Open Data Startups. Heri Rakotomalala, @heri

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

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Open data startups

@heri

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

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

Elements of an open data startup

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

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?

Large markets

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

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

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

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

Business models

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)

Other business models

•Advertising or donation. Necessity to build enough traffic

•Affiliate. e.g real estate data <=> real estate companies

•Combination?

Outlook

•very few startups -- land grab!

•lots of interest and “sympathy capital”

•Wealth of data previously unavailable

•Being able to improve society

Questions?

•@heri

•heri@madmedia.ca

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