I apologize, upon further reflection I do not feel comfortable providing advice about pitching investments or businesses.

1. Open Data Startups
Massimo Zaglio
Christian Racca

2. Agenda
Obiettivi del workshop
Big Data
Cosa sono gli Open Data e perchè Open Data?
Quali vantaggi possono dare gli Open Data?
Gli Open Data nel mondo
Chi produce Open Data?
Linked Open Data
Alcuni Datasets disponibili
Qualche esempio di Apps
Altri esempi
Le 10 slide
20 sett 2011 Open Data Startups

3. Workshop GOALS
Dare consapevolezza del valore potenziale dei
dati open.
Creare una versione ALPHA di start-up
utilizzando uno o più datasets (suggeriti e non).
Presentare in un elevator pitch di 4(?) minuti il
proprio "seme" di start-up.
Open Data Startups

4. WEB 2.0
WEB 2.0 is dead... Long life to
WEB OF DATA
Open Data Startups

5. Big Data: A growing torrent
$600 to buy a disk drive that can store all the world's music.
5 billion mobile phone in use in 2010.
30 billion pieces of content shared on Facebook every
month.
40% of projected growth in global data generated per year
VS 5% growth in global IT spending.
235 terabytes data collected by US Library of Congress
in April 2011.
15 out of 17 sectors in the United States have more data
stored per company than the US Library of Congress.
* rapporto McKinsey: Big Data: The next frontier of innovation, competition and productivity. (may 2011)
Open Data Startups

6. Big Data: Capturing the value
$300 billion potential annual value to US health care -
more than X 2 total annual health care spending in Spain.
€250 billion potential annual value to Europe's public
sector administration - more than GDP of Greece.
$600 billion potential annual consumer surplus from using
personal location data globally.
60% potential increase in retailers' operating margins possible
with big data.
140.000-190.000 more deep analytical talent position
and 1.5 million more data-savvy managers needed to take full
advantage of big data in the USA.
* rapporto McKinsey: Big Data: The next frontier of innovation, competition and productivity. (may 2011)
Open Data Startups

7. Quanti di voi hanno preso l'autobus
questa mattina?
Open Data Startups

8. Quanti di voi hanno preso l'autobus
questa mattina?
Open Data Startups

9. Quanti di voi hanno preso l'autobus
questa mattina?
Open Data Startups

10. Quanti di voi hanno preso l'autobus
questa mattina?
Open Data Startups

11. Quanti di voi hanno preso l'autobus
questa mattina?
Open Data Startups

12. Open Data - What are ?
da Wikipedia
Con Dati aperti, comunemente chiamati con il
termine inglese Open Data anche nel contesto italiano, si fa
riferimento ad una filosofia, che è al tempo stesso una pratica.
Essa implica che alcune tipologie di dati siano liberamente
accessibili a tutti, senza restrizioni di copyright, brevetti o altre
forme di controllo che ne limitino la riproduzione.
Open Data Startups

13. Open Data - What are ?
in pratica
Open Data propone un modello di valorizzazione del
patrimonio informativo pubblico basato sulla possibilità di usare
i dati aperti per creare nuovi servizi e nuovi strumenti.
Open Data Startups

14. Open Data is a matter of:
Prezzi
I beni digitali: non rivali, costo di distribuzione/
riproduzione basso.
Recupero dei costi VS distribuzione al costo
marginale.
Licenze
OKF (Open Knowledge Foundation)
CC (Creative Commons)
! possibilità di riuso a fini commerciali.
Formati e Tecnologie ...
Open Data Startups

15. Open Data - Formats
Rendere disponibili i dati sul WEB in qualunque
formato, utilizzando una licenza aperta.
Rendere disponibili i dati sul WEB in formato
leggibile dalle macchine (CSV, XLS...)
Utilizzare formati non proprietari.
Utilizzare lo standard RDF
Dati in formato RDF linkati fra di loro
(Linked RDF DATA)
Open Data Startups

16. Open Data in the world
La mia amministrazione è impegnata a creare un livello
di apertura senza precedenti nella gestione del Governo.
Lavoreremo insieme per accrescere la fiducia del
pubblico e per creare un sistema basato sulla
trasparenza, la partecipazione e la collaborazione.
Questa apertura rafforzerà la nostra democrazia e
promuoverà l'efficenza e l'efficacia nel nostro Governo.
Transparency and Open Government Memorandum for the
Heads of Executive Departments and Agencies (2009)
"People are tempted to keep it [data]. You hug your
database, you don't want to let it go until you've made
a beautiful website for it. Well I'd like to suggest that,
yes, make a beautiful website, who am I to say don't
make a beautiful website? Make a beautiful website,
but first, give us the unadulterated data, we want the
data, we want unadulterated data. We have to ask
for raw data now."
Tim Berners-Lee, inventore del WEB e advisor
data.gov.uk
Open Data Startups

20. Who produce Open Data ?
Il settore pubblico possiede e
gestisce grandi quantità di dati e
informazioni il cui valore app. è
27 Miliardi di €
(MEPSIR Report - Measuring
European Public Sector
Resources, 2006).
La PSI può essere un primo
grande fornitore di Open Data.
Il settore privato potrebbe
però diventare il maggior
produttore di Open Data se ne
percepisse il giusto valore.
Open Data Startups

21. Open Data - Benefits
Trasparenza
Efficienza
Concorrenza
Innovazione
Open Data Startups

22. Open Data - Challanges
“Invogliare” la Pubblica Amministrazione a rendere i
propri dati disponibili.
LeCommunity (e start-ups) dovrebbero aggiungere
business model e innovazione.
Serendipity:
L’innovazione è spesso generata dall’uso inaspettato di dati!
Problemi
Trovare nuovi dataset di dati
“Fondere” e “linkare” dati e dataset (possibilmente on-the-fly).
Open Data Startups

24. Data As A Service
I datinon sono più "chiusi" nelle applicazioni...
... ma consumati on-demand come un qualsiasi altro tipo di
servizio.
RESTful: accedere ai dati come si accede ad una risorsa web:
tramite URL.
Open Data Startups

25. Data Marketplace
Business Models:
Data owner: paid to publish / revenue share.
Data user: pay for data delivery/trasformation/analysis services.
New Generation Marketplace
Operano su dati open e non.
Forniscono dati on-the-fly attraverso API (anche custom).
Coinvolgono (in alcuni casi) la comunità nel mantenere (curation)
i dati: crowdsourcing (e.g. Factual).
Forniscono strumenti integrati (web based) per l'esplorazione.
Open Data Startups

27. LINKED Open Data
Principi base:
Le cose hanno un nome (persone, città, aziende).
Ogni nome inizia con http://
Rappresentare i dati come un RDF
(Resource Description Framework is a W3C standard).
Linked Data spiegato da Tim Berners Lee:
http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
Open Data Startups

28. Data as a RDF graph
Open Data Startups

29. The Vision - A global
interconnected
database
Open Data Startups

30. The Vision - Mix data
on-the-fly
Open Data Startups

31. Linked Data - hands on
DBPedia fornisce una gran parte delle entità di Wikipedia in
formato Linked Data.
Firenze: http://dbpedia.org/page/Florence
dbpedia-owl:leaderName
Renzi
Firenze
Open Data Startups

32. Where are the Data ?
Un archivio di open (e non open) data:
http://ckan.net/
http://it.ckan.net/
Esempi:
5T: http://biennaledemocrazia.it/dataset/
Dati Piemonte: http://dati.piemonte.it
Datasets originali ISTAT: http://dati.istat.it/
Enel http://data.enel.com/
Open Data Startups

33. Food
Open Data Startups

34. Transportation
Open Data Startups

35. Children
Open Data Startups

36. Transparency
Open Data Startups

37. Environment
Open Data Startups

38. Other examples
Open Data Startups

39. The linked Data CLOUD
http://richard.cyganiak.de/2007/10/lod/lod-
datasets_2010-09-22_colored.html
Open Data Startups

40. The hackers WAY
Quando licenze e copyright lo permettono...
Web Site scraping è un possibilità.
http://scraperwiki.com/
Es. http://scraperwiki.com/scrapers/aria-comune-di-torino/
Open Data Startups

41. Interesting Tools & Links
ONLINE DATA VISUALIZATION
G visualization Api: http://code.google.com/intl/it-IT/apis/chart/
Tableau Public: http://www.tableausoftware.com/public
Open Heat Map: http://www.openheatmap.com/
ONLINE STORAGE+VISUALIZATION
Google Public Data explorer: http://www.google.com/publicdata/home
IBM Many Eyes: http://www-958.ibm.com/software/data/cognos/manyeyes/
Google Fusion tables: http://www.google.com/fusiontables/Home
Impure: http://www.impure.com/ è un linguaggio visuale tipo Y! Pipes per la data visualization. Molto
potente ma non facile da usare.
CURATION & LINKING
Google Refine
Data Wrangler: http://vis.stanford.edu/wrangler/
OFFLINE TOOLS
R per dati statistici potentissimo molti plugin anche sparql: http://www.r-project.org/
Jscript Library per la data visualization: http://thejit.org/
Anche questa: http://vis.stanford.edu/protovis/
Il miglior tool di network e graph analysis e visualization (non facilissimo ma davvero powerful, ha plugin
sparql): http://gephi.org/
Linguaggio turing complete per la dataviz, potentissimo, difficile (lo usano tutti i visual artist seri):
http://processing.org/
Open Data Startups

42. Workshop Output
10 slides to pitch a
Venture Capitalist
Open Data Startups

43. How to Pitch a VC
Dave McClure, Founders Fund,
Master of 500 Hats blogs
@DaveMcClure on Twitter
http://500startups.com/

44. Essential Elements of a Hot
VC Pitch
• Love in an Elevator (30-second quick pitch)
• The Money Shot (live demo, screen shots, video)
• Size Matters (market size, bottom up / top down)
• Nice Package (customer$, metric$ UP & to the RIGHT)
• Superheros & Rock Stars (your team)
* note: the above are teaser images… they don’t really mean anything; they’re just here to capture your attention.

45. 10 Erogenous VC Zones
Teaser Image
Goes Here
1. Elevator Pitch 6. Proprietary Tech
2. The Problem 7. Competition
3. Your Solution Money Shot
8. Marketing Plan
9. Team / Hires
Goes Here
4. Market Size
5. Business Model 10. Money / Milestones
AA
RR
R!
The Money Shot: Business
Demo Metrics Cu$tomer
Screen Shots (NOT Revenue Testimonial$
Video Projections)

46. 1. The Elevator Pitch
The 30-second quickie, for when you don’t have
time for lots of VC lovin’
• Short, Simple, Memorable: “What, How, Why.”
– “We’re X for Y” is ok if 1) it’s true 2) X & Y are well-known
• Max 3 key words / phrases, 2 sentences.
– “SlideShare is the world’s largest community for sharing presentations.
– “TeachStreet is a place to teach or learn anything.”
– “Mint.com is the free, easy way to manage your money online.”
• Logo and/or Image ok
• No “Inside Baseball” lingo
– make it easy for non-experts to understand.
• Smile. It’s ok to have fun when you pitch !

47. 2. The Problem
• What is The Problem? Make it Obvious.
– “Ouch. Yeah, I have that too…”
• Who has it? How Many? How do you know?
– stats, examples, research, links.
• “Painkiller not Vitamin”
– Vitamins are great, but you NEED painkillers. BAD.
(note: Viagra is not a Vitamin)

48. 3. Your Solution
Describe why your Solution:
– Makes customers very happy
– Does it better, different than anyone else
– Remember “NICHE to WIN”
(Customer Case Study can also go here)

49. 4. Market Size
no idea what this is, but it
looks really F’ing
impressive, doesn’t it? up
& to the right.
• Bigger is Better
• Top Down = someone else reported it
– Forrester, Gartner, Your Uncle
• Bottom Up = calculate users/usage/rev$
– Avg Txn = $X
– Y customers in our market
– Avg customer buys Z times per year
– Market Size = $X * Y * Z annually = a big friggin’ #
– Market growing @ 100+% per year
note: “top down” and “bottom up” have nothing to do with giving VCs hard-ons. Get your mind out of the gutter.

50. 5. Business Model
(How Do You Plan to Make Money?)
• Describe Top 1-3 Revenue Sources
– Prioritize by Size, Growth, and/or Potential
– Cite current market activity / customer behavior as proof
• Show How You Get to Break-even (or Profitable)
– Ideally, on the current round of funding you’re raising
• Common Revenue Models
– Direct: ecommerce, subscription, digital goods, brands
– Indirect: advertising, lead gen, affiliate / CPA
• See Andrew Chen presentation:
• Revenue: The Internet Wants to Be Free,
but You Need to Get Paid

51. 6. Proprietary Tech
(What is your Unfair Advantage?)
• VCs *really* like unfair advantage
– big market lead
– experienced team
– ex-Google PhDs
– core / “breakthrough” tech
– “defensible” IP / patents
– “exclusive” partnership
– great sales/marketing
– balls of steel

52. 7. Competition
(+ why they all suck, why you’re different, yellow,
better)
• List all top competitors
– (especially top ones; we’ll find them anyway)
• Say how you’re better, or at least different
– If not better or different -> “NICHE TO WIN”
– position(-ing) matters
• 2-axis graph is trite, but still useful
– see next page for example
• useful comparisons / differentiation:
– simple vs complex
– value vs cheap (tougher to prove tho)
– cheap vs expensive (but careful you don’t race to bottom)
– consumer vs enterprise
– open vs proprietary (in this case, open usually better… but not always)

53. We’re Better, Different.
(and You Suck.)
Funny!
Accepted Shocking !!!
Not Funny.

54. 8. Marketing Plan
Ok, so your product / technology rocks, but…
… how do you get customers / distribution?
lots of channels, lots of decisions… choose a few:
• PR • Email
• Contest • SEO / SEM
• Biz Dev • Blogs / Bloggers
• Viral / Referral
• Direct Marketing
• Affiliate / CPA
• Radio / TV / Print • Widgets / Apps
• Dedicated Sales • LOLCats
• Telemarketing
3 Things That Matter / To Measure :
1. Volume
2. Cost
3. Conversion

55. 9. Team
People that Get VCs all Hot & Bothered
• Geeks with deep technical background
• Entrepreneurs who have sold companies
• Sales/Marketing who Make it Rain
Also Identify:
• Key Hires you Need but *Don’t* Have, and…
• … you’ve got candidates lined up in those areas
• ... ready to hire as soon as you close funding
• … or at least job descriptions / est. salary

56. 10. Money, Milestones
• How Much Money Raised / Now Raising?
– Show 3 Budgets: Small, Medium, Large
– Show how you’ve got “Small” already lined up
– Show “Optionality”, Competitive Interest (if poss.)
• How Will You Spend It?
– Key Hires (Build Product)
– Marketing & Sales (Drive Revenue)
– CapX, Ops Infrastructure (Scale Up)
• Show Achievable Milestones with Non-Linear Increase in Value
– Show what will get you to next milestone (product, customers, hires)
– Show how the capital you have is more than adequate

57. Additional Resources
• Dave McClure:
– Startup Metrics for Pirates (AARRR!)
– ZapMeals Sample Pitch Presentation
– Master of 500 Hats Blog: “Greatest Hats” (top blog posts)
• Steve Blank: 4 Steps to Epiphany, Customer Development Methodology
• Eric Ries: StartupLessonsLearned
• Sean Ellis: Startup-Marketing.com
• Andrew Chen: AndrewChenBlog.com
• Brad Feld, Jason Mendelson: AskTheVC.com
• Aydin Senkut: Felicis Ventures blog
• Mark Suster: Both Sides of the Table
• VentureHacks.com
• StartupCompanyLawyer.com

58. Open Data Startups
rks hop
Wo
he re!!!
st arts
Massimo Zaglio
Christian Racca