understanding  audiences  using  data  

Page 26: Big Ideas from Big (or Small) Data

I  wonder  how  The  Signal  and  the  Noise  is  doing?    

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     26  

#1  Bestseller.  In  Statistics  Textbooks….  

#989  overall.  Without  being  able  to  see  POS,  I  don’t  know  if  that  signifies…  

I  might  throw  a  “Business  BISAC”  at  Amazon.  It’s  not  a  

textbook.  

Page 27: Big Ideas from Big (or Small) Data

Nate  Silver’s  audience.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     27  

Wonder  who  they  are.  I  have  guesses  but  that’d  be  bias.  Let’s  look.  

720k  is  a  hefty  Twitter  following.  He’s  tweeted  often  and  “on  message.”  Recency.    

Page 28: Big Ideas from Big (or Small) Data

Where  do  they  live?  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     28  

Home  locations  of  unnamed  Silver  Twitter  followers  based  on  a  sample.  Directional.  

New  York,  LA,  London.  Is  that  Canada  I  see?  

Page 29: Big Ideas from Big (or Small) Data

Canada?  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     29  

It  is  indeed.  But  those  followers  are  in  Seattle.  Drats!  

Why  no  Canadian  followers?  Bug?  Opportunity?  (We  know  Canadians  use  Twitter.)    

Page 30: Big Ideas from Big (or Small) Data

Google.ca  auto-­‐prompts  me  at  “s.”  That’s  good.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     30  

1   1b  

Book  results  are  low  and  related.  

Amazon  is  first  book  result.  Way  below  the  fold  on  any  device.  

Page 31: Big Ideas from Big (or Small) Data

How  does  the  book  look  an  Amazon.ca,  Kobo,  Indigo.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     31  

There  a  book  audience  but  it  feels  small.  

Two  reviews    feels  low…  

Good  position.  Seem  like  more  consumer-­‐

aligned  categories  

Would  have  expected  him  to  be  prompted  above    

Nate  Southard…    

Page 32: Big Ideas from Big (or Small) Data

What  is  the  search  interest  like?  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     32  

Canada  –  Spikes  –  Volume  is  on  Him  

Interest  falls  but  stays.  Book  present.  Google  Trends  Canada,  US.  

January  2007  –  September  2012    

September  2012  –  May  2014    

Interest  falls  fast.  No  book.  

January  2007  –  September  2012    

US  –  Very  Similar    

Page 33: Big Ideas from Big (or Small) Data

Comparing  raw  search  volume.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     33  

Canada  Brand  Search  Volumes  

US  Brand  Search  Volume  

1,400  reach  in  Facebook  CA  advertising    vs.  62,000  in  US    

Ratios  feel  as  if  he  is  punching  below  weight.  

Page 34: Big Ideas from Big (or Small) Data

 More  data  on  interest  in  Canada  allows  inference…  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     34  

Silver  does  not  enjoy  the  interest  here  that  he  does  in  the  states.  

3%  is  too  small  number,  given  expected  ratios.  Canada  has  about  the  population  of  California.  

Hypothesis:  he  is  under-­‐indexing  in  CA.  

Perhaps  there  is  room  for  sales  growth  –  in  and  

using  social.  

Page 35: Big Ideas from Big (or Small) Data

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   35  

Efficiently  growing  audiences  using  data  

Page 36: Big Ideas from Big (or Small) Data

Mine  adjacencies.    

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     36  

Some  potential  adjacencies  for  Nate  Silver.  

Page 37: Big Ideas from Big (or Small) Data

One  adjacent  audience:  Moneyball.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     37  

Google  Adwords  and  Facebook  confirm  connection  and  show  Canada  reach.  

=  

=  

50,000  

196,000  

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Ride  big  waves.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     38  

Google  Trends  Canada.  

“538”  is  Silver’s  recently  re-­‐launched  site,  covering  things  from  sports  to  politics.  

There  is  Canadian  search  interest  in  538.  

He  is  predicting  the  World  Cup  winner  in  real  time.  

15M  Tweets  on  World  Cup  in  past  month.    

The  World  Cup  is  big  in  Canada  (I  did  verify).  Though  it  is  an  adjacency  that  is  further  away,  Silver  has  tied  himself  to  the  World  Cup  explicitly.    Hypothesis:  It  can  likely  be  capitalized  on  to  get  people  interested  in  him.  

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Reaching  “look-­‐alikes”  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     39  

Some  characteristics  of  his  audience.  

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Regionality  gleaned  from  search.    

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     40  

Are  there  attributes  of  the  US  locales  that  “match”  Canadian  locales?  (DMAs)  

Page 41: Big Ideas from Big (or Small) Data

Comp  authors:  adjacent  fans  and  look-­‐alikes.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     41  

Authors  whom  the  consumer  comps,  as  opposed  to  us.  Preferably  outside  book  spaces.  

The  intersecting  folks  are  a  great  source  of  look-­‐alike  attributes.    

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Comp  authors:  adjacent  fans  and  look-­‐alikes.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     42  

We  can  use  the  Venn  to  find  people  to  target  who  look  exactly  like  the  shared  followers.  

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Thinking  in  terms  of  optimizing  “funnels.”  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     43  

Goal:  sell  The  Signal  and  the  Noise  in  Canada.  One  potential  funnel  (to  test).    

Segment    

§  Male  §  Like  Moneyball  §  And  topics  

related  directly  to  Moneyball  

Platform    §  Facebook  §  Mobile  stream  

Landing    §  Kobo  page  

Creative    §  A:  Sports  §  B:  Business  

This  is  funnel  A.    There  should  at  minimum  be  a  B,  testing  with  at  least  one  variable  changed.  

Measure  costs  to  reach  fans  and  conversion  to  sale  (the  goal  here).  See  who  is  responding,  adjust  (more  hypotheses)  or  “get  out.”  

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This  may  not  be  a  “big  idea.”  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     44  

But  if  it  were  to  be  successful  it  would  be  a  nice  one-­‐off  and  could  lead  to  learning  how  to  develop  a  process  of  outsizing  “American”  authors  in  Canada.  

»  One  could  systematically  identify  US  authors  with  works  on  sale  in  CA  §  Look  for  the  delta  in  unit  sales  between  US  and  CA.  IF  greater  than  norm,  examine.  

»  Do  the  same  with  authors  with  major  digital  presences  in  US  without  in  CA.  §  See  what  can  be  modeled  in  CA  from  the  US  presence  

And  so  on…  

Page 45: Big Ideas from Big (or Small) Data

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   45  

Suggestions  if  you’d  like  them  (along  with  2  warnings)  

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Suggestions  

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   46  

»  Establish  goals  regarding  audience  identification.  

§  What  outcome  would  be  ideal.  

»  Involve  organization  around  the  approach.  

§  Marketing,  sales,  publicity,  IT  need  to  align  to  gain  maximum  value.  

§  Affects  everything;  physical  distribution,  ad  creative,  PR  to  metadata,  etc.  

»  Recognize  that  it  is  a  process  of  testing  and  learning.  

§  Failure  (of  a  reasonable  hypothesis)  is  not  a  bad  thing.  

»  Buy,  build,  find,  learn  the  systems  to  support  the  work.  

§  Capture  learning  at  all  times.  

§  Scale  when  the  value  is  there  (eg.  Big  Ideas  are  coming  and  are  repeatable).  

May  prove  useful  if  data-­‐driven,  audience-­‐centric  marketing  is  of  interest.  

See  warnings.  

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Two  warnings  

1.  This  is  relatively  technical  work  but  does  not  require  one  to  be  a  “data  scientist.”  Just  unafraid  of  technology,  curious,  and  able  to  employ  the  logic.    

2.  The  more  one  does  it,  the  faster  it  goes.  It  is  not  fast  at  first  but  is,  in  the  end,  likely  more  efficient  and  will  yield  big  ideas.  

June  19,  2014   Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada     47  

—Nate  Silver,  The  Signal  and  the  Noise    

Page 48: Big Ideas from Big (or Small) Data

Thank  you  

Big  Ideas  from  Big  (or  Small)  Data    |  Book  Summit  Canada    June  19,  2014   48