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WHT/082311	
  
	
  
Unleash	
  the	
  power	
  of	
  Big	
  Data	
  in	
  
your	
  legacy	
  Data	
  Warehouse	
  
	
  
Harro	
  M.	
  Wiersma	
  M.Sc.	
  
Big	
  Data	
  Guy	
  
WHT/082311	
  
§  Harro M. Wiersma
§  born 1976 in Groningen, the Netherlands
§  Master of Science – University of Phoenix (AZ)
Computer Information Systems
§  past: contractor (DBA / Project Management / Team Management)
§  Manager Database IKEA / Technical Lead Infrastructure Engineering Sunrise /
§  Department Head Service Engineering Opitz Consulting CH
§  Head of IT Data Warehouse at PostFinance
§  current: Big Data Guy – looking for nice challenges
§  main	
  focus	
  area‘s:	
  Telecom,	
  Finance	
  and	
  Retail.	
  
§  hobby‘s:	
  golf,	
  whisky,	
  freelance	
  sound	
  engineer	
  and	
  tv	
  producer.	
  
§  contact: h@rro.wiersma.info
WHO	
  AM	
  I	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
MAIN	
  PROBLEM	
  –	
  A	
  CLEAR	
  VIEW	
  
how can we prevent to get different results
from different systems
about
the same KPI’s?
	
  
how	
  can	
  we	
  use	
  our	
  own	
  data	
  
to	
  
support	
  our	
  opera+onal	
  processes?	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
KEEP	
  A	
  STRAIGHT	
  FOCUS	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
BIG	
  DATA	
  OR	
  RIGHT	
  DATA	
  
I‘m	
  not	
  interested	
  in	
  technology.	
  
I‘m	
  not	
  interested	
  in	
  data.	
  	
  
I	
  am	
  interested	
  in	
  translaRng	
  data	
  into	
  informaRon	
  for	
  
decision	
  making.	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
MORE	
  DATA,	
  WAY	
  MORE	
  DATA	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
TRACK	
  TWEETS	
  ...	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
TRACK	
  EMOTIONS	
  ...	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
www.realeyesit.com	
  
WHT/082311	
  
TRACK	
  MOVEMENTS	
  ...	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
www.retailnext.net	
  
WHT/082311	
  
CURRENT	
  DWH	
  CHALLENGES	
  
§  load-to-report, very unflexibile
§  longer nightly loads – is the night still long enough?
§  does the project-requester still now why (s)he needed the data when
finally delivered, or has an alternative solution been created in the
meanwhilea?
§  several different „sources-of-truth“ ...
§  how can we process these vast amounts of data?
§  how to implement new sources of untraditional data?
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
BIG	
  DATA	
  CHALLENGES	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
bDWH	
  –	
  BRINGING	
  BUSINESS	
  AND	
  IT	
  STRATEGIES	
  TOGETHER	
  
	
  
Leveraging	
  untradiRonal	
  sources,	
  social	
  media	
  and	
  transacRonal	
  data	
  to	
  gain	
  the	
  
elusive	
  360	
  degree	
  view	
  of	
  the	
  customer	
  and	
  your	
  business.	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
TRADITIONAL	
  DWH	
  INFRASTRUCTURE	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
TRADITIONAL	
  DWH	
  INFRASTRUCTURE
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
LET’S	
  SIMPLIFY	
  THIS	
  MESS	
  …	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
…	
  AND	
  BRING	
  BIG	
  DATA	
  INTO	
  THE	
  WAREHOUSE	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
THE	
  POWER	
  OF	
  BIG	
  DATA	
  –	
  THE	
  bDWH	
  CONCEPT	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
§  IT	
  does	
  knows	
  data	
  and	
  infrastructure	
  (only)	
  
§  Business	
  knows	
  the	
  intelligence	
  to	
  be	
  applied	
  to	
  the	
  data	
  to	
  derive	
  value	
  
§  Business	
  knows	
  how	
  to	
  discover	
  data	
  pa;erns	
  (manual	
  and	
  automated)	
  –	
  
Data	
  ScienRsts	
  
§  Business	
  understands	
  their	
  seman+cs	
  beVer	
  
§  Business	
  can	
  perform	
  data	
  interroga+on	
  in	
  an	
  experiment	
  and	
  associate	
  rules	
  
of	
  engagement	
  early	
  on	
  for	
  data	
  usefulness	
  
§  IT	
  can	
  create	
  reusable	
  reports	
  of	
  these	
  experimental	
  results.	
  
§  Business	
  can	
  siX	
  the	
  data	
  to	
  curate	
  the	
  context	
  
§  Big	
  Data	
  needs	
  to	
  be	
  curated	
  to	
  be	
  useful	
  
The	
  bDWH	
  concept	
  brings	
  Business	
  and	
  IT	
  together	
  to	
  create	
  	
  
added	
  value	
  
IN	
  WHAT	
  DOES	
  THE	
  bDWH	
  CONCEPT	
  DIFFER	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
THE	
  bDWH	
  PARADIGM	
  CHANGE	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
THE	
  COMPLETE	
  bDWH	
  VALUE	
  CHAIN	
  
20	
  
Collec+on	
  –	
  Structured,	
  unstructured	
  and	
  semi-­‐structured	
  data	
  from	
  mulRple	
  sources	
  
	
  
Inges+on	
  –	
  loading	
  vast	
  amounts	
  of	
  data	
  onto	
  a	
  single	
  data	
  hub	
  
	
  
Discovery	
  &	
  Cleansing	
  –	
  understanding	
  format	
  and	
  content;	
  clean	
  up	
  and	
  forma[ng	
  
	
  
Integra+on	
  –	
  linking,	
  enRty	
  extracRon,	
  enRty	
  resoluRon,	
  indexing	
  and	
  data	
  fusion	
  
	
  
Analysis	
  –	
  Intelligence,	
  staRsRcs,	
  predicRve	
  and	
  text	
  analyRcs,	
  machine	
  learning	
  
	
  
Delivery	
  –	
  querying,	
  visualizaRon,	
  real	
  Rme	
  delivery	
  on	
  enterprise-­‐class	
  availability	
  
Collec+on	
   Inges+on	
  
Discovery	
  	
  &	
  
Cleansing	
  
Integra+on	
   Analysis	
   Delivery	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
KEY	
  SUCCES	
  FACTORS	
  
§  Business	
  needs	
  to	
  drive	
  and	
  execute	
  the	
  bDWH	
  program	
  
§  Data	
  colloca+on	
  and	
  discovery	
  is	
  the	
  most	
  cri+cal	
  step	
  
§  Metadata	
  is	
  needed	
  to	
  process	
  the	
  data	
  prior	
  and	
  post	
  bDWH	
  
integraRon	
  
§  Data	
  quality	
  can	
  be	
  processed	
  by	
  integraRng	
  taxonomies	
  
§  Data	
  visualiza+on	
  is	
  needed	
  to	
  discover	
  data	
  
§  Metrics	
  and	
  metadata	
  will	
  be	
  the	
  bridge	
  to	
  integrate	
  to	
  the	
  bDWH	
  
§  Centralized	
  infrastructure	
  is	
  needed	
  to	
  create	
  a	
  data-­‐hub	
  
	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
§  Bring	
  together	
  exis+ng	
  internal	
  knowhow,	
  combine	
  it	
  with	
  external	
  
knowhow.	
  don‘t	
  silo	
  your	
  teams.	
  
§  It‘s	
  not	
  about	
  hardware,	
  it‘s	
  about	
  the	
  concept	
  and	
  way	
  of	
  thinking.	
  
§  Reusable	
  data,	
  but	
  which	
  data	
  is	
  the	
  ‚sole	
  truth‘?	
  
§  Who	
  owns	
  your	
  data?	
  do	
  they	
  really	
  want	
  to	
  have	
  transparency?	
  
§  Are	
  we	
  allowed	
  to	
  use	
  our	
  data	
  as	
  we	
  would	
  like	
  to?	
  
§  Think	
  of	
  new	
  and	
  future	
  business-­‐concepts	
  to	
  be	
  supported.	
  
FIRST	
  STEPS	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
The	
  challenge	
  facing	
  the	
  business	
  today	
  is	
  the	
  ability	
  to	
  influence	
  the	
  buyer	
  
decisions	
  in	
  a	
  window	
  of	
  opportunity	
  that	
  does	
  not	
  last	
  long.	
  The	
  analyRcs	
  
available	
  at	
  a	
  personalizaRon	
  level	
  drives	
  the	
  buyer	
  whether	
  it	
  is	
  choosing	
  a	
  
Doctor	
  or	
  buying	
  a	
  new	
  laptop.	
  
To	
  compete	
  in	
  this	
  new	
  era,	
  businesses	
  need	
  to	
  be	
  driven	
  by	
  data	
  and	
  analyRcs,	
  
which	
  are	
  largely	
  different	
  from	
  tradiRonal	
  transacRons	
  and	
  campaigns!	
  
Both	
  the	
  “GeneraRon	
  Z”	
  and	
  “Millennial	
  GeneraRon”	
  of	
  buyers	
  will	
  not	
  be	
  
swayed	
  by	
  tradiRonal	
  engagement	
  models	
  of	
  selling	
  products	
  and	
  services!	
  
FROM	
  TRANSACTIONAL	
  TO	
  BEHAVIOURAL	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
PREDICTIVE	
  BUSINESS	
  INTELLIGENCE	
  –	
  DATA	
  ANALYSIS	
  
§  you	
  know	
  what	
  you	
  know	
  –	
  perfect,	
  use	
  it!	
  
§  you	
  know	
  what	
  you	
  don‘t	
  know	
  –	
  learn	
  
§  you	
  don‘t	
  know	
  what	
  you	
  know	
  –	
  invesRgate	
  
§  you	
  don‘t	
  know	
  what	
  you	
  don‘t	
  know	
  –	
  find	
  someone	
  who	
  does!	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
§  Do	
  not	
  try	
  to	
  implement	
  without	
  integraRon	
  in	
  your	
  current	
  
landscape	
  
§  Find	
  a	
  easy	
  target,	
  for	
  example	
  your	
  data-­‐archive	
  
§  Collabora+on	
  is	
  key!	
  Learn	
  from	
  other	
  industries	
  
§  Create	
  cross-­‐func+onal	
  teams:	
  	
  IT	
  –	
  Analysts	
  –	
  Business	
  
§  Champion	
  business	
  value:	
  a	
  ROI	
  is	
  there!	
  
§  OrganizaRons	
  that	
  don’t	
  leverage	
  the	
  big	
  data	
  that	
  they	
  have,	
  risk	
  
losing	
  ground	
  to	
  their	
  compeRtors	
  
§  Get	
  on	
  it,	
  now!	
  
	
  
	
  
TAKE	
  AWAYS	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
This is the moment…
Are you ready?
Big	
  Data	
  is	
  a	
  Game	
  Changer	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
QUESTIONS	
  &	
  ANSWERS	
  
Harro M. Wiersma M.Sc.
h@rro.wiersma.info
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
REFERENCE	
  CASE	
  I	
  -­‐	
  FINANCE	
  
§  no	
  fixed	
  card-­‐limit	
  
§  acRve	
  transacRon	
  monitoring	
  based	
  on:	
  
§  customer	
  profile	
  
§  credit	
  raRng	
  firms	
  (4!	
  in	
  the	
  USA)	
  
§  acRve	
  balance	
  
§  payment	
  history	
  
§  result:	
  lower	
  security:	
  payment	
  in	
  profile:	
  only	
  signature,	
  
otherwise:	
  pincode	
  or	
  direct	
  contact	
  by	
  phone	
  with	
  AmEx	
  
§  result:	
  less	
  reversed	
  transacRons	
  (<3%)	
  -­‐>	
  lower	
  costs!	
  
§  result:	
  beVer	
  insight	
  in	
  customers	
  spending	
  -­‐>	
  predicRve	
  analyRcs!	
  
©	
  2013	
  Harro	
  M.	
  Wiersma	
  –	
  23	
  September	
  2013	
  –	
  Swiss	
  Big	
  Data	
  Usergroup	
  Zürich	
  
WHT/082311	
  
REFERENCE	
  CASE	
  II	
  -­‐	
  LOGISTICS	
  

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Unleash the power of Big Data in your existing Data Warehouse

  • 1. WHT/082311     Unleash  the  power  of  Big  Data  in   your  legacy  Data  Warehouse     Harro  M.  Wiersma  M.Sc.   Big  Data  Guy  
  • 2. WHT/082311   §  Harro M. Wiersma §  born 1976 in Groningen, the Netherlands §  Master of Science – University of Phoenix (AZ) Computer Information Systems §  past: contractor (DBA / Project Management / Team Management) §  Manager Database IKEA / Technical Lead Infrastructure Engineering Sunrise / §  Department Head Service Engineering Opitz Consulting CH §  Head of IT Data Warehouse at PostFinance §  current: Big Data Guy – looking for nice challenges §  main  focus  area‘s:  Telecom,  Finance  and  Retail.   §  hobby‘s:  golf,  whisky,  freelance  sound  engineer  and  tv  producer.   §  contact: h@rro.wiersma.info WHO  AM  I   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 3. WHT/082311   MAIN  PROBLEM  –  A  CLEAR  VIEW   how can we prevent to get different results from different systems about the same KPI’s?   how  can  we  use  our  own  data   to   support  our  opera+onal  processes?   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 4. WHT/082311   KEEP  A  STRAIGHT  FOCUS   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 5. WHT/082311   BIG  DATA  OR  RIGHT  DATA   I‘m  not  interested  in  technology.   I‘m  not  interested  in  data.     I  am  interested  in  translaRng  data  into  informaRon  for   decision  making.   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 6. WHT/082311   MORE  DATA,  WAY  MORE  DATA   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 7. WHT/082311   TRACK  TWEETS  ...   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 8. WHT/082311   TRACK  EMOTIONS  ...   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich   www.realeyesit.com  
  • 9. WHT/082311   TRACK  MOVEMENTS  ...   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich   www.retailnext.net  
  • 10. WHT/082311   CURRENT  DWH  CHALLENGES   §  load-to-report, very unflexibile §  longer nightly loads – is the night still long enough? §  does the project-requester still now why (s)he needed the data when finally delivered, or has an alternative solution been created in the meanwhilea? §  several different „sources-of-truth“ ... §  how can we process these vast amounts of data? §  how to implement new sources of untraditional data? ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 11. WHT/082311   BIG  DATA  CHALLENGES   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 12. WHT/082311   bDWH  –  BRINGING  BUSINESS  AND  IT  STRATEGIES  TOGETHER     Leveraging  untradiRonal  sources,  social  media  and  transacRonal  data  to  gain  the   elusive  360  degree  view  of  the  customer  and  your  business.   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 13. WHT/082311   TRADITIONAL  DWH  INFRASTRUCTURE   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 14. WHT/082311   TRADITIONAL  DWH  INFRASTRUCTURE ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 15. WHT/082311   LET’S  SIMPLIFY  THIS  MESS  …   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 16. WHT/082311   …  AND  BRING  BIG  DATA  INTO  THE  WAREHOUSE   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 17. WHT/082311   THE  POWER  OF  BIG  DATA  –  THE  bDWH  CONCEPT   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 18. WHT/082311   §  IT  does  knows  data  and  infrastructure  (only)   §  Business  knows  the  intelligence  to  be  applied  to  the  data  to  derive  value   §  Business  knows  how  to  discover  data  pa;erns  (manual  and  automated)  –   Data  ScienRsts   §  Business  understands  their  seman+cs  beVer   §  Business  can  perform  data  interroga+on  in  an  experiment  and  associate  rules   of  engagement  early  on  for  data  usefulness   §  IT  can  create  reusable  reports  of  these  experimental  results.   §  Business  can  siX  the  data  to  curate  the  context   §  Big  Data  needs  to  be  curated  to  be  useful   The  bDWH  concept  brings  Business  and  IT  together  to  create     added  value   IN  WHAT  DOES  THE  bDWH  CONCEPT  DIFFER   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 19. WHT/082311   THE  bDWH  PARADIGM  CHANGE   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 20. WHT/082311   THE  COMPLETE  bDWH  VALUE  CHAIN   20   Collec+on  –  Structured,  unstructured  and  semi-­‐structured  data  from  mulRple  sources     Inges+on  –  loading  vast  amounts  of  data  onto  a  single  data  hub     Discovery  &  Cleansing  –  understanding  format  and  content;  clean  up  and  forma[ng     Integra+on  –  linking,  enRty  extracRon,  enRty  resoluRon,  indexing  and  data  fusion     Analysis  –  Intelligence,  staRsRcs,  predicRve  and  text  analyRcs,  machine  learning     Delivery  –  querying,  visualizaRon,  real  Rme  delivery  on  enterprise-­‐class  availability   Collec+on   Inges+on   Discovery    &   Cleansing   Integra+on   Analysis   Delivery   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 21. WHT/082311   KEY  SUCCES  FACTORS   §  Business  needs  to  drive  and  execute  the  bDWH  program   §  Data  colloca+on  and  discovery  is  the  most  cri+cal  step   §  Metadata  is  needed  to  process  the  data  prior  and  post  bDWH   integraRon   §  Data  quality  can  be  processed  by  integraRng  taxonomies   §  Data  visualiza+on  is  needed  to  discover  data   §  Metrics  and  metadata  will  be  the  bridge  to  integrate  to  the  bDWH   §  Centralized  infrastructure  is  needed  to  create  a  data-­‐hub     ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 22. WHT/082311   §  Bring  together  exis+ng  internal  knowhow,  combine  it  with  external   knowhow.  don‘t  silo  your  teams.   §  It‘s  not  about  hardware,  it‘s  about  the  concept  and  way  of  thinking.   §  Reusable  data,  but  which  data  is  the  ‚sole  truth‘?   §  Who  owns  your  data?  do  they  really  want  to  have  transparency?   §  Are  we  allowed  to  use  our  data  as  we  would  like  to?   §  Think  of  new  and  future  business-­‐concepts  to  be  supported.   FIRST  STEPS   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 23. WHT/082311   The  challenge  facing  the  business  today  is  the  ability  to  influence  the  buyer   decisions  in  a  window  of  opportunity  that  does  not  last  long.  The  analyRcs   available  at  a  personalizaRon  level  drives  the  buyer  whether  it  is  choosing  a   Doctor  or  buying  a  new  laptop.   To  compete  in  this  new  era,  businesses  need  to  be  driven  by  data  and  analyRcs,   which  are  largely  different  from  tradiRonal  transacRons  and  campaigns!   Both  the  “GeneraRon  Z”  and  “Millennial  GeneraRon”  of  buyers  will  not  be   swayed  by  tradiRonal  engagement  models  of  selling  products  and  services!   FROM  TRANSACTIONAL  TO  BEHAVIOURAL   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 24. WHT/082311   PREDICTIVE  BUSINESS  INTELLIGENCE  –  DATA  ANALYSIS   §  you  know  what  you  know  –  perfect,  use  it!   §  you  know  what  you  don‘t  know  –  learn   §  you  don‘t  know  what  you  know  –  invesRgate   §  you  don‘t  know  what  you  don‘t  know  –  find  someone  who  does!   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 25. WHT/082311   §  Do  not  try  to  implement  without  integraRon  in  your  current   landscape   §  Find  a  easy  target,  for  example  your  data-­‐archive   §  Collabora+on  is  key!  Learn  from  other  industries   §  Create  cross-­‐func+onal  teams:    IT  –  Analysts  –  Business   §  Champion  business  value:  a  ROI  is  there!   §  OrganizaRons  that  don’t  leverage  the  big  data  that  they  have,  risk   losing  ground  to  their  compeRtors   §  Get  on  it,  now!       TAKE  AWAYS   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 26. WHT/082311   This is the moment… Are you ready? Big  Data  is  a  Game  Changer   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 27. WHT/082311   QUESTIONS  &  ANSWERS   Harro M. Wiersma M.Sc. h@rro.wiersma.info ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 28. WHT/082311   REFERENCE  CASE  I  -­‐  FINANCE   §  no  fixed  card-­‐limit   §  acRve  transacRon  monitoring  based  on:   §  customer  profile   §  credit  raRng  firms  (4!  in  the  USA)   §  acRve  balance   §  payment  history   §  result:  lower  security:  payment  in  profile:  only  signature,   otherwise:  pincode  or  direct  contact  by  phone  with  AmEx   §  result:  less  reversed  transacRons  (<3%)  -­‐>  lower  costs!   §  result:  beVer  insight  in  customers  spending  -­‐>  predicRve  analyRcs!   ©  2013  Harro  M.  Wiersma  –  23  September  2013  –  Swiss  Big  Data  Usergroup  Zürich  
  • 29. WHT/082311   REFERENCE  CASE  II  -­‐  LOGISTICS