NAVIGATING  ASYMMETRICAL  BUSINESS TERRAIN WITH  OPEN  SOURCE  INTELLIGENCE +  NETWORKS +  MACHINES

CHANDLER  T  WILSON  DIRECTOR  OF  INSIGHTS/ANALYTICS  WALMART  

THIS  IS  ABOUT  MAKING  SENSE  OF  AND  CONNECTING  THE  HUMAN  CONDITION,  COLLECTIVE  KNOWLEDGE,  EVENTS,  ACTIONS  AND  INTERNAL  DATA.  THE  NEW  WORLD  OF  BUSINESS  BELONGS  TO  COMPANIES  WHO  CAN  NAVIGATE  ASYMMETRICAL  TERRAIN,  TEST  ASSUMPTIONS  AND  CREATE  INTELLECTUAL,  AS  WELL  AS  HARD  VALUE.

Technology  has  made  the  world  more  similar  and  faster,  yet  business  strategies  are  not  typically  scalable  from  one  market  to  the  next.  

The  Paradox  

Inherently  intelligence  and  insights  is  about  decisions.  The  value  of  information  that  leads  to  a  “roughly  right”  decision  ishighest  in  the  beginning  – and  often  more  valuable  than  a  perfect  decision.  To  address  this  bias,  organizations  need  to  focus  on  developing  process  and  internal  communication  that  foster  faster  “information-­to-­action”  transaction  times,  much  like  how  traders  look  at  financial  markets.

Time

Benefit  of    Decision  

Cost  of  Decision/IndecisionCompetitive  advantage  

Many  variables  +  little  time  =  rapidly  diminishing  information  value.

Studies  show  experts  consistently  fail  at  forecasting  and  traditionally  perform  worse  than  random  guessing  in  businesses  as  diverse  as  medicine,  real-­estate  valuation  and  political  elections.  This  is  because  traditionally  people  weight  experiences  and  information  in  very  biased  ways.  

Combining  open  source  web  intelligence  +  internal  data  (product  or  operations)  can  help  organizations  make  contextually  cognizant  decisions  while  acknowledging  the  level  granularity  needed  to  be  competitive  in  todays  globalized  market  place.  

Key  Themes:

• Network  Analysis:  Find  how  people,  events,  things  and  places  are  connected  to  find  unforeseen  risk  and  nonobvious  opportunities.  Offers  more  potential  for  higher  resolution  &  descriptive  KPI’s.    

• Open  Source:Anything  data  that  is  online  -­ structure  or  unstructured.Given  the  amount  of  descriptive  events  now  reported  online,  the  results  describe  reality.  

• Machines:    Weight  thousands  of  variables  at  once  in  an  unbiased  way.

• Operations  focused:  It's not  the  companies  that  adopt  new  technologies  that  are  going  to  win,  it’s  the  ones  that  mandate  processes  and  operation  that  leverage  them  who  will.  Legacy  business  processes  &  thinking  need  to  be  assessed  with  the  utmost  scrutiny.  

Machines  +  Networks  +  Open  Source  Intelligence  

Traditional  BI,  Strategy  and  Insights  • Linear  &  disparate  isolated  trends  lead  to  biased  decisions  – humans  are  bad  at  weighting  information• Doesn’t  isolate  influencing  variables  that  can  (actually)  be  controlled  for  quickly    • Predetermined  questions  to  derive  importance  or    meaning    

• All  business  environments  are  multidimensional,  asymmetrical  and  granular.• Linear  or  rules-­based  analysis  not  only  has  dangerous  consequences  in  terms  of  biased  decision  making,  but  it’s  also  time  consuming  and  not  contextual.

Typically  business  strives  to  be  linear  and  simple  when  it’s  not.    

Open  SourceMost  of  the  world’s  collective  knowledge  and  descriptions  of  events  are  online.  Applying  Machines  to  these  data  sets  can  upscale  the  quality  of  intelligence  in  ways  not  possible  just  a  few  years  ago.  Much  like  how  Google  sped  up  information  discovery,  tools  with  sophisticated  graphing  capabilities  are  allowing  people  “up  skill”  their  intelligence.  

Entities  

Emotions

Location  

Event    Actions        

• Machines  analyze  and  weight  vast  amounts  of  information  in  ways  humans  cannot.• Understanding  these  dynamics  allows  for  higher  resolution  forecasts.  • No  need  to  make  variables  into  a  binary  KPI  like  positive/negative  sentiment  – go  with  context.  

How  machines  extract  meaning  from  open  source  text.

Dynamics:  2015  MN  Vikings  Draft  Online  News  • How  legacy  BI  tends  to  look  at  trends• Time  lines  are  great  but  show  little  in  the  name  of  context• Descriptive,  not  prescriptive  • People  weight  information  in  inconsistent  ways  which  doesn’t  enable  them  to  see  certain  connections  that  matter  do  to  experience,  insider  knowledge,  personal  connection/interest.

The  obvious  isn’t  a  competitive  advantage.  In  an  era  when  the  obvious  leads  to  little  or  no  wins,  finding  novel  connections  can  generally  offer  more  return  on  investment  than  legacy  strategies.  Industry  disruption  is  often  times  led  by  outsiders.

Entity  extraction  from  specific  “expert”  commentary  on  the  draft.  • Top  people  extracted  from  open  source  content  related  to  the  20015  NFL  Draft  and  Minnesota  Vikings

Leveraging  the  machine• Zooming  out  with  a  few  filters  enable  the  machines  to  build  connections  in  less  obvious  areas  through  interpolating  entities  and  key  concepts.  

• All  nodes  could  be  zoomed  in  on  and  have  vast  amounts  of  meta  data  for  further  analyses.  

Using  machines  to  extract  entities  can  help  predict  outcomes  (less  specific).• The  broad  domain  which  leaned  in  on  machine  hypothesis  – hit’s  on  our  2nd round  draft  pick  Eric  Kendricks.  • This  enables  us  to  go  into  this  ideation  phase  where  machines  define  the  framework  with  plenty  of  room  left for  creative  thinking.

Humans,  when  looking  for  more  specificity,  added  more  noise  opposed  to  exploring  vast  data  sets  with  machines  driving  the  connections  =  less  potential  insights.  

Machine  and  network  driven  frameworks  allow  us  to  have  both  more  meaningful  and  creative  strategies  by  cutting  through  noise  and  finding  what  we  can  actually control.  

And  further  weight,  non-­superficially,  variables  that  influence  those  outcomes.  

VAR

AC

E

D

F

B

Be  cautious  of  “common  sense”.      • The  best  operations  and  information  that  need  the  level  of  speed  and  quality  are  often  times  abstract  and  uncongenial  to  standard  processes  or  thinking  when  in  most  cases  they  are  the  only  things  that  drives  a  competitive  advantage

• The  more  we  know  the  less  clear  things  become  often  times,  this  is  why  priming  an  organization  is  vital.  As  is  reframing  the  idea  of  what  is  means  to  have  expertise.    

• Under  the  majority  of  daily  business  circumstance  niche’  domain  expertise  isn’t  readily  available.  Organizations  need  to  be  cognizant  that  the  level  of  expertise  needed  to  beat  a  machine  is  constantly  swinging  in  favor  of  machines.

• Lean  in  on  building  automated  hypothesis  systems  that  isolate  key  problems  and  variable  thereafter  using  humans  ability  to  understand  context  humans  and  organization  structure  – something  machines  are  not  able  to  do  (currently).  

• Build  decision,  not  insight  systems  that  push  decisions,  not  insights.  Only  look  to  apply  information  where  it  can  be  effected  – there  are  drastic  biased  consequences  to  people  looking  at  too  much  information  (most  of  which  is  irrelevant)  with  out  context  (emails,  twitter,  insider  information).

What’s  next?

HitchBot