What  are  we  aiming  for?  

If  the  data  is  99%  accurate:    

•  12  newborns  would  be  given  to  the  wrong  parents  daily,    

•  107  incorrect  medical  procedures  would  be  performed  every  day    

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Agenda  

•  Business  challenge  

•  Methodology  

•  SoluCon  

•  Deliverables  &  Outcomes  

•  Future  roadmap    

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Business  Challenge  

•  Technology  –  how  do  we  make  the  right  thing  easy  and  the  wrong  thing  hard?  

•  Processes  –  embedding  into  Kaizen.  Do  we  have  enough  controls,  checks  and  balances  or  too  many?  

•  People  –  Knowing  vs.  doing  gap.  The  data  as  an  enterprise  asset  fills  the  promises  and  obsession  

•  Data  –  what  is  current  state,  dynamics?  Key  to  get  the  ambulances  vs  fences  balance  right  

3  

Business  case  –  things  to  consider  

•  What  is  the  return/unreachable  rate  of  DM/eDM/calling  campaigns?  

•  What  is  the  cost  of  a  customer  complaint?  

•  How  many  remediaCon  projects  and  at  what  cost  were  run  last  year?  

•  What  is  the  quality  of  meta-­‐data?  How  much  Cme  is  spent  on  your  intranet’s  search  results  page?  

•  How  are  your  risk  models  impacted  by  accuracy  and    completeness  of  data?  

•  Can  CollecCons/Recovery  get  hold  of  everyone  they  need  to?  

•  What  are  the  benefits  lost  through  underuClisaCon  of  new  soZware  caused  by  poor  data  quality  ?  

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Transporta?on    • Un-­‐reconciled  movements  of  data  i.e.  email  Excel  

Inventory    • Redundant  data  • Noise    • MulCple  systems  

Mo?on    • Typing  • Paper  checklists  vs.  validaCon  

Delays/Wai?ng  • NoCficaCons  • WaiCng  for  double  –checks  and  cleansing  

Over  Processing  • Double  -­‐  checking  • FighCng  vs.  working  with  data  

• Siloed  cleansing    

Over  Produc?on  • MulCple  copies  of  the  same  data  

• MulCple  inconsistent  definiCons  

         Defects    • Rework  • Siloed  soluCons  • Failure  demand  

Tool: 7 Wastes

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The  DQ  Journey  

Jul-­‐12  

Legacy  DQ  Rules  

Oct-­‐12   Jan-­‐13   Mar-­‐13   Jan-­‐14  

AML/KYC  Delivery  

Project  Falcon  –  DQ  Repor?ng  

BIDQ  Data  profiling  and  cleansing  

IPS  -­‐  Assessment  

Informa?ca  OOTB  Accelerators  

DeloiSe  DQ  POC  

DQ  Rules  Web  Services  

DQ  Flag  

DQ  Valida?on  Services  

BNZ  Standard  formaXng  rules  

AML/KYC  Requirement  

Roles/PD’s/Governance  

BAG  Established  

Jul-­‐13   Oct-­‐13  

E-­‐Learn  Module  

Mercury  DQ  Hub  

Mercury  DQ  Hub  

Performance  Alignment  Framework  

BNZ  Data  Quality  Framework  

Governance  and  Culture  

Technical  capability  

Projects  delivery  

BIS  Update  interface  

New  BNZ  DQ  Rules  

Third  Party  Cleansing  

Methodology  -­‐  DQ  Framework  

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Step    1  

Define  the  Data  Requirement  

Step    2  

Measure  Quality  of  Data  Output  

Step    3    

Assess  OpportuniCes  to  Improve  Quality  

Step    4    

Improve  Process  

Step    5    

Remediate  Data  

Establish  DQ  Excellence  

Step

   0  

Methodology  –  DQ  Framework  in  real  life  

8  

DQ  Dimensions  Model  

9  

Methodology  –  Capability  build  

10  ?me  

Maturity  

Technology    upliZ  

Assessment  

Alignment  

Cost    saved  

Strategy

Problems

Requirements

Common  defini?ons  to  mul?-­‐layered  ac?on…  

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Repor?ng  

Informa?ca    Data  Director  (self-­‐service)  

“in-­‐?me”    conversa?ons  

CRM  Support  

Entry  point    valida?on  

Data  Cleansing  

One  standard    service  

Online  campaigns  

Ini?a?ve  selec?on  source  and  type  

•   DQ  Rules    

•   Postal  Address  file  •   Company  Register  •   Infologs  

•   Online  •   Call  •   DQ  Flag    

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The  source  of  correct  data    needs  to  be  established  early:  

Depending  on  the  source,    the  method  for  correcCon  is  selected:   Correct

data

Customer interaction

3rd Party

DQ derived

•   Online  •   Call  •   DQ  Flag    

High reputation risk & expensive

Validated correct data

Manual

Customer Interaction Risk of

outdated information

Easy & large volume

Bulk or Hybrid (manual & bulk)

3rd Party

Risk of logic breaking

Logical & cheap

Bulk

DQ Derived

Cons  

Method  

Pros  

DQ  Flag  

13  

DQ  Repor?ng  -­‐  Falcon  

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…all  supported  by  culture  stream  ini?a?ves  

15  

People  Management  Essen/als  

Learning  Campus  Module  

Performance  Alignment  Framework  

Data  Quality  Content  Hub  on  BNZ  Intranet  

Deliverables  and  Outcomes    

Culture  • Visibility    &  Awareness  

• DQ  Community  

Capability  

•   World-­‐class  tooling  &  framework  

• “All  data”  readiness  • Knowledge  bank  

Outcomes  

• ReducCon  in  defects  • Complaints  prevented  

• Improved  capital  allocaCon    

16  

Future  roadmap  •  More  informaCon  domains  

•  InformaCca  Data  Director  Self  Service  

17  

•  Data  Quality  as  a  service  

•  Enterprise  InformaCon  Management  

Data  Quality  at  BNZ  

Drive  con/nuous  improvement  in  quality  of  Data  in  BNZ  via  cohesive  set  of  technology  and  culture  change  ini/a/ves  

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