Open Government, Open Data and Data Management - Coradix

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Slides from Coradix Executive Breakfast on Open Government and Open Data - Ottawa, Aug 16, 2012

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Open Government, Open Data and Data Management - Coradix

  1. 1.  Open  Government  Open  Data  and  Data  Management    
  2. 2. •  We  acknowledge  that  people  all  around  the  world   are  demanding  more  openness  in  government;  •  We  accept  responsibility  for  seizing  this  moment  to   strengthen  our  commitments  to  promote   transparency;  •  We  accept  responsibility  to  harness  the  power  of   new  technologies;  •  We  uphold  the  value  of  openness  in  our  engagement   with  ci9zens  
  3. 3. The  Open  Data  Landscape  DOB: 2009Thank-youObama
  4. 4. Ac9on  Plan  on  Open  Government  Source: Treasury Board of Canada Secretariat
  5. 5. Tony  Clement   “Data is Canada’s new Natural Resource” Winnipeg Free Press, July 12, 2012
  6. 6. Canadians Government Citizen / Industry ParticipationOpportunities / Benefits Timely Access to Elimination of effort and cost Quality Data responding to ad-hoc requests Economic Innovation Royalties from commercial Gov’t Accountability exploitation of liberated data Loss of Revenue from Data Privacy rights compromised Cost/Capacity of Provisioning Data Lack of skills to manipulate /Challenges / Risks understand (ie non-tech savvy) Lack of consistency of standards- architecture, meta-data, delivery Decisions compromised by relying on erroneous data Quality Issues Misinterpretation of Data Unable to explain contextual questions National / Individual Security
  7. 7. NeighborhoodKnowledge LosAngeles (http://nkla.ucla.edu) (NKLA) is awebsite dedicated toproviding public access tovital data and information forneighborhood improvementin Los Angeles.
  8. 8. Digital  Economy  “The  total  size  of  digital  economy  is  es=mated  at  $20.4  trillion,  equivalent  to  roughly  13.8%  of  all  sales  flowing  through  the  world  economy.”  Source:  The  New  Digital  Economy  How  it  will  transform  business,  Oxford  Economics  
  9. 9. Source: McKinsey Global Institute, Big Data Next Frontier for Innovation
  10. 10. Volume  of  Data  
  11. 11. Velocity  of  Data  
  12. 12. “Big  Brother  is  Watching”  
  13. 13. Variety  of  Data  
  14. 14. Data  Analysis  Source: AIIM Industry Watch, Big Data
  15. 15. Content  Management  Source: AIIM Industry Watch, Big Data
  16. 16. Implica9ons  •  Governance  •  Insight/Analy9cs  •  Privacy  •  Discoverability    •  Security  •  IP    
  17. 17. Does  Big  Data  =  More  Technology?  
  18. 18. Big  Data  Challenges   •  Source:  AIIM  Industry  Watch,  Big  Data  Source: AIIM Industry Watch, Big Data
  19. 19. They  set  out  to  buy  this  
  20. 20. And  this  is  what  they  got  
  21. 21. Data  Quality  Problems  are  not   cheap  
  22. 22. The  ERP  Experience  
  23. 23. Costs
  24. 24. Peopleso[  Anyone  ?  
  25. 25. Peopleso[  Anyone  ?  
  26. 26. Tank,  Tanks,  Tankers,  Tanked  
  27. 27. Legal  Challenges  
  28. 28. Result  
  29. 29. This hitsclose tohome
  30. 30. Professional  Data  Management  is  new  
  31. 31. Data  Blueprint  –  Na9onal  Cancer   Ins9tute  Re-­‐architec9ng  Data  
  32. 32. Data  Management  Planning  Online  
  33. 33. State  of  Colorado  •  How  Data  Management  Improved   –  The  EDM  program  helped  facilitate  much  greater  communica=on  between   business  and  IT   –  A  robust  Governance  process  and  commiOee  structure  was  established   –   A  set  of  Data  Principles  were  developed  and  accepted   –  Specific  ini=a=ves  were  undertaken  in  the  areas  of  Master  Data,  Architecture   and  Meta-­‐data  •  How  the  Business  Issue  was  addressed   –  Colorado  Unique  Personal  Iden=fier  (CUPID)  MDM  program  generated   benefits  in  quality,  sharing,  understanding,  security  and  stewardship   –  Educa=on  Longitudinal  Data  System  Architecture  ini=a=ve  reduced  the  gaps  in   school  readiness  and  academic  achievement  between  popula=ons  of  children   –  Improved  client-­‐service  through  access  to  integrated  health  informa=on   –  Improved  policy  making  through  a  more  informed  process  
  34. 34. Recognize  this  ?   Architectural Bubble Chart
  35. 35. Enterprise  Architecture  John Zachman’s Seminalarticle in 1987 launchedEnterprise Architecture
  36. 36. W3C  Linking  Open  Data  
  37. 37. DBpedia   A community-based effort structure Wikipedia Semantic techniques extend this to structured models
  38. 38. For   Against  •  "Data  belong  to  the  human  race”   •  Government  funding  may  not  be  used  to  duplicate  or  •  Public  money  was  used  to  fund  the   challenge  the  ac=vi=es  of  the  private  sector   work  and  so  it  should  be  universally   •  Governments  have  to  be  accountable  for  the  efficient   available.   use  of  taxpayers  money:  If  public  funds  are  used  to  •  It  was  created  by  or  at  a  government   aggregate  the  data  and  if  the  data  will  bring  commercial   ins=tu=on   (private)  benefits  to  only  a  small  number  of  users,  the  •  Facts  cannot  legally  be  copyrighted.   users  should  reimburse  governments  for  the  cost  of  •  Sponsors  of  research  do  not  get  full   providing  the  data.   value  unless  the  resul=ng  data  are   •  The  government  gives  specific  legi=macy  for  certain   freely  available.   organisa=ons  to  recover  costs  (Stats  Canada)  •  Data  are  required  for  the  smooth   •  Privacy  concerns  may  require  that  access  to  data  is   process  of  running  communal   limited  to  specific  users  or  to  sub-­‐sets  of  the  data.   human  ac=vi=es  (map  data,  public   •  Collec=ng,  cleaning,  managing  and  dissemina=ng  data   ins=tu=ons).   are  typically  labour-­‐  and/or  cost-­‐intensive  processes  -­‐  •  In  scien=fic  research,  the  rate  of   whoever  provides  these  services  should  receive  fair   discovery  is  accelerated  by  beOer   remunera=on  for  providing  those  services.   access  to  data.   •  O]en,  targeted  end-­‐users  cannot  use  the  data  without   addi=onal  processing  (analysis,  apps  etc.)  
  39. 39. Canadians Government Citizen / Industry ParticipationOpportunities / Benefits Timely Access to Elimination of effort and cost Quality Data responding to ad-hoc requests Economic Innovation Royalties from commercial Gov’t Accountability exploitation of liberated data Loss of Revenue from Data Privacy rights compromised Cost/Capacity of Provisioning Data Lack of skills to manipulate /Challenges / Risks understand (ie non-tech savvy) Lack of consistency of standards- architecture, meta-data, delivery Decisions compromised by relying on erroneous data Quality Issues Misinterpretation of Data Unable to explain contextual questions National / Individual Security
  40. 40. Addressing  the  Challenges,  Realizing  the  Opportunity   Decisions compromised by Quality Issues relying on erroneous data Data Quality Management EDM   Privacy rights compromised Governance   Enterprise Data Security National / Individual SecurityLack of consistency of standards- Master-Data Management Open Dataarchitecture, meta-data, delivery Delivery Misinterpretation of Data Meta-Data Management Platform Can’t address contextual questions Data Architecture Timely Access to Lack of skills to Quality Data manipulate / understand EDM Competency Center Loss of Revenue Cost/Capacity of Citizen / Industry from Data Provisioning Data Participation Royalties from commercial Elimination of effort and cost Economic Innovation exploitation of liberated data responding to ad-hoc requests

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