Understanding analytics types and needs

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Research report that breaks down "analytics" into four categories, describes their attributes and aligns them with skills needed. Also advice to embarking on analytics programs.

Research report that breaks down "analytics" into four categories, describes their attributes and aligns them with skills needed. Also advice to embarking on analytics programs.

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  • 1. Hired BrainsUnderstanding  Analytics  Types  and  Needs  By  Neil  Raden,  January,  2013  Purpose  and  Intent  “Analytics”  is  a  critical  component  of  enterprise  architecture  capabilities,  though  most  organizations  have  only  recently  begun  to  develop  experience  using  quantitative  methods.  As  Information  Technology  emerges  from  a  scarcity-­‐based  mentality  of  constrained  and  costly  resources  to  a  commodity  consumption  model  of  data,  processors  and  tools,  analytics  is  quickly  becoming  table  stakes  for  competition.  This  report  is  the  first  of  a  two-­‐part  series.  (Part  II  will  cover  analytic  functionality  and  matching  the  right  technology  to  the  proper  analytic  tools  and  best  practices.)  It  discusses  the  importance  of  understanding  the  role  of  analytics,  why  it  is  a  difficult  topic  for  many,  and  what  actions  you  should  take.  It  will  explore  the  various  meanings  of  analytics,  provide  a  framework  for  aligning  various  types  of  analytics  with  associated  roles  and  skill  sets  needed.    Executive  Summary  Using  quantitative  methods  is  rapidly  becoming,  not  an  option  for  competitive  advantage,  but  rather,  at  the  very  least,  barely  enough  to  keep  up.  Everyone  needs  to  understand  what’s  involved  in  analytics,  what  you  particular  organization  needs  and  how  to  do  it.  Few  people  are  comfortable  with  the  concepts  of  advanced  analytic  methods.  In  fact,  most  people  cannot  explain  the  difference  between  a  mean,  a  median  and  a  sample  mean.  The  misapplication  of  statistics  is  widespread,  but  today’s  explosion  of  data  sources  and  intriguing  technologies  to  deal  with  them  have  changed  the  calculus.  Embedded  quantitative  methods  may  relieve  analysts  of  the  actual  construction  of  predictive  models,  but  applying  those  models  correctly  requires  understanding  the  different  analytical  types,  roles  and  skill.  Analytics  in  the  Enterprise  The  emphasis  of  analytics  is  changing  from  one  of  long-­‐range  planning  based  on  historical  data,  to  dynamic  and  adaptive  response  based  on  timely  information  from  multiple  contexts,  augmented  and  interpreted  through  various  degrees  of  quantitative  analysis.  Analytics  now  permeates  every  aspect  leading  organizations’  operations.  Competitive,  technological  and  economic  factors  combine  to  require  more  precision  and  less  lag  time  in  discovery  and  decision-­‐making.     Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 2. Hired BrainsFor  example,  operational  processing,  the  orchestration  of  business  processes  and  secure  capture  of  transactional  data  is  merging  with  analytical  processing,  the  gathering  and  processing  of  data  for  reporting  and  analysis.  Analytics  in  commercial  organizations  has  historically  been  limited  to  special  groups  working  more  or  less  off-­‐line.  Platforms  for  transaction  processing  were  separated  for  performance  and  security  reasons,  an  effect  of  “managing  from  scarcity.”  But  scarcity  is  not  the  issue  anymore  as  the  relative  cost  of  computing  has  plummeted.  Driven  equally  by  technology  and  competition,  operational  systems  are  either  absorbing  or  at  least  cooperating  with  analytical  processes.  This  convergence  elevates  the  visibility  of  all  forms  of  analytics.    Figure 1: Convergence of OLTP and AnalyticsConfusion  and  mistakes  in  deploying  analytics  are  common  due  to  imprecise  understanding  of  the  various  forms  and  types.  Uncertainty  about  the  staff  and  skills  needed  for  various  “types”  of  analytics  are  common.  Messaging  from  technology  vendors,  service  providers  and  analysts  is  murky  and  misleading,  sometimes  deliberately  so.    The  urgency  behind  implementing  an  analytics  program,  however,  can  be  driven  not  by  getting  a  leg  up,  but  rather  not  falling  behind.       Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 3. Hired BrainsAnalytics  and  the  Red  Queen  Effect  Analytics  are  crucial  because  the  barriers  to  getting  started  are  lower  than  ever.  Everyone  can  engage  in  analytics  now,  of  one  type  or  another.  As  analytic  capabilities  increase  across  competitors,  everyone  must  step  up  –  it’s  a  Red  Queeni  effect.    When  everyone  was  shooting  from  the  hip,  efficiency  was  a  matter  of  degree.  If  everyone  used  crude  models  and  unreliable  data,  then  everyone  should,  more  or  less,  work  within  the  same  margin  of  error.  What  separated  competitors  was  good  strategy  and  good  execution.  But  now  that  everyone  can  employ  quantitative  methods  and  techniques  like  Naive  Bayes,  C4.5  and  support  vector  machines,  it  will  still  be  the  strategy  and  execution  that  count.  Companies  must  improve  just  to  stay  in  place.    Each  new  level  of  analytics  becomes  the  “table  stakes”  for  the  next.      Can  You  Compete  on  Analytics?  Analytics  Are  Necessary  –  but  Not  Sufficient  Statistical  methods  using  software  have  been  shown  to  be  useful  in  many  aspects  of  an  organization,  such  as  fraud  detection,  demand  forecasting  and  inventory  management,  but  just  using  analytics  has  not  been  shown  to  necessarily  improve  the  fortunes  or  effectiveness  of  the  overall  organization.  In  2007,  Davenport  and  Harris  released  their  influential  bookii,  Competing  on  Analytics,  which  described  how  a  dozen  or  so  companies  used  “analytics”  to  not  only  advise  decision-­‐makers,  but  to  play  a  major  role  in  the  development  of  strategy  and  implementation  of  business  initiatives.  The  book  found  a  huge  following  and  was  a  bestseller  on  the  business  book  lists.  It  certainly  placed  the  word  “analytics”  in  the  top  of  the  mind  of  many  decision  makers.  However,  when  comparing  the  fortunes  of  the  twelve  companies  highlighted  in  the  book,  their  performance  in  the  stock  market  is  less  than  spectacular  as  illustrated  in  Figure  2:        Figure 2: Performance of Davenports Analytical Competitors This   150.0% scenario  is  often   repeated  –  good   100.0% work  is  performed   inside  an   50.0% organization,  but   the  benefits  of  the   0.0% discipline  do  not   % Growth permeate  other   -50.0% parts  of  the   business  and,   -100.0% 2005-2010 Stock Market Performance of Copyright 2013 Hired Brains Inc. 2005- 2010 Performance Reserved. May not be copied or duplicated and Neil Raden. All Rights Davenports Analytics Competitors 2005-2010expressPerformance without NYSE written consent
  • 4. Hired Brainshence,  have  little  effect  on  the  organization  as  a  whole.  In  another  example,  statistical  methods  have  been  used  in  the  U.S.  in  agriculture  for  decades,  and  yields  have  improved  dramatically,  but  the  quality  of  the  food  supply  has  clearly  degraded  along  with  the  fortunes  of  individual  farmers.      Too  many  organizations,  despite  good  intentions,  do  not  see  dramatic  improvement  in  their  fortunes  after  adopting  wider-­‐based  analytical  methods  because:  First,  rarely  does  one  thing  change  a  company.  Analytics  are  a  powerful  tool,  but  it  takes  execution  to  realize  the  benefits.  Perhaps  if  good  analytical  technique  had  been  applied  across  the  board  along  with  a  clear  strategy  to  drive  decisions  based  on  quantitative  models,  better  results  may  have  followed.  Instead,  as  is  often  the  case,  a  visible  project  shows  great  promise  and  early  results,  but  the  follow  through  is  wanting.  Data  mining  tools  can  actually  be  predictive,  showing  what  is  likely  to  happen  or  not  happen.  But  what  is  often  misunderstood  is  that  data  mining  tools  are  usually  poor  at  specifying  when  things  will  happen.  In  this  case,  too  much  faith  is  placed  in  the  models,  imbuing  them  with  fortune-­‐telling  capabilities  they  simply  lack.  The  correct  approach  is  to  test,  run  proofs  of  concept,  and  once  in  production  engage  in  continuous  improvement  through  mechanisms  like  champion/challenger  and  A/B  testing.  Most  of  the  companies  try  to  understand  customer  behavior  –  which  you  can  do  with  data  mining  –  but  it  rarely  captures  the  randomness  of  people’s  behavior  leading  to  overconfidence  in  the  models.  Given  this  customer  is  likely  to  purchase  a  car,  when  is  the  correct  time  to  reach  out?  Perhaps  right  away,  perhaps  not.  Data  mining  tools  are  not  very  good  at  individual  propensities  derived  from  behavior  due  to  the  randomness  of  human  behavior.  It  is  pretty  common  for  inexperienced  modelers  to  put  too  much  faith  in  model  results.  The  solution  is  to  engage  experienced  talent  to  get  a  program  started  in  the  right  track.  Return  on  investment  in  analytics  is  difficult  to  measure  because  there  isn’t  often  a  straight  line  from  the  model  to  results.  Other  parts  of  the  organization  contribute.  An  analytical  process  can  inform  decisions,  either  human  or  machine-­‐driven,  but  the  execution  of  those  decisions  is  beyond  the  reach  of  an  analytical  system.  People  and  process  have  to  perform  too.  In  addition,  a  successful  analytical  program  can  be  the  result  of  a  well-­‐defined  strategy.  Positive  results  from  analytics  would  not  have  been  possible  without  the  formation  of  that  strategy.  Professionals  skilled  in  statistics,  data  mining,  predictive  modeling  and  optimization  have  been  a  part  of  many  organizations  for  some  time,  but  their  contribution,  and  even  an  awareness  of  what  they  do,  is  sometimes  poorly  understood  –  and  filled  with  many  impediments  to  success.    By  categorizing  analytics  by  the  quantitative  techniques  used  and  the  level  of  skill  of  the  practitioners  who  use  these  techniques  (the  business   Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 5. Hired Brainsapplications  that  they  support  are  detailed  in  Part  II  of  the  series),  companies  can  begin  to  understand  when  and  how  to  use  analytics  effectively  and  deploy  their  analytic  resources  to  achieve  better  results.      The  Four  Types  of  Analytics  There  are  related  and  unrelated  disciplines  that  are  all  combined  under  the  term  analytics.  There  is  advanced  analytics,  descriptive  analytics,  predictive  analytics  and  business  analytics,  all  defined  in  a  pretty  murky  way.  It  cries  out  for  some  precision.  What  follows  is  a  way  to  characterize  the  many  types  of  analytics  by  the  quantitative  techniques  used  and  the  level  of  skill  of  the  practitioners  who  use  these  techniques.    Figure  4:  The  Four  Types  of  Analytics   Descriptive Quantitative Sample Roles Title Sophistication/Numeracy Creation of theory, development of algorithms. Type I Quantitative PhD or equivalent Academic/research. Often employed in business or Research government for very specialized roles Data Scientist Internal expert in statistical and or mathematical modeling andType II Advanced Math/Stat, not development, with solid Quantitative necessarily PhD business domain knowledge Analyst Running and managing analytical models. Strong skillsType III Operational Good business domain, in and/or project management Analytics background in statistics optional of analytical systems implementation Reporting, dashboard, OLAP and visualization use, possiblyType IV Business Data and numbers oriented, but design, Performing posterior Intelligence/ so special advanced statistical analysis of results driven by Discovery skills quantitative methods    Type  I  Analytics:  Quantitative  Research     Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 6. Hired BrainsThe  creation  of  theory  and  development  of  algorithms  for  all  forms  of  quantitative  analysis  deserves  the  title  Type  I.  Quantitative  Research  analytics  are  performed  by  mathematicians,  statisticians  and  other  pure  quantitative  scientists.  They  discover  new  ideas  and  concepts  in  mathematical  terms  and  develop  new  algorithms  with  names  like  Hidden  Markov  Support  Vector  Machines,  Linear  Dynamical  Systems,  Spectral  Clustering,  Machine  Learning  and  a  host  of  other  exotic  models.  The  discovery  and  enhancement  of  computer-­‐based  algorithms  for  these  concepts  is  mostly  the  realm  of  academia  and  other  research  institutions  (though  not  exclusively).    Commercial,  governmental  and  other  organizations  (Google  or  Wall  Street  for  example)  employ  staff  with  these  very  advanced  skills;  but  in  general,  most  organizations  are  able  to  conduct  their  necessary  analytics  without  them,  or  employ  the  results  of  their  research.  An  obvious  example  is  the  FICO  score,  developed  by  Quantitative  Research  experts  at  FICO  (Formerly  Fair  Isaac)  but  employed  widely  in  credit-­‐granting  institutions  and  even  human  resource  organizations.    Type  II  Analytics:  “Data  Scientists”    More  practical  than  theoretical,  Type  II  is  the  incorporation  of  advanced  analytical  approaches  derived  from  Type  I  activities.  This  includes  commercial  software  companies,  vertical  software  implementations,  and  even  the  heavy  “quants”  in  industry  who  apply  these  methods  specifically  to  the  work  they  do  like  fraud  detection,  failure  analysis,  propensity  to  consume  models,  among  hundreds  of  other  examples.  They  operate  in  much  the  same  way  as  commercial  software  companies  but  for  just  one  customer  (though  they  often  start  their  own  software  companies  too).  The  popular  term  for  this  role  is  “data  scientist.”   “Heavy”  Data  Scientists.  The  Type  II  category  could  actually  be  broken  down  into   two  subtypes,  Type  II-­‐A  and  Type  II-­‐B.  While  both  perform  roughly  the  same  function   –  providing  guidance  and  expertise  in  the  application  of  quantitative  analysis  –  they   are  differentiated  by  the  sophistication  of  the  techniques  applied.  II-­‐A  practitioners   understand  the  mathematics  behind  the  analytics  and  may  apply  very  complex  tools   such  as  Kucene  wrapper,  loopy  logic,  path  analysis,  root  cause  analysis,  synthetic   time  series  or  Naïve  Bayes  derivatives  that  are  understood  by  a  small  number  of   practitioners.  What  differentiates  the  Type  II-­‐A  from  Type  I  is  not  necessarily  the   depth  of  knowledge  they  have  about  the  formal  methods  of  analytics  (it  is  not   uncommon  for  Type  II’s  to  have  a  PhD  for  example),  it  is  that  they  also  possess  the   business  domain  knowledge  they  apply  and  their  goal  is  to  develop  specific  models   for  the  enterprise,  not  for  the  general  case  as  Type  I’s  usually  do.   Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 7. Hired Brains “Light”  Data  Scientists.  Type  II-­‐Bs  on  the  other  hand  may  work  with  more  common   and  well-­‐understood  techniques  such  as  logistic  regression,  ANOVA,  CHAID  and   various  forms  of  linear  regression.  They  approach  the  problems  they  deal  with  using   more  conventional  best  practices  and/or  packaged  analytical  solutions  from  third   parties  Data  Scientist  Confusion.  “Data  Scientist”  is  a  relatively  new  title  for  quantitatively  adept  people  with  accompanying  business  skills.  The  ability  to  formulate  and  apply  tools  to  classification,  prediction  and  even  optimization,  coupled  with  fairly  deep  understanding  of  the  business  itself,  is  clearly  in  the  realm  of  Type  II  efforts.  However,  it  seems  pretty  likely  that  most  so-­‐called  data  scientists  will  lean  more  towards  the  quantitative  and  data-­‐oriented  subjects  than  business  planning  and  strategy.  The  reason  for  this  is  that  the  term  data  scientist  emerged  from  those  businesses  like  Google  or  Facebook  where  the  data  is  the  business;  so  understanding  the  data  is  equivalent  to  understanding  the  business.  This  is  clearly  not  the  case  for  most  organizations.  We  see  very  few  Type  II  data  scientists  with  the  in-­‐depth  knowledge  of  the  whole  business  as,  say,  actuaries  in  the  insurance  business,  whose  extensive  training  should  be  a  model  for  the  newly  designated  data  scientists  (see  our  blog  at:  “What  is  a  Data  Scientist  and  What  Isn’t”)      Though  not  universally  accepted,  data  scientists  must  be  able  to  effectively  communicate  their  work  to  non-­‐technical  people.  This  is  a  major  discriminator  between  a  data  scientist  and  a  statistician.  It  is  absolutely  essential  that  someone  in  the  analytics  process  have  the  role  of  chief  communicator,  someone  who  is  comfortable  working  with  quants,  analysts  and  programmers,  deconstructing  their  methodologies  and  processes,  distilling  them,  and  then  rendering  it  in  language  that  other  stakeholders  understand.  Companies  often  fail  to  see  that  there  is  almost  never  anything  to  be  gained  by  trying  to  put  a  PhD  statistician  into  the  role  of  managing  a  group  of  analysts  and  developers.  It  is  safe  to  say  that  this  role  is  represented  more  by  a  collaborative  group  of  professionals  than  by  a  single  individual.  Type  III  Analytics:  Operational  Analytics    Historically,  this  is  the  part  of  analytics  we’re  most  familiar  with.  For  example,  a  data  scientist  may  develop  a  scoring  model  for  his/her  company.  In  Type  III  activity,  parameters  are  chosen  by  the  operational  analytics  expert  analyst  and  are  input  into  the  model,  generating  the  scores  calculated  by  the  Type  II  models  and  embedded  into  an  operational  system  that,  say,  generates  offers  for  credit  cards.  Models  developed  by  data  scientists  can  be  applied  and  embedded  in  an  almost  infinite  number  of  ways  today.  The  application  of  Type  II  applications  into  real  work  is  the  realm  of  operational   Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 8. Hired Brainsanalysts.  In  very  complex  applications,  real-­‐time  data  can  be  streamed  into  applications  based  on  Type  II  models  with  outcomes  instantaneously  derived  through  decision-­‐making  tools  such  as  rules  engines.    Packaged  applications  that  embed  quantitative  methods  such  as  predictive  modeling  or  optimizations  are  also  Type  III  in  that  the  intricacies  and  the  operation  of  the  statistical  or  stochastic  method  are  mostly  hidden  in  a  sort  of  “black  box.”  As  analytics  using  advanced  quantitative  methods  becomes  more  acceptable  to  management  over  time,  these  packages  become  more  popular.    Decision  making  systems  that  are  reliant  on  quantitative  methods  that  are  not  well  understood  by  the  operators  can  lead  to  trouble.  They  must  be  carefully  designed  (and  improved)  to  avoid  overly  burdening  the  recipients  of  useless  or  irrelevant  information.  This  was  a  lesson  learned  in  the  early  days  of  data  mining,  that  generating  “interesting”  results  without  understanding  what  was  relevant  usually  led  to  flagging  interest  in  the  technology.  In  today’s  business  environment,  time  is  perhaps  the  scarcest  commodity  of  all.  Whether  a  decision-­‐making  system  notifies  people  or  machines,  it  must  confine  those  messages  to  those  that  are  the  most  relevant  and  useful.    False  negatives  are  quite  a  bit  more  problematic  as  they  can  lead  to  transactions  passing  through  that  should  not  have.  Large  banks  have  gone  under  by  not  catching  trades  that  cost  billions  of  dollars.  Think  of  false  negatives  as  being  asleep  at  the  wheel.      Type  IV  Analytics:  Business  Intelligence  &  Discovery      Type  III  analytics  aren’t  of  much  value  if  their  application  in  real  business  situations  cannot  be  evaluated  for  their  effectiveness.  This  is  the  analytical  work  we  are  most  familiar  with  via  reports,  OLAP,  dashboards  and  visualizations.  This  includes  almost  any  activity  that  reviews  information  to  understand  what  happened  or  how  something  performed,  or  to  scan  and  free  associate  what  patterns  appear  from  analysis.  The  mathematics  involved  is  simple.  But  pulling  the  right  information  –  and  understanding  what  information  means  –  is  still  an  art  and  requires  both  business  sense  and  knowledge  about  sources  and  uses  of  the  data.      Know  Your  Needs  First  The  scope  of  analytics  is  vast,  ranging  from  the  familiar  features  of  business  intelligence  to  the  arcane  and  mysterious  world  of  applied  mathematics.  Organizations  need  to  be   Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 9. Hired Brainsclear  on  their  objectives  and  capabilities  before  funding  and  staffing  an  analytic  program.  Predictive  modeling  to  dramatically  improve  your  results  makes  for  good  reading,  but  the  reality  is  quite  different.  The  four  types  are  meant  to  help  you  understand  where  you  can  begin  or  advance.  These  categories  are  not  hard  and  fast.  Some  activities  are  clearly  a  blend  of  various  types.  But  the  point  is  to  add  some  clarity  to  the  term  “analytics”  in  order  to  understand  its  various  use  cases.  Tom  Davenport,  for  example,  advocated  creating  a  cadre  of  “PhDs  with  personality”  in  order  to  become  an  analytically  competitive  organization.  That  is  one  approach.  Implementing  analytics  as  part  of  other  enterprise  software  you  already  have  –  or  purchasing  a  specialized  application  that  is  already  used  and  vetted  in  your  industry  –  is  a  better  place  to  start.  Recommendations  Use  of  some  clear  terminology  can  avoid  confusion  within  your  organization,  not  just  internally,  but  in  communication  with  vendors  and  service  providers.  To  get  the  most  out  of  analytics:   1. Be  clear  about  what  you  need.    Having  clarity  on  the  meaning  of  analytics  has   clear  benefits.  Because  the  nature  of  analytics  is  a  little  mysterious  to  most   people,  a  vendor  statement  that  they  provide  “embedded  predictive  analytics”   can  no  longer  be  taken  at  face  value.  You  should  look  closely  to  see  if  those   capabilities  line  up  with  your  needs.   2. Don’t  assume  high  value  means  high  resource  costs.  In  the  same  vein,  you   needn’t  hesitate  to  begin  analytical  projects  because  you  believe  you  need  to   source  a  dozen  PhDs,  when  in  fact,  your  needs  are  in  the  Type  II  category.     3. Formulate  specific  vendor  questions  based  on  what  level  of  sophistication  and   resources  you  need.  By  more  clearly  specifying  what  type  of  analytics  you  need,   it  becomes  very  easy  to  ask:   Is  this  tool  designed  to  discover  and  create  predictive  models,  or  to  deploy   them  from  other  sources?   Do  you  offer  training  in  quantitative  methods  or  only  in  the  use  of  your   product?   Is  the  tool  designed  for  authoring  scoring  models  or  just  using  scored  values?     4. Use  analytic  knowledge  to  start  to  prepare  for  Big  Data.    Understanding  what   type  of  analytics  –  and  results  –  you  need  will  even  help  you  in  your  soon-­‐to-­‐be-­‐ serious  consideration  of  Big  Data  solutions,  including  Hadoop,  its  variants  and  its   Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 10. Hired Brains competitors,  all  of  which  use  variants  of  the  above  techniques  to  process  large   quantities  of  information.     Analytics  is  a  catchall  phrase,  but  understanding  the  various  uses  and  types   should  help  in  implementing  the  right  approach  for  accomplishing  the  tasks  at   hand.    It  should  also  help  in  discerning  what  is  meant  when  the  term  is  used,  as   almost  anything  can  be  called  analytics.    Next  Steps  Part  II  of  this  series  will  examine  in  depth  the  forms  that  analytics  take  in  the  organization  and  the  business  purposes  it  serves,  and  demonstrate  through  examples  and  case  studies  how  analytics  of  all  types  are  successfully  employed.  But  analytics  are  a  step  in  the  process.  Without  effective  decision-­‐making  practices  the  value  in  analytics  is  lost.  Part  III  of  this  series  will  deal  with  decision  making  and  decision  management.    Author  Bio:  Neil  Raden  Analyst,  Consultant  and  Author  in  Analytics  and  Decision  Science  Neil  Raden,  nraden@hiredbrains.com  is  the  founder  and  Principal  Analyst  at  Hired  Brains  Research,  a  provider  of  consulting  and  implementation  services  in  business  intelligence,  analytics  and  decision  managemen.  Hired  Brains  focuses  on  the  needs  of  organizations  and  capabilities  of  technology.  He  began  his  career  as  a  property  and  casualty  actuary  with  AIG  in  New  York  before  moving  into  predictive  analytics  services,  software  engineering,  and  systems  integration  with  experience  in  delivering  environments  for  decision  making  in  fields  as  diverse  as  health  care  to  nuclear  waste  management  to  cosmetics  marketing  and  many  others  in  between.      Business  and  Technology  Experience    Neil  has  decades  of  experience  in  implementing  analytical  systems.  In  1985,  he  started  Archer  Decision  Sciences  as  a  consulting  company  providing  advanced  quantitative  expertise  and  decision  support  system  design  and  implementation  services  in  the  financial  services,  telecom,  pharmaceutical,  consumer  products,  insurance,  manufacturing  and  utilities  industries,  to  organizations  such  as  Pepsi,  Merck,  Warner-­‐Lambert,  AlliedSignal,  United  Telecom,  Sprint,  Dun  &  Bradstreet,  C|Net,  ABB,  Premier  Healthcare,  Chubb,  Estee  Lauder,  GE,  and  many  others.  In  1991,  Archer  was  one  of  the  first  to  develop  large-­‐scale  (at  that  time)  data  warehouses  and  business  intelligence  environments,  and  Neil  took  a  leading  role  in  training,  speaking  and  publishing  in  the  field.  In  2003,  Neil  expanded  into  a  role  as  industry  analyst,  publishing  over  40  white  papers,  hundreds  of  articles,  blogs  and  research  reports.       Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 11. Hired BrainsMedia  Influence  Neil  is  the  co-­‐author  of  the  book,  Smart  (Enough)  Systems:  How  to  Deliver  Competitive  Advantage  by  Automating  Hidden  Decisions.    In  1995,  Neils  first  in  a  series  of  feature  articles  appeared  in  InformationWeek,  followed  by  dozens  of  others  in  magazines  and  journals.  In  1998,  he  formed  Hired  Brains  Magazine,  a  high-­‐end  monthly  for  professional  consultants.  Neil’s  blog  appears  at  hiredbrains.wordpress.com.  He  is  a  regular  contributor  to  expert  sites  such  as  LinkedIn  Groups,  Focus,  Quora  and  eBizQ,  as  well  as  contributed  article  to  Forbes  Magazine  in  2012  on  Big  Data.  Neil  was  also  an  early  Wikipedia  editor  and  administrator  in  areas  of  technology,  health  care  and  mathematics.    Industry  Recognition  In  1995,  he  was  the  first  lecturer  in  dimensional  data  modeling  and  evaluating  OLAP  systems  at  the  Data  Warehousing  Institute  (now  TDWI).  Appointed  Chairman  of  Advisory  Board  to  Sandia  National  Laboratories  overseeing  information  management  and  software  engineering  for  the  Nuclear  Weapons  Stockpile  Stewardship  and  Nuclear  Waste  Management  initiatives.  Member  of  the  Boulder  Business  Intelligence  Brain  Trust  (BBBT).  Keynote  speaker  at  dozens  of  events  in  North  America,  Europe  and  Asia.  Included  in  Top  50  of  Jonny  Bentwood’s  Technobabble  2.0  Top  Industry  Analyst  Tweeters.  Education  Neil  Raden  attended  Webster  College  (now  Webster  University)  in  St.  Louis,  and  received  a  B.A.  in  Mathematics  with  additional  advanced  study  in  mathematics  and  economics  at  Washington  University  and  the  New  York  University  Graduate  School  of  Business  (since  renamed  the  Stern  School  of  Business).  He  is  currently  pursuing  a  Masters  degree  at  the  Graduate  Institute  at  St  John’s  College  in  Santa  Fe,  New  Mexico     Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent
  • 12. Hired Brainsi The Red Queen is a concept from evolutionary biology first used in Matt Ridley, The RedQueen: Sex and the Evolution of Human Nature, (New York: Macmillan Publishing Co, 1994).The allusion is to the Red Queen in Lewis Carrolls Through the Looking-Glass, who had to keeprunning just to stay in place.ii Davenport, Harris, et al, “Competing on Analytics: The New Science of Winning,” New York,Harvard Business Press, 2007. Copyright 2013 Hired Brains Inc. and Neil Raden. All Rights Reserved. May not be copied or duplicated without express written consent