SlideShare a Scribd company logo
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
	
  
Predic)ve	
  Analy)cs	
  in	
  Oracle:	
  	
  
Mining	
  the	
  Gold	
  &	
  Crea)ng	
  Valuable	
  Products	
  
	
  
	
  
Brendan Tierney
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
§  Data	
  Warehousing	
  since	
  1997	
  
§  Data	
  Mining	
  since	
  1998	
  
§  Analy)cs	
  since	
  1993	
  
Big	
  Data	
  –	
  Example	
  Applica)ons	
  
Not	
  all	
  of	
  these	
  are	
  using	
  Hadoop	
  or	
  require	
  Hadoop	
  or	
  …..	
  !	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
How	
  to	
  approach	
  a	
  Data	
  Science	
  Project	
  
Find	
  me	
  something	
  interes.ng	
  
in	
  my	
  data	
  is	
  a	
  ques.on	
  from	
  
hell.	
  	
  
	
  
Analy.cs	
  should	
  be	
  guided	
  by	
  
business	
  goals
Focus	
  hard	
  on	
  Business	
  
Ques.on	
  (and	
  the	
  relevant	
  	
  
variables)	
  that	
  captures	
  the	
  
essence	
  of	
  the	
  ques.on.
Before	
  you	
  can	
  measure	
  
something	
  you	
  really	
  need	
  to	
  
lay	
  down	
  a	
  very	
  concrete	
  
defini.on	
  of	
  what	
  you’re	
  
measuring
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Be	
  Specific	
  in	
  Problem	
  Statement	
  
Poorly	
  Defined	
   Be-er	
   Data	
  Mining	
  Technique	
  
Predict	
  employees	
  that	
  leave	
   •  Based	
  on	
  past	
  employees	
  that	
  voluntarily	
  leV:	
  
•  Create	
  New	
  AWribute	
  EmplTurnover	
  à	
  O/1	
  
Predict	
  customers	
  that	
  churn	
   •  Based	
  on	
  past	
  customers	
  that	
  have	
  churned:	
  
•  Create	
  New	
  AWribute	
  Churn ! YES/NO
Target	
  “best”	
  customers	
  	
   •  Recency,	
  Frequency	
  Monetary	
  (RFM)	
  Analysis	
  
•  Specific	
  Dollar	
  Amount	
  over	
  Time	
  Window:	
  	
  	
  
•  Who	
  has	
  spent	
  $500+	
  in	
  most	
  recent	
  18	
  months	
  
How	
  can	
  I	
  make	
  more	
  $$?	
   •  What	
  helps	
  me	
  sell	
  soV	
  drinks	
  &	
  coffee?	
  
Which	
  customers	
  are	
  likely	
  to	
  buy?	
   •  How	
  much	
  is	
  each	
  customer	
  likely	
  to	
  spend?	
  
Who	
  are	
  my	
  “best	
  customers”?	
   •  What	
  descrip)ve	
  “rules”	
  describe	
  “best	
  
customers”?	
  
How	
  can	
  I	
  combat	
  fraud?	
   •  Which	
  transac)ons	
  are	
  the	
  most	
  anomalous?	
  	
  	
  
•  Then	
  roll-­‐up	
  to	
  physician,	
  claimant,	
  employee,	
  etc.	
  
	
  
How	
  are	
  you	
  going	
  to	
  measure	
  	
  the	
  results?	
  
	
  
What	
  are	
  the	
  evalua)on	
  metrics?	
  
	
  
How	
  are	
  you	
  going	
  to	
  use	
  models	
  &	
  results?	
  
	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Be	
  Specific	
  in	
  Problem	
  Statement	
  
Poorly	
  Defined	
   Be-er	
   Data	
  Mining	
  Technique	
  
Predict	
  employees	
  that	
  leave	
   •  Based	
  on	
  past	
  employees	
  that	
  voluntarily	
  leV:	
  
•  Create	
  New	
  AWribute	
  EmplTurnover	
  à	
  O/1	
  
Predict	
  customers	
  that	
  churn	
   •  Based	
  on	
  past	
  customers	
  that	
  have	
  churned:	
  
•  Create	
  New	
  AWribute	
  Churn ! YES/NO
Target	
  “best”	
  customers	
  	
   •  Recency,	
  Frequency	
  Monetary	
  (RFM)	
  Analysis	
  
•  Specific	
  Dollar	
  Amount	
  over	
  Time	
  Window:	
  	
  	
  
•  Who	
  has	
  spent	
  $500+	
  in	
  most	
  recent	
  18	
  months	
  
How	
  can	
  I	
  make	
  more	
  $$?	
   •  What	
  helps	
  me	
  sell	
  soV	
  drinks	
  &	
  coffee?	
  
Which	
  customers	
  are	
  likely	
  to	
  buy?	
   •  How	
  much	
  is	
  each	
  customer	
  likely	
  to	
  spend?	
  
Who	
  are	
  my	
  “best	
  customers”?	
   •  What	
  descrip)ve	
  “rules”	
  describe	
  “best	
  
customers”?	
  
How	
  can	
  I	
  combat	
  fraud?	
   •  Which	
  transac)ons	
  are	
  the	
  most	
  anomalous?	
  	
  	
  
•  Then	
  roll-­‐up	
  to	
  physician,	
  claimant,	
  employee,	
  etc.	
  
	
  
How	
  are	
  you	
  going	
  to	
  measure	
  	
  the	
  results?	
  
	
  
What	
  are	
  the	
  evalua)on	
  metrics?	
  
	
  
	
  
I’ve	
  got	
  all	
  this	
  data;	
  	
  	
  
	
  	
  	
  	
  	
  	
  can	
  you	
  “mine”	
  it	
  and	
  find	
  useful	
  insights?	
  
	
  
	
  
	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Define	
  the	
  
Ques)on	
  
Why	
  is	
  this	
  
Topic	
  
Important	
  
(any	
  sub	
  
topics/areas)	
  
What	
  has	
  
been	
  done	
  
before	
  
What	
  are	
  the	
  
evalua)on	
  
measures?	
  
What	
  is	
  the	
  
relevant	
  data.	
  
	
  
(what	
  data	
  is	
  
accessible	
  and	
  
what	
  is	
  not)	
  
Define	
  
techniques	
  to	
  
use.	
  
How	
  are	
  you	
  
going	
  to	
  use	
  the	
  
results.	
  
	
  
Out	
  of	
  lab	
  and	
  
into	
  
Architecture	
  
How	
  
Frequently	
  are	
  
you	
  going	
  to	
  
revisit/update	
  
Predic)ve	
  Analy)cs	
  
Requirements	
  Gathering	
  
	
  
(6-­‐10	
  day	
  exercise)	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Define	
  the	
  
Ques)on	
  
Why	
  is	
  this	
  
Topic	
  
Important	
  
(any	
  sub	
  
topics/areas)	
  
What	
  has	
  
been	
  done	
  
before	
  
What	
  are	
  the	
  
evalua)on	
  
measures?	
  
What	
  is	
  the	
  
relevant	
  data.	
  
	
  
(what	
  data	
  is	
  
accessible	
  and	
  
what	
  is	
  not)	
  
Define	
  
techniques	
  to	
  
use.	
  
How	
  are	
  you	
  
going	
  to	
  use	
  the	
  
results.	
  
	
  
Out	
  of	
  lab	
  and	
  
into	
  
Architecture	
  
How	
  
Frequently	
  are	
  
you	
  going	
  to	
  
revisit/update	
  
Predic)ve	
  Analy)cs	
  
Requirements	
  Gathering	
  
	
  
(6-­‐10	
  day	
  exercise)	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
	
  	
  	
  	
  	
  Define	
  what	
  data	
  is	
  relevant	
  to	
  the	
  Ques)on/Specific	
  Problem	
  
–  What	
  data	
  is	
  easily	
  available	
  now	
  
–  What	
  data	
  is	
  not	
  easily	
  available	
  now	
  
–  What	
  data	
  do	
  you	
  not	
  have,	
  not	
  captured	
  etc.	
  
What	
  is	
  the	
  
relevant	
  data.	
  
	
  
(what	
  data	
  is	
  
accessible	
  and	
  
what	
  is	
  not)	
  
Your	
  Data	
  
hWps://hbr.org/2016/11/you-­‐dont-­‐need-­‐big-­‐data-­‐you-­‐need-­‐the-­‐right-­‐data	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
All	
  of	
  the	
  following	
  are	
  Real	
  projects	
  
§  on	
  Real	
  data	
  
§  using	
  Real	
  products	
  
§  on	
  Real	
  business	
  problems	
  
§  Are	
  full	
  cycle	
  implementa)ons	
  &	
  in	
  produc)on	
  
	
  
Most	
  Data	
  Science	
  	
  /	
  Predic)ve	
  Analy)cs	
  stories	
  you	
  hear	
  about	
  are	
  very	
  limited	
  
§  Many	
  only	
  exist	
  on	
  paper,	
  in	
  a	
  test	
  lab/environment,	
  on	
  a	
  presenta)on,	
  etc	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Fraud	
  Detec)on	
  
§  I’m	
  not	
  allowed	
  to	
  talk	
  about	
  what	
  I	
  did	
  
§  But	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Insurance	
  Fraud	
  
Insurers	
  discovered	
  a	
  total	
  118,500	
  false	
  claims	
  were	
  
made,	
  equivalent	
  to	
  2,279	
  a	
  week.	
  
§  Using	
  OAA	
  to	
  assess	
  each	
  Claim	
  as	
  it	
  is	
  received	
  
–  Iden)fy	
  possibility	
  of	
  it	
  being	
  a	
  Claim	
  
–  Iden)fy	
  possible	
  Claim	
  Amount	
  
–  Measure	
  of	
  Risk	
  Exposure	
  :	
  	
  Used	
  to	
  manage	
  work	
  flow	
  and	
  priority	
  
§  Works	
  in	
  conjunc)on	
  with	
  other	
  Fraud	
  preven)on	
  measures	
  
§  Supports	
  	
  Claim	
  Risk	
  Exposure	
  measures	
  
–  Various	
  regulatory,	
  group	
  and	
  share	
  holder	
  requirements	
  on	
  Risk	
  Exposure	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Retail	
  Banking	
  Fraud	
  
§  Using	
  OAA	
  being	
  used	
  to	
  monitor	
  retail	
  banking	
  transac)ons	
  
–  Iden)fy	
  unusual	
  paWerns	
  in	
  transac)ons	
  
–  Iden)fy	
  unusual	
  paWerns	
  on	
  accounts	
  
–  Iden)fy	
  unusual	
  paWerns	
  between	
  branches	
  
–  Iden)fy	
  unusual	
  Staff	
  behavior	
  	
  
§  Working	
  with	
  exis)ng	
  Freud	
  Detec)on	
  methods	
  to	
  give	
  
–  Addi)onal	
  insights	
  
–  Near	
  real-­‐)me	
  monitoring	
  
–  Working	
  within	
  their	
  exis)ng	
  Informa)on	
  Architecture	
  
§  Near	
  Real-­‐)me	
  Fraud	
  Preven)on	
  measures	
  
–  Previous/Current	
  Fraud	
  inves)ga)on	
  is	
  next	
  day	
  or	
  next	
  week	
  
–  Now	
  can	
  iden)fy	
  intra-­‐day,	
  	
  Fraud	
  teams	
  acts	
  quicker,	
  	
  etc	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
An	
  Post	
  
§  An	
  Post	
  has	
  made	
  innova)ve	
  use	
  of	
  Oracle’s	
  
business	
  intelligence	
  and	
  data	
  warehousing	
  systems	
  
to	
  deliver	
  efficiencies	
  across	
  a	
  range	
  of	
  areas,	
  
including	
  HR,	
  mail	
  processing	
  and	
  quality	
  of	
  service.	
  
§  Oracle’s	
  business	
  intelligence	
  suite	
  founda)on	
  
Edi)on,	
  which	
  provides	
  same-­‐day	
  view	
  of	
  cash	
  flow	
  
through	
  an	
  easy-­‐to-­‐use	
  dashboard	
  
•  Near	
  Real-­‐)me	
  Fraud	
  Preven)on	
  measures	
  
•  Previous/Current	
  Fraud	
  inves)ga)on	
  is	
  next	
  day	
  or	
  next	
  
week	
  
•  Now	
  can	
  iden)fy	
  intra-­‐day,	
  	
  Fraud	
  teams	
  acts	
  quicker,	
  	
  etc	
  
•  Iden)fy	
  unusual	
  paWerns	
  in	
  transac)ons	
  
•  Iden)fy	
  unusual	
  paWerns	
  on	
  accounts	
  
•  Iden)fy	
  unusual	
  paWerns	
  between	
  branches	
  
•  Iden)fy	
  unusual	
  Staff	
  behavior	
  	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Higher	
  Educa)on	
  
Example	
  :	
  Higher	
  EducaNon	
  
	
  
Student	
  Reten)on	
  	
  
	
  
Funding	
  Model	
  of	
  Universi)es	
  in	
  the	
  UK	
  
	
  
How	
  can	
  we	
  maximise	
  our	
  Student	
  Reten)on	
  and	
  increase	
  our	
  funding	
  
	
  
Can	
  we	
  manage	
  our	
  Student	
  selec)on	
  process	
  beWer?	
  
	
  
	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
OBIEE	
  Dashboard	
  
E-Learner
Female
Science“unknown”
Poor data quality
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
What	
  about	
  the	
  Money	
  ££££	
  
What	
  was	
  the	
  original	
  problem?	
  	
  
We	
  want	
  to	
  reduce	
  the	
  number	
  of	
  students	
  who	
  withdraw	
  early	
  from	
  their	
  
courses?	
  	
  (student	
  churn)	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  and	
  increase	
  our	
  funding	
  (revenue)	
  
	
  
Did	
  we	
  achieve	
  this	
  ?	
  
Typical	
  student	
  churn	
  of	
  2,300	
  per	
  year	
  	
  x	
  	
  £10K	
  =	
  (£23,000,000)	
  
82%	
  success	
  =	
  £18,860,000	
  of	
  poten.al	
  revenue	
  gain	
  
Implemented	
  using	
  a	
  mixture	
  of	
  	
  
Provide	
  beTer/addi.onal	
  student	
  support	
  
Be	
  more	
  selec.ve	
  with	
  making	
  offers	
  
Restructure	
  Courses	
  
Look	
  at	
  how	
  courses	
  are	
  adver.sed	
  and	
  entry	
  requirements	
  
	
  
You	
  can	
  imagine	
  how	
  
much	
  this	
  would	
  save	
  
across	
  all	
  the	
  133	
  
Universi)es	
  in	
  UK	
  
	
  
>	
  £1b	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
HCM	
  Analy)cs	
  
§  An	
  Oxford	
  economics	
  report	
  (Feb	
  2014)	
  es)mates	
  the	
  average	
  cost	
  per	
  
employee	
  is	
  £30,614:	
  
–  Lost	
  “cost	
  of	
  lost	
  output”	
  whilst	
  replacement	
  employees	
  get	
  up	
  to	
  speed	
  
–  The	
  “logical	
  lost”	
  of	
  recrui)ng	
  and	
  absorbing	
  a	
  new	
  worker	
  
§  Average	
  employee	
  turnover	
  rate	
  in	
  the	
  UK	
  is	
  approx	
  15%	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
HCM	
  Analy)cs	
  
§  Oracle	
  HCM	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
HCM	
  Analy)cs	
  
§  Major	
  World-­‐Wide	
  Financial	
  Ins)tu)on	
  
§  >300K	
  employees	
  
§  Employee	
  Churn.	
  	
  	
  	
  It	
  is	
  all	
  about	
  the	
  money?	
  or	
  promo)ons?	
  	
  	
  	
  Right?	
  
§  Not	
  what	
  we	
  discovered	
  
–  Employee	
  engagement,	
  training,	
  support,	
  regulatory	
  requirements,	
  staff	
  
requirements,	
  etc.	
  
–  93%	
  accuracy	
  
–  68%	
  of	
  these	
  had	
  monetary	
  reward	
  indicators	
  	
  
Some)mes	
  we	
  discover	
  
trends	
  that	
  are	
  not	
  
expected.	
  
	
  
Un-­‐biased	
  trends	
  
	
  
Can	
  be	
  difficult	
  to	
  accept	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Merchandising	
  Management	
  of	
  Outlets	
  
§  Supply	
  Chain	
  Management	
  
§  Ensuring	
  your	
  products	
  are	
  on	
  the	
  shelves	
  in	
  the	
  outlets	
  
§  Limited	
  Staff	
  to	
  visit	
  outlet	
  :	
  Who	
  should	
  they	
  be	
  targe)ng?	
  
§  Learn	
  from	
  the	
  Past	
  
§  >85%	
  accuracy	
  
11th	
  Feb,	
  2015	
  
Out	
  of	
  stock	
  considered	
  supply	
  chain	
  problem.	
  
Problem	
  is	
  'not	
  on	
  shelf'.	
  'Out	
  back'	
  no	
  good,	
  
according	
  to	
  consumers	
  
@dunnhumby	
  #BASummit2015	
  	
  	
  
Holland	
  
Belgium	
  
Spain	
  
Eastern	
  Canada	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Customer	
  Churn	
  Management	
  
§  Mobile	
  Phone	
  Companies	
  
§  Why	
  is	
  Customer	
  Churn	
  management	
  is	
  important?	
  
–  It	
  costs	
  a	
  lot	
  more	
  to	
  recruit	
  a	
  new	
  customer	
  than	
  to	
  keep	
  an	
  exis)ng	
  one.	
  
–  You	
  don	
  not	
  want	
  to	
  target	
  all	
  possible	
  churner.	
  
–  High	
  value	
  customers	
  :	
  How	
  to	
  you	
  determine	
  high	
  value?	
  
–  69%-­‐75%	
  accuracy	
  
§  Social	
  Network	
  Analysis	
  
–  How	
  big	
  is	
  your	
  Social	
  Network	
  
–  How	
  valuable	
  is	
  your	
  Social	
  Network	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
§  Tracking	
  customer	
  Sen)ment	
  –	
  Call	
  Centre	
  &	
  Customer	
  reten)on	
  
–  Part	
  of	
  Customer	
  Churn	
  management	
  
–  Combined	
  with	
  other	
  Predic)ve	
  Analy)cs	
  methods	
  
–  Ensemble	
  Data	
  Mining/Predic)ve	
  Analy)cs	
  
§  Can	
  we	
  predict	
  what	
  )meframe	
  they	
  might	
  churn?	
  
–  Is	
  this	
  Big	
  Data?	
  
•  Most	
  of	
  this	
  processing	
  is	
  done	
  on	
  a	
  Laptop/Desktop	
  
Customer	
  Sen)ment	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Is	
  Advanced	
  Analy)cs	
  for	
  you?	
  
§  If	
  you	
  have	
  Data	
  then	
  YES	
  
§  You	
  don’t	
  need	
  to	
  have	
  Big	
  Data	
  to	
  do	
  Advanced	
  Analy)cs	
  
§  You	
  don’t	
  need	
  to	
  hire	
  PhDs	
  or	
  Data	
  Scien)sts	
  
§  You	
  can	
  do	
  Advanced	
  Analy)cs	
  on	
  the	
  the	
  data	
  you	
  have.	
  
§  Do	
  you	
  have	
  any	
  historical	
  data?	
  	
  
§  Use	
  what	
  data	
  you	
  have	
  available	
  
–  As	
  new	
  data	
  becomes	
  available	
  you	
  can	
  add	
  these	
  in	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Everyone	
  only	
  talks	
  
up	
  to	
  this	
  point	
  
Nobody	
  talks	
  
about	
  Deployment	
  
Or	
  what	
  happens	
  
aVer	
  Deployment	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Create	
  Valuate	
  Products	
  
✓	
   ✗	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
	
  	
  
	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  brendan.)erney@oraly)cs.com	
  
	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  @brendan)erney	
  
	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  www.oraly)cs.com	
  
	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  ie.linkedin.com/in/brendan)erney	
  
 	
  	
  www.oraly)cs.com 	
  t	
  :	
  @brendan)erney 	
  e	
  :	
  brendan.)erney@oraly)cs.com	
   	
   	
   	
  	
  
Word	
  Cloud	
  of	
  the	
  Oracle	
  Advanced	
  
Analy)cs	
  web-­‐pages	
  
	
  
hWp://www.oraly)cs.com/2015/01/crea)ng-­‐word-­‐cloud-­‐of-­‐oracle-­‐oaa.html	
  

More Related Content

Similar to Predictive analytics: Mining gold and creating valuable product

Training Taster: Leading the way to become a data-driven organization
Training Taster: Leading the way to become a data-driven organizationTraining Taster: Leading the way to become a data-driven organization
Training Taster: Leading the way to become a data-driven organization
GoDataDriven
 
A picture is worth a thousand words
A picture is worth a thousand wordsA picture is worth a thousand words
A picture is worth a thousand words
Masum Billah
 
The Future of HR: From Metrics to Analytics [Webcast]
The Future of HR: From Metrics to Analytics [Webcast]The Future of HR: From Metrics to Analytics [Webcast]
The Future of HR: From Metrics to Analytics [Webcast]
LinkedIn Talent Solutions
 
What MBA Students Need to Know about CX, Data Science and Surveys
What MBA Students Need to Know about CX, Data Science and SurveysWhat MBA Students Need to Know about CX, Data Science and Surveys
What MBA Students Need to Know about CX, Data Science and Surveys
Business Over Broadway
 
Rd big data & analytics v1.0
Rd big data & analytics v1.0Rd big data & analytics v1.0
Rd big data & analytics v1.0
Yadu Balehosur
 
Huntel global webinar aligning data talent with your analytics needs
Huntel global webinar aligning data talent with your analytics needsHuntel global webinar aligning data talent with your analytics needs
Huntel global webinar aligning data talent with your analytics needs
Huntel Global
 
How to succeed at data without even trying!
How to succeed at data without even trying!How to succeed at data without even trying!
How to succeed at data without even trying!
Dylan
 
ONE-SIZE DOESN'T FIT ALL - EFFECTIVELY (RE)EVALUATE A DATA SOLUTION FOR YOUR ...
ONE-SIZE DOESN'T FIT ALL - EFFECTIVELY (RE)EVALUATE A DATA SOLUTION FOR YOUR ...ONE-SIZE DOESN'T FIT ALL - EFFECTIVELY (RE)EVALUATE A DATA SOLUTION FOR YOUR ...
ONE-SIZE DOESN'T FIT ALL - EFFECTIVELY (RE)EVALUATE A DATA SOLUTION FOR YOUR ...
DevOpsDays Tel Aviv
 
Planning a data solution - "By Failing to prepare, you are preparing to fail"
Planning a data solution - "By Failing to prepare, you are preparing to fail"Planning a data solution - "By Failing to prepare, you are preparing to fail"
Planning a data solution - "By Failing to prepare, you are preparing to fail"
Itai Yaffe
 
Big Data and Advertising
Big Data and AdvertisingBig Data and Advertising
Big Data and Advertising
MindEcology1
 
A6 big data_in_the_cloud
A6 big data_in_the_cloudA6 big data_in_the_cloud
A6 big data_in_the_cloud
Dr. Wilfred Lin (Ph.D.)
 
Conversion Hotel 2014: Craig Sullivan (UK) keynote
Conversion Hotel 2014: Craig Sullivan (UK) keynoteConversion Hotel 2014: Craig Sullivan (UK) keynote
Conversion Hotel 2014: Craig Sullivan (UK) keynote
Webanalisten .nl
 
20 top AB testing mistakes and how to avoid them
20 top AB testing mistakes and how to avoid them20 top AB testing mistakes and how to avoid them
20 top AB testing mistakes and how to avoid them
Craig Sullivan
 
Applying Data Quality Best Practices at Big Data Scale
Applying Data Quality Best Practices at Big Data ScaleApplying Data Quality Best Practices at Big Data Scale
Applying Data Quality Best Practices at Big Data Scale
Precisely
 
From Asset to Impact - Presentation to ICS Data Protection Conference 2011
From Asset to Impact - Presentation to ICS Data Protection Conference 2011From Asset to Impact - Presentation to ICS Data Protection Conference 2011
From Asset to Impact - Presentation to ICS Data Protection Conference 2011
Castlebridge Associates
 
Scientific Revenue USF 2016 talk
Scientific Revenue USF 2016 talkScientific Revenue USF 2016 talk
Scientific Revenue USF 2016 talk
ScientificRevenue
 
Why Big Data is Really about Small Data
Why Big Data is Really about Small DataWhy Big Data is Really about Small Data
Why Big Data is Really about Small Data
Hurwitz & Associates
 
lec01-IntroductionToDataMining.pptx
lec01-IntroductionToDataMining.pptxlec01-IntroductionToDataMining.pptx
lec01-IntroductionToDataMining.pptx
AmjadAlDgour
 
What Managers Need to Know about Data Science
What Managers Need to Know about Data ScienceWhat Managers Need to Know about Data Science
What Managers Need to Know about Data Science
Annie Flippo
 
Marketing Strategy Hacks
Marketing Strategy HacksMarketing Strategy Hacks
Marketing Strategy Hacks
April Dunford
 

Similar to Predictive analytics: Mining gold and creating valuable product (20)

Training Taster: Leading the way to become a data-driven organization
Training Taster: Leading the way to become a data-driven organizationTraining Taster: Leading the way to become a data-driven organization
Training Taster: Leading the way to become a data-driven organization
 
A picture is worth a thousand words
A picture is worth a thousand wordsA picture is worth a thousand words
A picture is worth a thousand words
 
The Future of HR: From Metrics to Analytics [Webcast]
The Future of HR: From Metrics to Analytics [Webcast]The Future of HR: From Metrics to Analytics [Webcast]
The Future of HR: From Metrics to Analytics [Webcast]
 
What MBA Students Need to Know about CX, Data Science and Surveys
What MBA Students Need to Know about CX, Data Science and SurveysWhat MBA Students Need to Know about CX, Data Science and Surveys
What MBA Students Need to Know about CX, Data Science and Surveys
 
Rd big data & analytics v1.0
Rd big data & analytics v1.0Rd big data & analytics v1.0
Rd big data & analytics v1.0
 
Huntel global webinar aligning data talent with your analytics needs
Huntel global webinar aligning data talent with your analytics needsHuntel global webinar aligning data talent with your analytics needs
Huntel global webinar aligning data talent with your analytics needs
 
How to succeed at data without even trying!
How to succeed at data without even trying!How to succeed at data without even trying!
How to succeed at data without even trying!
 
ONE-SIZE DOESN'T FIT ALL - EFFECTIVELY (RE)EVALUATE A DATA SOLUTION FOR YOUR ...
ONE-SIZE DOESN'T FIT ALL - EFFECTIVELY (RE)EVALUATE A DATA SOLUTION FOR YOUR ...ONE-SIZE DOESN'T FIT ALL - EFFECTIVELY (RE)EVALUATE A DATA SOLUTION FOR YOUR ...
ONE-SIZE DOESN'T FIT ALL - EFFECTIVELY (RE)EVALUATE A DATA SOLUTION FOR YOUR ...
 
Planning a data solution - "By Failing to prepare, you are preparing to fail"
Planning a data solution - "By Failing to prepare, you are preparing to fail"Planning a data solution - "By Failing to prepare, you are preparing to fail"
Planning a data solution - "By Failing to prepare, you are preparing to fail"
 
Big Data and Advertising
Big Data and AdvertisingBig Data and Advertising
Big Data and Advertising
 
A6 big data_in_the_cloud
A6 big data_in_the_cloudA6 big data_in_the_cloud
A6 big data_in_the_cloud
 
Conversion Hotel 2014: Craig Sullivan (UK) keynote
Conversion Hotel 2014: Craig Sullivan (UK) keynoteConversion Hotel 2014: Craig Sullivan (UK) keynote
Conversion Hotel 2014: Craig Sullivan (UK) keynote
 
20 top AB testing mistakes and how to avoid them
20 top AB testing mistakes and how to avoid them20 top AB testing mistakes and how to avoid them
20 top AB testing mistakes and how to avoid them
 
Applying Data Quality Best Practices at Big Data Scale
Applying Data Quality Best Practices at Big Data ScaleApplying Data Quality Best Practices at Big Data Scale
Applying Data Quality Best Practices at Big Data Scale
 
From Asset to Impact - Presentation to ICS Data Protection Conference 2011
From Asset to Impact - Presentation to ICS Data Protection Conference 2011From Asset to Impact - Presentation to ICS Data Protection Conference 2011
From Asset to Impact - Presentation to ICS Data Protection Conference 2011
 
Scientific Revenue USF 2016 talk
Scientific Revenue USF 2016 talkScientific Revenue USF 2016 talk
Scientific Revenue USF 2016 talk
 
Why Big Data is Really about Small Data
Why Big Data is Really about Small DataWhy Big Data is Really about Small Data
Why Big Data is Really about Small Data
 
lec01-IntroductionToDataMining.pptx
lec01-IntroductionToDataMining.pptxlec01-IntroductionToDataMining.pptx
lec01-IntroductionToDataMining.pptx
 
What Managers Need to Know about Data Science
What Managers Need to Know about Data ScienceWhat Managers Need to Know about Data Science
What Managers Need to Know about Data Science
 
Marketing Strategy Hacks
Marketing Strategy HacksMarketing Strategy Hacks
Marketing Strategy Hacks
 

More from Brendan Tierney

Ireland OUG Meetup May 2017
Ireland OUG Meetup May 2017Ireland OUG Meetup May 2017
Ireland OUG Meetup May 2017
Brendan Tierney
 
Embedded R Execution using SQL
Embedded R Execution using SQLEmbedded R Execution using SQL
Embedded R Execution using SQL
Brendan Tierney
 
Introduction to Map-Reduce
Introduction to Map-ReduceIntroduction to Map-Reduce
Introduction to Map-Reduce
Brendan Tierney
 
Overview of Hadoop and HDFS
Overview of Hadoop and HDFSOverview of Hadoop and HDFS
Overview of Hadoop and HDFS
Brendan Tierney
 
Overview of running R in the Oracle Database
Overview of running R in the Oracle DatabaseOverview of running R in the Oracle Database
Overview of running R in the Oracle Database
Brendan Tierney
 
OUG Ireland Meet-up - Updates from Oracle Open World 2016
OUG Ireland Meet-up - Updates from Oracle Open World 2016OUG Ireland Meet-up - Updates from Oracle Open World 2016
OUG Ireland Meet-up - Updates from Oracle Open World 2016
Brendan Tierney
 
OUG Ireland Meet-up 12th January
OUG Ireland Meet-up 12th JanuaryOUG Ireland Meet-up 12th January
OUG Ireland Meet-up 12th January
Brendan Tierney
 

More from Brendan Tierney (7)

Ireland OUG Meetup May 2017
Ireland OUG Meetup May 2017Ireland OUG Meetup May 2017
Ireland OUG Meetup May 2017
 
Embedded R Execution using SQL
Embedded R Execution using SQLEmbedded R Execution using SQL
Embedded R Execution using SQL
 
Introduction to Map-Reduce
Introduction to Map-ReduceIntroduction to Map-Reduce
Introduction to Map-Reduce
 
Overview of Hadoop and HDFS
Overview of Hadoop and HDFSOverview of Hadoop and HDFS
Overview of Hadoop and HDFS
 
Overview of running R in the Oracle Database
Overview of running R in the Oracle DatabaseOverview of running R in the Oracle Database
Overview of running R in the Oracle Database
 
OUG Ireland Meet-up - Updates from Oracle Open World 2016
OUG Ireland Meet-up - Updates from Oracle Open World 2016OUG Ireland Meet-up - Updates from Oracle Open World 2016
OUG Ireland Meet-up - Updates from Oracle Open World 2016
 
OUG Ireland Meet-up 12th January
OUG Ireland Meet-up 12th JanuaryOUG Ireland Meet-up 12th January
OUG Ireland Meet-up 12th January
 

Recently uploaded

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Edge AI and Vision Alliance
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Miro Wengner
 
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
Fwdays
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Jason Yip
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
DianaGray10
 
Christine's Product Research Presentation.pptx
Christine's Product Research Presentation.pptxChristine's Product Research Presentation.pptx
Christine's Product Research Presentation.pptx
christinelarrosa
 
Apps Break Data
Apps Break DataApps Break Data
Apps Break Data
Ivo Velitchkov
 
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge GraphGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Neo4j
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Brandon Minnick, MBA
 
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
AstuteBusiness
 
High performance Serverless Java on AWS- GoTo Amsterdam 2024
High performance Serverless Java on AWS- GoTo Amsterdam 2024High performance Serverless Java on AWS- GoTo Amsterdam 2024
High performance Serverless Java on AWS- GoTo Amsterdam 2024
Vadym Kazulkin
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
Pablo Gómez Abajo
 
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Pitangent Analytics & Technology Solutions Pvt. Ltd
 
Northern Engraving | Modern Metal Trim, Nameplates and Appliance Panels
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving | Modern Metal Trim, Nameplates and Appliance Panels
Northern Engraving | Modern Metal Trim, Nameplates and Appliance Panels
Northern Engraving
 
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Neo4j
 
Must Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during MigrationMust Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during Migration
Mydbops
 
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
MichaelKnudsen27
 
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
saastr
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
saastr
 

Recently uploaded (20)

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
 
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
 
Christine's Product Research Presentation.pptx
Christine's Product Research Presentation.pptxChristine's Product Research Presentation.pptx
Christine's Product Research Presentation.pptx
 
Apps Break Data
Apps Break DataApps Break Data
Apps Break Data
 
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge GraphGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
 
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
 
High performance Serverless Java on AWS- GoTo Amsterdam 2024
High performance Serverless Java on AWS- GoTo Amsterdam 2024High performance Serverless Java on AWS- GoTo Amsterdam 2024
High performance Serverless Java on AWS- GoTo Amsterdam 2024
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
 
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
 
Northern Engraving | Modern Metal Trim, Nameplates and Appliance Panels
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving | Modern Metal Trim, Nameplates and Appliance Panels
Northern Engraving | Modern Metal Trim, Nameplates and Appliance Panels
 
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
 
Must Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during MigrationMust Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during Migration
 
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
 
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
 

Predictive analytics: Mining gold and creating valuable product

  • 1.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com             Predic)ve  Analy)cs  in  Oracle:     Mining  the  Gold  &  Crea)ng  Valuable  Products       Brendan Tierney
  • 2.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           §  Data  Warehousing  since  1997   §  Data  Mining  since  1998   §  Analy)cs  since  1993  
  • 3. Big  Data  –  Example  Applica)ons   Not  all  of  these  are  using  Hadoop  or  require  Hadoop  or  …..  !  
  • 4.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           How  to  approach  a  Data  Science  Project   Find  me  something  interes.ng   in  my  data  is  a  ques.on  from   hell.       Analy.cs  should  be  guided  by   business  goals Focus  hard  on  Business   Ques.on  (and  the  relevant     variables)  that  captures  the   essence  of  the  ques.on. Before  you  can  measure   something  you  really  need  to   lay  down  a  very  concrete   defini.on  of  what  you’re   measuring
  • 5.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Be  Specific  in  Problem  Statement   Poorly  Defined   Be-er   Data  Mining  Technique   Predict  employees  that  leave   •  Based  on  past  employees  that  voluntarily  leV:   •  Create  New  AWribute  EmplTurnover  à  O/1   Predict  customers  that  churn   •  Based  on  past  customers  that  have  churned:   •  Create  New  AWribute  Churn ! YES/NO Target  “best”  customers     •  Recency,  Frequency  Monetary  (RFM)  Analysis   •  Specific  Dollar  Amount  over  Time  Window:       •  Who  has  spent  $500+  in  most  recent  18  months   How  can  I  make  more  $$?   •  What  helps  me  sell  soV  drinks  &  coffee?   Which  customers  are  likely  to  buy?   •  How  much  is  each  customer  likely  to  spend?   Who  are  my  “best  customers”?   •  What  descrip)ve  “rules”  describe  “best   customers”?   How  can  I  combat  fraud?   •  Which  transac)ons  are  the  most  anomalous?       •  Then  roll-­‐up  to  physician,  claimant,  employee,  etc.     How  are  you  going  to  measure    the  results?     What  are  the  evalua)on  metrics?     How  are  you  going  to  use  models  &  results?    
  • 6.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Be  Specific  in  Problem  Statement   Poorly  Defined   Be-er   Data  Mining  Technique   Predict  employees  that  leave   •  Based  on  past  employees  that  voluntarily  leV:   •  Create  New  AWribute  EmplTurnover  à  O/1   Predict  customers  that  churn   •  Based  on  past  customers  that  have  churned:   •  Create  New  AWribute  Churn ! YES/NO Target  “best”  customers     •  Recency,  Frequency  Monetary  (RFM)  Analysis   •  Specific  Dollar  Amount  over  Time  Window:       •  Who  has  spent  $500+  in  most  recent  18  months   How  can  I  make  more  $$?   •  What  helps  me  sell  soV  drinks  &  coffee?   Which  customers  are  likely  to  buy?   •  How  much  is  each  customer  likely  to  spend?   Who  are  my  “best  customers”?   •  What  descrip)ve  “rules”  describe  “best   customers”?   How  can  I  combat  fraud?   •  Which  transac)ons  are  the  most  anomalous?       •  Then  roll-­‐up  to  physician,  claimant,  employee,  etc.     How  are  you  going  to  measure    the  results?     What  are  the  evalua)on  metrics?       I’ve  got  all  this  data;                  can  you  “mine”  it  and  find  useful  insights?        
  • 7.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Define  the   Ques)on   Why  is  this   Topic   Important   (any  sub   topics/areas)   What  has   been  done   before   What  are  the   evalua)on   measures?   What  is  the   relevant  data.     (what  data  is   accessible  and   what  is  not)   Define   techniques  to   use.   How  are  you   going  to  use  the   results.     Out  of  lab  and   into   Architecture   How   Frequently  are   you  going  to   revisit/update   Predic)ve  Analy)cs   Requirements  Gathering     (6-­‐10  day  exercise)  
  • 8.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Define  the   Ques)on   Why  is  this   Topic   Important   (any  sub   topics/areas)   What  has   been  done   before   What  are  the   evalua)on   measures?   What  is  the   relevant  data.     (what  data  is   accessible  and   what  is  not)   Define   techniques  to   use.   How  are  you   going  to  use  the   results.     Out  of  lab  and   into   Architecture   How   Frequently  are   you  going  to   revisit/update   Predic)ve  Analy)cs   Requirements  Gathering     (6-­‐10  day  exercise)  
  • 9.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com                    Define  what  data  is  relevant  to  the  Ques)on/Specific  Problem   –  What  data  is  easily  available  now   –  What  data  is  not  easily  available  now   –  What  data  do  you  not  have,  not  captured  etc.   What  is  the   relevant  data.     (what  data  is   accessible  and   what  is  not)   Your  Data   hWps://hbr.org/2016/11/you-­‐dont-­‐need-­‐big-­‐data-­‐you-­‐need-­‐the-­‐right-­‐data  
  • 10.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           All  of  the  following  are  Real  projects   §  on  Real  data   §  using  Real  products   §  on  Real  business  problems   §  Are  full  cycle  implementa)ons  &  in  produc)on     Most  Data  Science    /  Predic)ve  Analy)cs  stories  you  hear  about  are  very  limited   §  Many  only  exist  on  paper,  in  a  test  lab/environment,  on  a  presenta)on,  etc  
  • 11.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Fraud  Detec)on   §  I’m  not  allowed  to  talk  about  what  I  did   §  But  
  • 12.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Insurance  Fraud   Insurers  discovered  a  total  118,500  false  claims  were   made,  equivalent  to  2,279  a  week.   §  Using  OAA  to  assess  each  Claim  as  it  is  received   –  Iden)fy  possibility  of  it  being  a  Claim   –  Iden)fy  possible  Claim  Amount   –  Measure  of  Risk  Exposure  :    Used  to  manage  work  flow  and  priority   §  Works  in  conjunc)on  with  other  Fraud  preven)on  measures   §  Supports    Claim  Risk  Exposure  measures   –  Various  regulatory,  group  and  share  holder  requirements  on  Risk  Exposure  
  • 13.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Retail  Banking  Fraud   §  Using  OAA  being  used  to  monitor  retail  banking  transac)ons   –  Iden)fy  unusual  paWerns  in  transac)ons   –  Iden)fy  unusual  paWerns  on  accounts   –  Iden)fy  unusual  paWerns  between  branches   –  Iden)fy  unusual  Staff  behavior     §  Working  with  exis)ng  Freud  Detec)on  methods  to  give   –  Addi)onal  insights   –  Near  real-­‐)me  monitoring   –  Working  within  their  exis)ng  Informa)on  Architecture   §  Near  Real-­‐)me  Fraud  Preven)on  measures   –  Previous/Current  Fraud  inves)ga)on  is  next  day  or  next  week   –  Now  can  iden)fy  intra-­‐day,    Fraud  teams  acts  quicker,    etc  
  • 14.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           An  Post   §  An  Post  has  made  innova)ve  use  of  Oracle’s   business  intelligence  and  data  warehousing  systems   to  deliver  efficiencies  across  a  range  of  areas,   including  HR,  mail  processing  and  quality  of  service.   §  Oracle’s  business  intelligence  suite  founda)on   Edi)on,  which  provides  same-­‐day  view  of  cash  flow   through  an  easy-­‐to-­‐use  dashboard   •  Near  Real-­‐)me  Fraud  Preven)on  measures   •  Previous/Current  Fraud  inves)ga)on  is  next  day  or  next   week   •  Now  can  iden)fy  intra-­‐day,    Fraud  teams  acts  quicker,    etc   •  Iden)fy  unusual  paWerns  in  transac)ons   •  Iden)fy  unusual  paWerns  on  accounts   •  Iden)fy  unusual  paWerns  between  branches   •  Iden)fy  unusual  Staff  behavior    
  • 15.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Higher  Educa)on   Example  :  Higher  EducaNon     Student  Reten)on       Funding  Model  of  Universi)es  in  the  UK     How  can  we  maximise  our  Student  Reten)on  and  increase  our  funding     Can  we  manage  our  Student  selec)on  process  beWer?      
  • 16.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           OBIEE  Dashboard   E-Learner Female Science“unknown” Poor data quality
  • 17.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           What  about  the  Money  ££££   What  was  the  original  problem?     We  want  to  reduce  the  number  of  students  who  withdraw  early  from  their   courses?    (student  churn)                                                      and  increase  our  funding  (revenue)     Did  we  achieve  this  ?   Typical  student  churn  of  2,300  per  year    x    £10K  =  (£23,000,000)   82%  success  =  £18,860,000  of  poten.al  revenue  gain   Implemented  using  a  mixture  of     Provide  beTer/addi.onal  student  support   Be  more  selec.ve  with  making  offers   Restructure  Courses   Look  at  how  courses  are  adver.sed  and  entry  requirements     You  can  imagine  how   much  this  would  save   across  all  the  133   Universi)es  in  UK     >  £1b  
  • 18.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           HCM  Analy)cs   §  An  Oxford  economics  report  (Feb  2014)  es)mates  the  average  cost  per   employee  is  £30,614:   –  Lost  “cost  of  lost  output”  whilst  replacement  employees  get  up  to  speed   –  The  “logical  lost”  of  recrui)ng  and  absorbing  a  new  worker   §  Average  employee  turnover  rate  in  the  UK  is  approx  15%  
  • 19.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           HCM  Analy)cs   §  Oracle  HCM  
  • 20.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           HCM  Analy)cs   §  Major  World-­‐Wide  Financial  Ins)tu)on   §  >300K  employees   §  Employee  Churn.        It  is  all  about  the  money?  or  promo)ons?        Right?   §  Not  what  we  discovered   –  Employee  engagement,  training,  support,  regulatory  requirements,  staff   requirements,  etc.   –  93%  accuracy   –  68%  of  these  had  monetary  reward  indicators     Some)mes  we  discover   trends  that  are  not   expected.     Un-­‐biased  trends     Can  be  difficult  to  accept  
  • 21.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Merchandising  Management  of  Outlets   §  Supply  Chain  Management   §  Ensuring  your  products  are  on  the  shelves  in  the  outlets   §  Limited  Staff  to  visit  outlet  :  Who  should  they  be  targe)ng?   §  Learn  from  the  Past   §  >85%  accuracy   11th  Feb,  2015   Out  of  stock  considered  supply  chain  problem.   Problem  is  'not  on  shelf'.  'Out  back'  no  good,   according  to  consumers   @dunnhumby  #BASummit2015       Holland   Belgium   Spain   Eastern  Canada  
  • 22.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Customer  Churn  Management   §  Mobile  Phone  Companies   §  Why  is  Customer  Churn  management  is  important?   –  It  costs  a  lot  more  to  recruit  a  new  customer  than  to  keep  an  exis)ng  one.   –  You  don  not  want  to  target  all  possible  churner.   –  High  value  customers  :  How  to  you  determine  high  value?   –  69%-­‐75%  accuracy   §  Social  Network  Analysis   –  How  big  is  your  Social  Network   –  How  valuable  is  your  Social  Network  
  • 23.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           §  Tracking  customer  Sen)ment  –  Call  Centre  &  Customer  reten)on   –  Part  of  Customer  Churn  management   –  Combined  with  other  Predic)ve  Analy)cs  methods   –  Ensemble  Data  Mining/Predic)ve  Analy)cs   §  Can  we  predict  what  )meframe  they  might  churn?   –  Is  this  Big  Data?   •  Most  of  this  processing  is  done  on  a  Laptop/Desktop   Customer  Sen)ment  
  • 24.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com          
  • 25.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com          
  • 26.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Is  Advanced  Analy)cs  for  you?   §  If  you  have  Data  then  YES   §  You  don’t  need  to  have  Big  Data  to  do  Advanced  Analy)cs   §  You  don’t  need  to  hire  PhDs  or  Data  Scien)sts   §  You  can  do  Advanced  Analy)cs  on  the  the  data  you  have.   §  Do  you  have  any  historical  data?     §  Use  what  data  you  have  available   –  As  new  data  becomes  available  you  can  add  these  in  
  • 27.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Everyone  only  talks   up  to  this  point   Nobody  talks   about  Deployment   Or  what  happens   aVer  Deployment  
  • 28.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Create  Valuate  Products   ✓   ✗  
  • 29.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com                                            brendan.)erney@oraly)cs.com                                  @brendan)erney                                www.oraly)cs.com                                  ie.linkedin.com/in/brendan)erney  
  • 30.      www.oraly)cs.com  t  :  @brendan)erney  e  :  brendan.)erney@oraly)cs.com           Word  Cloud  of  the  Oracle  Advanced   Analy)cs  web-­‐pages     hWp://www.oraly)cs.com/2015/01/crea)ng-­‐word-­‐cloud-­‐of-­‐oracle-­‐oaa.html