Lecture for intro course on doing business with open agro-biodiversity data. Part of Green City Hackathon 2013 activities.

1. Nikos Manouselis
Agro-Know Technologies
nikosm@agroknow.gr
why is an agro-biodiversity data-
powered tech start up going to
profitable?

2. We help organizations and
people to address societal and
environmental challenges
using solutions that are
informed and enhanced by
high-quality data
We develop and put in real
practice end-to-end, modular
solutions that transform data
into meaningful knowledge
and services

3. why agriculture?

4. a huge market, globally
Food & Agricultural commodities production, http://faostat.fao.org

5. some figures
• Food - Gross Production Value globally in
2011: $2,318,966,621 millions
• Agriculture - Gross Production Value globally
in 2011: $2,405,001,443 millions
• Investment in agriculture - Gross Capital
Stock globally: $5,356,830 millions
… they are big

6. examples of EU production in 2010
Source: Eurostat

7. how many businesses?

8. going niche: feta
• (currently, still) protected milk product
• Feta cheese demand: 200,000 tones
– 50% produced in Greece (90% goes to Greek
market)
– a huge unmet demand, a growing potential
• ~300,000 people working in 100,000
production units in Greece only
– ~100,000 people in relevant animal production
– ~50,000 people in relevant agricultural production

9. ~100,000 clients?
~450,000 users?
in one country?

10. why open data?

11. Open Definition
“Open data is data that can be freely used,
reused and redistributed by anyone -
subject only, at most, to the requirement
to attribute and sharealike”

12. why open data?
• Open data, especially open government data,
is a tremendous resource that is as yet largely
untapped
– individuals and organisations collect broad range
of different types of data to perform their tasks
• Government is particularly significant in this
respect
– quantity and centrality of data it collects
– most is public data by law, could be made open
and made available for others to use

13. closed data
• examples
…always bad?

14. open data for businesses
“new businesses and new
business models are beginning
to emerge: Suppliers,
aggregators, developers,
enrichers and enablers”
“key link in the value chain for
open data is the consumer…
direct relevance to the choices
individuals make as part of
their day-to-day lives”

15. in agriculture: a political priority
“How Open Data can be
harnessed to help meet
the challenge of
sustainably feeding nine
billion people by 2050”

16. 16
Key facts about agricultural trends
Agriculture is about to experience a “growth shock” in order to cover
the exponentially increasing food needs of the global population
• All demographic and food demand projections suggest
that, by 2050, the planet will face severe food crises due to
our inability to meet agricultural demand – by 2050:
• 9.3 billion global population, 34% higher than
today
• 70% of the world’s population will be urban,
compared to 49% today
• food production (net of food used for biofuels)
must increase by 70%
• According to these projections, and in order to achieve the
forecasted food levels by 2050, a total investment of USD
83 billion per annum will be required

17. 17
Open Data in Agriculture
One of the most promising routes to agriculture modernisation
is the provision of Open Data to all interested parties
• In an era of Big Data, one of the most promising routes to
bootstrap innivation in agriculture is by the use of Open
Data:
– e.g. provisioning, maintaining, enriching with relevant
metadata, making openly available a vast amount of
• The use and wide dissemination of these data sets is
strongly advocated by a number of global and national
policy makers such as:
– The New Alliance for Food Security and Nutrition G-8
initiative
– Food & Agriculture Organization of the UN
– DEFRA & DFID in UK
– USDA & USAID in the US

18. what is this data about?

19. agricultural science

20. biodiversity

21. examples of variety & diversity

22. data sets

23. maps

24. photos

25. databases

26. • publications, theses, reports, other grey literature
• educational material and content, courseware
• primary data, such as measurements & observations
– structured, e.g. datasets as tables
– digitized, e.g. images, videos
• secondary data, such as processed elaborations
– e.g. dendrograms, pie charts, models
• provenance information, incl. authors, their
organizations and projects
• experimental protocols & methods
• social data, tags, ratings, etc.
• …
research(+) content

27. where can I find this data?

35. plug and play?
• No!
– requires a deep understanding of the data
– requires excellent data processing & analysis skills
– requires very good technical skills
• will evolve into a data-powered value chain
– the companies that develop innovative agro/ food
products (agro apps consumers) need…
– …companies that build apps on agro data (agro data
consumers, agro apps producers) who need…
– companies that process agro data (data science
powered)

36. where to use this data?

41. there is more…

42. …and more…

43. …and much much more

45. why do we (Agro-Know) care?

46. Our vision
To add value to the rich
information available in the
wide spectrum of agricultural
and biodiversity sciences
To make it universally
accessible, useful and
meaningful, through
innovative tools, services and
applications

47. Our values
use open data to solve
meaningful societal challenges
create a data-powered
ecosystem that may bootstrap
agricultural & food innovation
embrace all data sources,
formats & types relevant to
agricultural research &
innovation
promote open source and
open data

48. Unorganized Content in
local and remote sites
Widgets
Authoring services
Data Discovery Services
Analytics services
Agro-Know Data Platform
Ingestion Translation Publication
Harvesting BlossomCultivation
Organized and structured
Content in local and remote
DBs
Educational
Bibliographic
Other
Enrichment
Aggregate
data from
diverse
sources
Works with
different type
of data
Prepare data
for
meaningful
services
Educational
Bibliographic
data aggregation & sharing hub

49. in a data-powered value chain...
Open data providers
(research institutions,
public sector etc)
Open data providers
(research institutions,
public sector etc)
Agriculture & food
start ups & industry
Agriculture & food
start ups & industry
Innovative data-
powered start ups
Innovative data-
powered start ups
VCs / angel investors
Incubators
VCs / angel investors
Incubators
Data
aggregators
Data
aggregators
Techies &
friends
Techies &
friends
49
…we are here
… we have a gap here
Data scientistsData scientists

51. not only
hackers

52. but convincing business cases

53. its a long way to go
Creative
Hackathon
Creative
Hackathon
Creative
Boot camp
Creative
Boot camp
Ag & Food
Business
Meet Ups
Demo &
Investor Days
Demo &
Investor Days
Introductory
Course
Introductory
Course
53

54. take away point

55. “The future belongs
to the companies
that turn data
into products”

56. thank you!
nikosm@agroknow.gr
http://www.agroknow.gr