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Not all open data is born equal
Some context• Canadian nonprofit that builds websites and tools to help  governments and citizens engage with each other• ...
Ongoing projects• Citizen Budget: a online consultative budget simulator for  municipalities and civil society organizatio...
Data = Natural Resources?         Source: USGS   Source: James St-Jones (cc-by)    Value!                Meh?             ...
Value extraction    Diamond         Aluminum     Extract       Discover it’s valuable                                    ...
Traffic and Transit data • Sort of case study    – Region of San Francisco: 2 leader organizations        • Bay Area Rapid...
1. Standardization• Transit data   – GTFS & SIRI: open data-oriented standards   – Used by 250 transit/transportation agen...
2. Self sufficient• Transit data   – Data can be interpreted on its own. No need for external     data• Traffic   – Severa...
3. Complexity• Transit   – (Quite) simple: some schedules, some fares, some spatial     data• Traffic   – Complex: network...
4. Reliability• Transit   – Usually buses and trains follow their schedule   – Adding a GPS on each single bus is simple a...
Techno-utopian dream                       Your iphone 8S Dear smartphone,  I need to pickthe kids at schoolas fast as po...
A wealth of data Road events         Gaz price      Road data      Parking data       Crowdsourced data Realtime traffic s...
Multiplicative effect• “Diamond” data self-sufficient: a strength for adoption• For all data: real value is in cross-use w...
Not only gov data• Usually open data = open government data• But open data can be much more     Road events               ...
Some innovation theoryGartner’s hype cycle of innovation (but it is not only about hype)                                  ...
Conclusion• Assess your datasets: diamond vs bauxite analogy or any  other analysis framework• All datasets are not born e...
Stéphane Guidoin                       @hoedicTwitter: @opennorth Facebook: OpenNorth.NordOuvert            Blog: www.open...
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Not all data is born equal - B.C Open Data Summit 2013

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Several criteria are used to evaluate the value of a dataset. This presentation exemplify how some data is easy to value, while some are much more complex. For the later (which might be a large part of open data), the value discovery process might be long but worthy.

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Not all data is born equal - B.C Open Data Summit 2013

  1. 1. Not all open data is born equal
  2. 2. Some context• Canadian nonprofit that builds websites and tools to help governments and citizens engage with each other• Follows two main strategies: Improve access to government information via open data Make participation easy and meaningful
  3. 3. Ongoing projects• Citizen Budget: a online consultative budget simulator for municipalities and civil society organizations• Represent: the largest database and open API of elected Canadian officials with two drupal modules for easy website integration• MaMairie/MyCityHall: an online portal for tracking and interacting with your city hall• Open511: an open data standard for traffic data and basic related tools
  4. 4. Data = Natural Resources? Source: USGS Source: James St-Jones (cc-by) Value! Meh? Hint: this is bauxite
  5. 5. Value extraction Diamond Aluminum Extract Discover it’s valuable   Elaborate process Cut  Industrialize process   Tada! …  Cans, Car parts, etc.
  6. 6. Traffic and Transit data • Sort of case study – Region of San Francisco: 2 leader organizations • Bay Area Rapid Transit (BART): 80+ apps • Metropolitan Transportation Commission (MTC): handful of apps – Same (full of geeks and startups) region – Same “type” of data (transportation) – Both organizations are innovative Let’s look at “intrinsic” data value
  7. 7. 1. Standardization• Transit data – GTFS & SIRI: open data-oriented standards – Used by 250 transit/transportation agencies• Traffic – Several standards (TMDD, TPEG, etc.), but difficult to use in an open data context⇒ Standard = low barrier to entry,⇒ Tools/apps built for these standards can reach lots of customers
  8. 8. 2. Self sufficient• Transit data – Data can be interpreted on its own. No need for external data• Traffic – Several subsets of related data (accident, constructions, road data, etc.) – Data managed by several jurisdictions (local, regional, provincial, federal)⇒ Managing several sources and several datasets is always… complex
  9. 9. 3. Complexity• Transit – (Quite) simple: some schedules, some fares, some spatial data• Traffic – Complex: networks are wide, intertwined, with lots of rules, lots of “free” actors⇒ Modeling complex data is… complex and more prone to discrepancy
  10. 10. 4. Reliability• Transit – Usually buses and trains follow their schedule – Adding a GPS on each single bus is simple and give almost 100% reliability of the data• Traffic – Impossible to monitor every single road segment⇒ Lack of reliability has a strong, negative impact on data value
  11. 11. Techno-utopian dream Your iphone 8S Dear smartphone, I need to pickthe kids at schoolas fast as possible, what’s the best choice?
  12. 12. A wealth of data Road events Gaz price Road data Parking data Crowdsourced data Realtime traffic sensors (gov) Planned trip Car efficiency Realtime traffic (business) Personal data: car, location, habits
  13. 13. Multiplicative effect• “Diamond” data self-sufficient: a strength for adoption• For all data: real value is in cross-use with other datasets• Some datasets will find their value because of the existence of other datasets• Adding new datasets has a multiplier effects on existing related datasets
  14. 14. Not only gov data• Usually open data = open government data• But open data can be much more Road events Car, transit pass, bike share Road data Open Open Transportation habits Traffic data Gov personal Planned trip Parking data Crowdsourced data Data data Open (?) Bike share Gaz price data from Traffic data companies Parking data Vehicle efficiency
  15. 15. Some innovation theoryGartner’s hype cycle of innovation (but it is not only about hype) Stairway to heaven (internet-style) You might …or here be here… Peak of Plateau of inflated expectations productivity Slope of Trough of enlightment disillusionment Innovation trigger Abyssal crash
  16. 16. Conclusion• Assess your datasets: diamond vs bauxite analogy or any other analysis framework• All datasets are not born equal, some might take more time to show their value• Help discovery and value extraction process• Follow “open” standards when they exist or participate to their elaboration• Improve reliability of data where possible• Be patient… but active!
  17. 17. Stéphane Guidoin @hoedicTwitter: @opennorth Facebook: OpenNorth.NordOuvert Blog: www.opennorth.ca/blog

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