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Steve Peters: Monsters and Transformers

Steve Peters: Monsters and Transformers

This deck was used to support Steve's talk at http://power-of-data-2017.swirrl.com/ about how quality data analysis can make for more efficient and effective policy and service delivery. From data monsters, who breathe the fire of expensive licences and hide data in caves, to data engineers who are integral to good data infrastructure, he provides real-world examples of how data analysis has led to a better understanding and measuring the impact of complex real-world problems across authorities.

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Swirrl

June 15, 2017
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Transcript

  1. Is  it  a  monster,  or  a  transformer? What’s that coming

    over the hill? Steve  Peters @Open_Data 15th June  2017 Presentation  to  Swirrl’s “Unlocking  the  power  of  government  data”   conference
  2. None
  3. Fixing  our  broken  housing  market Data is key to understanding

    DCLG’s policy choices and outcomes Supporting  more  effective  &  efficient  local  services For  example….
  4. Finding our way through the data swamp

  5. Get  the  right  data,  more   easily Use  Data Publish

     Data Automated  for  easy  use  and   integration  into   development  and  evaluation   of  policy Accessible,  open  and   collaborative Tools  and   Technology People  and   Skills DCLG Data Programme Acquire  Data Governance
  6. Local  Brownfield  Land  Registers We are making progress… http://www.legislation.gov.uk/uksi/2017/403/schedule/2/made https://mappinggm.org.uk/gmodin/

    Acquire  Data
  7. Interactive  performance  dashboards,  to  automatically  display  key  metrics   We

    are making progress… Use  Data
  8. Publish  Data We are making progress…

  9. We are making progress… Skills  and  Capability target  building buildings

     within  100m touching  buildings  with  a   height  difference  >  3  metres touching   buildings We  are  beginning  to  apply  new  Data  Science  techniques  to  enrich  our  data  sources,  analysis  and  insights:  for   example…. Scope  for  Building  upwards… Locations  of  Mosques
  10. We are making progress… Explore,  visualise  and   extract  our

     data  in-­‐situ…. Connect  directly  to  our  data  (via  our  APIs)  and   re-­‐use  it  in  your  website   Tools  and  infrastructure Building  a  trusted  and   maintained  source  of  DCLG   data  in  fully  open,  re-­‐usable   (linked  data)  formats
  11. Where next? But… How  best  to  clarify   where  we

     want  to   be? And  how  we’ll  get   there  within  reality   of  significant   resource   constraints.
  12. needs YOU Our data Where next? Mario   says