A talk I recently gave to a group of Earth Observation professionals who were looking for advice on growing their businesses on the back of EO data. If you are interested in collaborating on data opportunities, particularly applications in Energy and Natural Resources, do get in touch via thierry.gregorius (at) getech (dot) com.

1. Turning Data into Revenue
Personal thoughts from a data career in Energy,
Natural Resources, Land & Property
Thierry Gregorius
London 21 March 2019
DISCLAIMER:
Personal opinions only

2. By Kirby Ferguson
“Everything is a Remix”, 2011

3. • Collect and sell your data
• Resell someone else’s data
• Transform data
• Derive data
• Combine/aggregate multiple data sources
• Interpret and analyse data
• Develop (and sell) data algorithms
• Provide a data platform to enable the above

4. Some revenue models…

5. Sponsorship (e.g. R&D grants)
Pays the bills but no more
Pre-Order (e.g. Multi-Client)
Low risk, potential for upside
Reseller / Broker
Needs good terms, high volumes
Marketplace (Platform)
Grow ecosystem, can be win-win
Pay Per Use / As You Go
Needs high volumes or margins
Subscription
Good for repeat revenue
Freemium or Razor Blade Model
Based on loss-leaders
Monopoly / Unique Niche
Congrats, enjoy while it lasts!

6. Active vs Passive Revenue

7. Active revenue: trade time for money
• Consulting
• One-off projects
• Expert reports
• R&D
• Project contributor (e.g.
UN, EU)
• etc.

8. Passive revenue: time-independent
• Database-driven products e.g.
automated reports
• Self-service products e.g.
portals
• Cloud services, Anything as a
Service (XaaS)
• Algorithms e.g. software add-
ons
• etc.

9. Difficult to scale
Time is a fixed resource
Highly scalable
Repeatable,
automated processes
Active
Passive
e.g. Google, Experian, Landmark

10. Move further downstream
Market size
End user data expertise
Demanding customers with
complex needs who
understand your domain
Lay or mainstream
customers/consumers
with simpler needs
Active revenue
Time-consuming
Passive revenue
Automated

11. Keep it simple
Not every service needs a map or image e.g. Y/N questions:
Yes
Yes
Is this place
prone to
flooding?
(Risk reports)
Has this rental car crossed a
country boundary?
(GPS tracker)

12. Don’t sell data – sell answers to real questions

13. Start with the end user’s needs
photo credit: rawpixel #570904 via unsplash

14. Charging for free data?
• The value is in the service
• Data access, hosting, ease of
use
• Data aggregation (one stop
shop, licensing)
• Data analysis
• Data transformation e.g. Y/N
answers
• etc.

15. Specialise and
differentiate

16. • Carve out your niche and unique strengths
• Specialise in vertical markets – serve their specific problems
• Differential pricing based on value to customer
• Even a good product does not usually sell itself – Marketing, Sales

17. And finally…
DON’T UNDERSELL YOURSELF !
It may be simple to you but
not to the customer…
Skills and experience are
expensive to acquire

18. Thank you
@Thierry_G