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Big Data: Friend, Phantom or Foe?

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John Girard's keynote talk at KM Singapore "Big Data: Friend, Phantom or Foe?" Asking and answering some of the tough questions leaders have about Big Data.

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Big Data: Friend, Phantom or Foe?

  1. 1. www.johngirard.net                                                                                    john@johngirard.net  1   John  P.  Girard,  Ph.D.   h�ps://www.youtube.com/watch?v=QL1dQuK5Wsg  
  2. 2. www.johngirard.net                                                                                    john@johngirard.net  2   Big  Data  in  your  organiza�on?   Leaders  in  my  organiza�on  are  _________  Big  Data.     B.    interested  in   C.    bored  with     D.    confused  about     A.    excited  about   Some  History   Many  leaders  were  (are)   very  skep�cal  about  the   real  value  of  KM  
  3. 3. www.johngirard.net                                                                                    john@johngirard.net  3   Big  Data  =  KM  by  another  name?   Big  Data  is  Everywhere  
  4. 4. www.johngirard.net                                                                                    john@johngirard.net  4   Is  Big  Data  New?   www.google.com/trends/   Teradata, 1991 (Osco Drug) www.�nyurl.com/GirardBD   Prairie  Business  Magazine,  7(1)  -­‐  2008       Is  data  mining   synonymous  with   Big  Data?   No.    Big  Data  is  the  data   set  (or  asset).     Data  mining  is  the   process  (or  handler).  
  5. 5. www.johngirard.net                                                                                    john@johngirard.net  5   Big  Data  =  KM  by  another  name?   The  History  of  Big  Data   Informa�on  Overload   Informa�on  overload   occurs  when  the  amount  of   input  to  a  system  exceeds   its  processing  capacity.   (Speier  et  al,  1999)   Informa�on  Overload   Informa�on  overload  is  that   state  in  which  available,   and  poten�ally  useful,   informa�on  is  a  hindrance   rather  than  a  help.   (Bawden,  2001)     Personal  Informa�on  Overload   A  percep�on  on  the  part  of  the  individual   (or  observers  of  that  person)  that  the  flow   of  informa�on  associated  with  work  tasks   is  greater  than  can  be  managed   effec�vely.  (Wilson,  2001)   Organiza�onal  Informa�on  Overload     A  situa�on  in  which  the  extent  of   perceived  informa�on  overload  is   sufficiently  widespread  within  an   organiza�on  as  to  reduce  the  overall   effec�veness  of  management  opera�ons. (Wilson,  2001)  
  6. 6. www.johngirard.net                                                                                    john@johngirard.net  6   Overload  is  not  new!   The  Roman  Philosopher  Seneca   worried  about  informa�on   overload  nearly  2,000  years   before  it  was  cool.  “What  is  the   point  of  having  countless  books   and  libraries  whose  �tles  the   owner  could  scarcely  read   through  in  a  whole  life�me?”   he  wondered.   Michael  Grunwald  @MikeGrunwald    Aug.  28,   2014   The  History  of  Big  Data   2/3  of  managers  complained  of   Informa�on  overload    (KPMG,   2000)     38%  of  the  surveyed  managers   waste  a  substan�al  amount  of   �me  loca�ng  informa�on   (Wilson,  2001)       Managers  “dwell  on  informa�on   that  is  entertaining  but  not   informa�ve,  or  easily  available   but  not  of  high  quality”  (Linden,   2001)     43%  of  the  managers  delayed   decisions  because  of  too  much   informa�on.  (Wilson,  2001)     The  total  accumulated  codified  database  of  the  world,  which  includes   all  books  and  all  electronic  files,  doubles  every  seven  years  and  some   predict  this  will  double  twice  a  day  by  2010  (Bon�s,  2000).     What  we  knew  a  decade  ago:  
  7. 7. www.johngirard.net                                                                                    john@johngirard.net  7   Exhibit  at  NAFA  School  of  Art  &  Design   KM  1.0  (According  to  John)   Knowledge Information Data Data to Information  Context  Categorize  Calculate  Correct  Condense Information to Knowledge  Compare  Consequences  Connects  Conversation
  8. 8. www.johngirard.net                                                                                    john@johngirard.net  8   KM  2.0   Ikujiro Nonaka Sociali zation Externa lization Interna lization Comb ination TACIT EXPLICIT EXPLICIT TACIT Seek  Wisdom   Seek  wisdom,     not  knowledge.       Knowledge  is  of  the  past,   wisdom  is  of  the  future.                          ~  Lumbee  Proverb   The  Lumbee  Tribe  of  North  Carolina  is  a  state  recognized  tribe  of  approximately   55,000  enrolled  members,  most  of  them  living  in  Robeson  and  the  adjacent   counties  in  southeastern  North  Carolina.    
  9. 9. www.johngirard.net                                                                                    john@johngirard.net  9   The  Cogni�ve  Hierarchy   10  Years   Knowledge Information Data Ackoff’s Apex Wisdom Understanding Knowledge Seek  Wisdom  not  Knowledge  (KM  2.5?)   Big  Data  –  Some  Defini�ons   A  term  coined  to  reflect  very   large  and  very  complex  data   sets.  (Sultanow  &  Chircu,   2015)   Big  data  is  a  term  for  any   collec�on  of  large  and  complex   data  sets  that  it  becomes   difficult  to  process.  (Gordon,   2015)   Data  set  that  is  beyond  the   capacity  of  rela�onal  database   applica�ons.  (Joseph,  2015)   Term  for  a  collec�on  of  large   and  complex  data  sets  that  it   becomes  difficult  to  process   with  tradi�onal  tools.  (Klepac   &  Berg,  2015)   Large   Complex   Dif�icult   Strategic  Data-­‐based  Wisdom  in  the  Big  Data  Era  
  10. 10. www.johngirard.net                                                                                    john@johngirard.net  10   Complex:  A  Defini�on   Large   Complex   Dif�icult   “a  group  of  obviously   related  units  of  which  the   degree  and  nature  of  the   rela�onship  is  imperfectly   known”   An  early  example  of  Big  Data  (KM  3.0)   Knowledge Information Data Wisdom Understanding Knowledge KnowledgeCreation “With 3,600 stores in the United States and roughly 100 million customers walking through the doors each week, Wal-Mart has access to information about a broad slice of America . . . The data are gathered item by item at the checkout aisle, then recorded, mapped and updated by store, by state, by region . . . By its own account Wal-Mart has 460 terabytes of data.” 14 November 2004 Hurricane
  11. 11. www.johngirard.net                                                                                    john@johngirard.net  11   A  more  recent  example   www.youtube.com/watch?v=SiFPjUTw_qM   Big  Data  Can  Provide  Decision  Support   Slamtracker's  Keys  to  the  Match  mines  over  8   years  of  Grand  Slam  Tennis  data  (~41  million  data   points)  to  determine  pa�erns  and  style.  Prior  to   each  match,  the  system  runs  an  analysis  of  both   compe�tors’  to  determine  what  the  data  indicates   each  player  must  do  to  do  well  in  the  match.  
  12. 12. www.johngirard.net                                                                                    john@johngirard.net  12   An  Example   Big  Data   What  do  we  know  about  Big  Data?  
  13. 13. www.johngirard.net                                                                                    john@johngirard.net  13   Big  Data  is  Global  and  Mul�disciplinary   www.google.com/trends/   Big  Data  is  NOT  just  technology   www.google.com/trends/  
  14. 14. www.johngirard.net                                                                                    john@johngirard.net  14   The  newest  technology   www.youtube.com/watch?v=qSJm3nIicK4   The  right  technology   Branson’s  secret  weapon  is  carrying  an  old-­‐fashioned   notebook  with  him  everywhere  he  goes.  
  15. 15. www.johngirard.net                                                                                    john@johngirard.net  15   Google  Flu  Trends   www.youtube.com/watch?v=6111nS66Dpk   Did  Google  get  is  wrong?  
  16. 16. www.johngirard.net                                                                                    john@johngirard.net  16   Gartner  Hype  Cycle  for  Emerging  Technologies   www.gartner.com/newsroom/id/3114217  www.gartner.com/newsroom/id/2819918   Big  Data  is   no  longer   there!   Data  Conerns   www.theguardian.com/technology/2015/mar/18/twi�er-­‐puts-­‐trillions-­‐tweets-­‐for-­‐sale-­‐data-­‐miners  
  17. 17. www.johngirard.net                                                                                    john@johngirard.net  17   The  Size  of  Big  Data   http://www.youtube.com/watch?v=B27SpLOOhWw   http://www.computerworlduk.com/news/infrastructure/3433595/boeing-­‐787s-­‐create-­‐half-­‐terabyte-­‐of-­‐data-­‐per-­‐�light-­‐says-­‐virgin-­‐atlantic/   Decide  later  …  
  18. 18. www.johngirard.net                                                                                    john@johngirard.net  18   The  History  of  Big  Data   2/3  of  managers  complained  of   Informa�on  overload    (KPMG,   2000)     38%  of  the  surveyed  managers   waste  a  substan�al  amount  of   �me  loca�ng  informa�on   (Wilson,  2001)       Managers  “dwell  on  informa�on   that  is  entertaining  but  not   informa�ve,  or  easily  available   but  not  of  high  quality”  (Linden,   2001)     43%  of  the  managers  delayed   decisions  because  of  too  much   informa�on.  (Wilson,  2001)     The  total  accumulated  codified  database  of  the  world,  which  includes   all  books  and  all  electronic  files,  doubles  every  seven  years  and  some   predict  this  will  double  twice  a  day  by  2010  (Bon�s,  2000).     What  we  knew  a  decade  ago:   Michael  Jordan  on  the  “Delusions”  of  Big  Data   http://spectrum.ieee.org/robotics/arti�icial-­‐intelligence/machinelearning-­‐maestro-­‐michael-­‐jordan-­‐on-­‐the-­‐ delusions-­‐of-­‐big-­‐data-­‐and-­‐other-­‐huge-­‐engineering-­‐efforts   When  you  have  large  amounts  of  data,  your   appe�te  for  hypotheses  tends  to  get  even  larger.   And  if  it’s  growing  faster  than  the  sta�s�cal   strength  of  the  data,  then  many  of  your   inferences  are  likely  to  be  false.  They  are  likely  to   be  white  noise.  
  19. 19. www.johngirard.net                                                                                    john@johngirard.net  19   "More  data  increases  our  confidence,  not  our  accuracy"   Image  Credit:  Kris  Krug   h�p://www.kriskrug.com   h�p://www.tylervigen.com/  
  20. 20. www.johngirard.net                                                                                    john@johngirard.net  20   h�p://www.tylervigen.com/   h�p://www.tylervigen.com/  
  21. 21. www.johngirard.net                                                                                    john@johngirard.net  21   What  did  happen?   h�ps://www.linkedin.com/pulse/whatever-­‐happened-­‐knowledge-­‐management-­‐tom-­‐davenport   What  did  happen?   h�ps://www.linkedin.com/pulse/whatever-­‐happened-­‐knowledge-­‐management-­‐tom-­‐davenport   KM never incorporated knowledge derived from data and analytics. I tried to get my knowledge management friends to incorporate analytical insights into their worlds, but most had an antipathy to that topic. It seems that in this world you either like text or you like numbers, and few people like both. I shifted into focusing on analytics and Big Data, but few of the KM crowd joined me.
  22. 22. www.johngirard.net                                                                                    john@johngirard.net  22   The  Future  …   The  Marketer’s  task  is  to  help  the  CEO/COO  see  …  
  23. 23. www.johngirard.net                                                                                    john@johngirard.net  23   John  P.  Girard,  Ph.D.  

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