Slides presented at Open Data Exchange 2013, April 6 2013, Montreal, Canada. ODX13.com. Sponsored by Trudat.co

1. Open data startups
@heri

2. What’s a startup?
• For-profit
• Technology based
• With a unique unfair advantage
• Not limited to geographical boundaries
• Scalable, hockey stick curve growth when
product-market fit
• e.g. : airbnb, kickstarter, square, songza

3. Challenges
• Raw data, sometimes not in a machine-
readible format
• Open data often related to a country.
Hardly scalable
• Open data available to all, low barriers to
entry
• Often related to government services

4. Elements of an open
data startup

5. Clarity of purpose
• Lots of noise in open data. Large diversity
of apps
• Be able to summarize what you in the back
of a business card... WITH LARGE
LETTERS

6. Painkiller
• Think about delivering something amazing:
pick one thing that is a burning importance
to a customer (painkiller) then deliver a
compelling solution
• Does any published data solve an existing
problem?

7. Large markets
• Address big existing markets ready for
rapid change. A market with a $1 B
potential allows for error.

8. Customers
• Is there anyone willing to pay?
• Listing a few people who pay a premium for
your unique offering is a good first sign.
• Often, open data apps can’t identify
customers

9. Think differently
• Challenge what’s existing and take the
contrarian route.
• Idea: offer for free what’s offer by big
companies for a high cost

10. Technology is key
• Spend only on great engineering. Be very
frugal on everything else
• Focus on stealth and speed of iteration
• Open data ideas: build superior intelligence
and integration with other services

11. Business models

12. Open Source parallel
• Open Source: first a movement for
programmers, then an asset for startups
• Deliver open data, charge monthly support
(saas)
• Sell access to a superior version, designed
for professional environments (freemium)

13. Other business models
• Advertising or donation. Necessity to build
enough traffic
• Affiliate. e.g real estate data <=> real estate
companies
• Combination?

14. Outlook
• very few startups -- land grab!
• lots of interest and “sympathy capital”
• Wealth of data previously unavailable
• Being able to improve society

15. Questions?
•@heri
• heri@madmedia.ca