L’enfasi posta di recente sul tema dei big data nasconde una serie di domande - per il momento ancora prive di una risposta esaustiva - sulle effettive trasformazioni che essi potranno generare in imprese e filiere. Nello specifico, quale possa essere la capacità delle imprese italiane di sfruttare le potenzialità del cambiamento tecnologico legato ai big data dipende in parte dalle risposte a queste domande di fondo. Come si stanno formando nuove filiere nei settori dei servizi basati sulla capacità di elaborare big data? In tali filiere quale specializzazione si stanno ricavando incumbents e nuove imprese? Quali sono i modelli di business che finora hanno dimostrato una buona sostenibilità economica? Quali servizi nella filiera dei big data sono invece ancora alla ricerca di un modello di business economicamente sostenibile? Quali sono le variabili di competizione nei servizi che si fondano sulla capacità di elaborare big data? L’intervento intende trattare tali questioni prendendo a riferimento il settore dell’infomobilità nelle smart cities.

1. Paolo Neirotti, Politecnico di Torino
Business models and new value chains
for big data

2. Big data as a source of GDP and wealth!
But for which nations?

3. Big data and business models: crucial
issues
● Go beyond hypes and fads
● Which opportunities for SMEs and large enterprises?
● Are open data economically exploitable?
● In many industries, business models around big data are
not clear yet.
– What services and what compelling value proposition?
– For what market segments?
 A FOCUS ON THE INFOMOBILITY SERVICE
INDUSTRY

4. Four reasons for a focus on the
Infomobility industry
1. Mature industry (e.g. maps) before the discontinuity produced by
Internet and mobile business
– New entrants (e.g. Google, Nokia, and many startups)
– Mergers and acquisitions (e.g. Nokia and Navtech, TomTom and
Atlas,
– Convergence with other industries
● directories/advertising (Google and the attempt to acquire Groupon)
● Car manufacturing (e.g. GM and OnStar)
2. In location-based services big data, customer co-creation, and
crowdsourcing are a reality more than in any other industry.
3. Cities are a generator of big data. Can they become “data
retailers” or wholesalers?

5. Four reasons for a focus on the
Infomobility market
● 4. the number of broadband mobile
devices is increasing  Just at the
beginning of the S-curve

6. Discontinuities in the infomobility sector [1]
● Factory-installed GPS equipment: around 1,500 €
● After-market GPS navigation into the car dashboard: 1,000 €
● Portable GPS device (Garmin, Magellan, TomTom): from 35 €
(“plain vanilla” service) to 200 € (live traffic data)
● Google Maps Navigation: FREE
● Many other “low-cost” apps for mobile linking information about
point of interests, alerts for speed-limits and camera.  business
for start-ups (e.g. Waze)

7. Discontinuities in the infomobility sector [2]
TOMTOM
Garmin
2010
Share Price
Repositioning towards B2B: licensing maps and live traffic data
delivery to car makers (e.g. PSA, Renault)
Not producing the HW anymore!
In search for new markets in the B2C: e.g. fitness smart watch

8. Digital maps: not a business for many
● Time compression economies exist!
Big Barriers to entry exist!
(time is needed to
accumulate data)…

9. Value chain in big data for infomobility
Data
generation
Data
aggregation
Data
elaboration
Data visualization
Data Retail
Crowdsourcing (e.g. Waze, Open
Street Map), open governmental
data, telcos, municipalities,
insurance firms, navigation
companies , Google.
Inrix, BusChecker
Mapbox
Google, Web portals, Radio and
TV networks, navigation firms
providing real‐time traffic
information
Data transportation
Microsoft MapPoint, Quantum GIS,
Wikitude (AR)
Infrastructure for data elaboration
(e.g. data storage)

10. High value added activities in the
infomobility service industry
● Data generation for real-time traffic (e.g. Google
through the service “My location” on mobile phones
with Android)
● Data elaboration and cleaning.
● Data distribution, e.g. getting data from local
transportation municipalities (Google Live Transit)
● The importance of data standards for an efficient value
chain (e.g. Google towards local transit authorities)
● Standards for overcoming local information silos and for
fast replication of initiatives across cities.

11. Where is the market for infomobility
services.
● Multi-sided market. Sides
are:
– Motorists/commuters
– Merchants (location-based
advertising)
– Local transit companies
● Companies (e.g.
Foursquare) needing to
“repackage” digital maps
contents.
● Completely free for
consumers
● “Freemium”
– Trade-off among the sides
 high prices reduce the
customer base for
advertisers
● Pay for the app (TomTom,
BusChecker, etc. )
Revenue modelsCustomer segments

12. Infomobility services. Segmenting the
market
Compelling value proposition? For Whom?
● Motorists? …Are they really interested when they do every
day the same route to go to work (and they barely have
two alternative ways)?
● Commuters? …Are they really interested in a coupon
when they have to rush to work or to catch a train?
● A market segment: tourists/travellers, in big cities
especially  large cities are “always on the move” and
they offer more scalable markets
●  NOT A MASS MARKET. BUT ONLY CERTAIN
MARKET SEGMENTS

13. Location-based Services
complementary to infomobility
● Collaborative consumption (e.g. ride sharing services
such as Zimride, Blablacar, Carpooling.com)
● Location-based social networks (e.g. Foursquare,
Nextdoor to strenght local physical communities)
● “Quantified-self” tracking services for sports (e.g.
Runtastic), places you have been, etc.
● …
● Still Opportunities for small businesses? Probably,
yes. But competition is time-based and a large scale of
investments is needed…

14. Big data markets – Some predictions
● Source of differentiation lies in customer interface and data
– Source of differentiation will eventually shift from the algorithms
for data fusion and cleaning to the data themselves
– Economic value will lie in the complementarities of data from
different sources
● Winner-takes-it all markets  Only few players will remain!
● Re-intermediation of merchants and many other local
businesses (e.g. travel agencies are slowly disappearing, the
yellow page industry is reconfiguring)  Big Infomediaries
squeeze big profits!

15. Consequences of the re-intermediation process: the
case of the Italian hospitality industry
Source: elaboration on AIDA data
Market shares
shifting from large
to small players,
BUT…

16. Consequences of the re-intermediation process:
the case of the Italian hospitality industry
15%
20%
25%
30%
35%
40%
45%
50%
Grandi
Medie
Piccole
Micro
Hotel size
Services costs/Revenues
Source: elaboration on AIDA data
…BUT, Increasing
unit service costs

17. Open data. Light and shade
● Better transparency for
customers
● Better analytics for
governments to reduce
inefficiencies (Health public
expense in the UK)
● Precondition for citizens’
empowerment.
● They may allow the citizen to
dis-intermediate the
government and media.
• Open data as a public good.
 Leave them it and then
someone will figure out how
to use them.
• Not yet many cases of
commercial exploitation
for open data.
– Few start-ups born on
open data (e.g. only four
in the UK) and with
business models yet to
be constructed!
LIGHT SHADE

18. Open data. Beyond the hype
● Data fusion and analytics as the value added
activities.
● From a survey on the Italian www.dati.gov.it
– 151 apps providing data that are in large part static
– Apps as “a static Window” for municipalities for
informing on events, parking, touristic attractions,
etc.
– No business models around the majority of the
apps (open data as a terrain for hobbyists? )

19. Recap and Final considerations
● Big data as a disruptive innovation (re-intermediation
processes, new entrants)
● Big investments are needed. Not always a business for small
firms…
● Many examples of small businesses providing services
“linked” to the ones of “platform leaders” á la Facebook.
● Can open data really be a “key ingredient” for start-ups?
Strategic resources are rarely for free, “by definition”! Still
Searching for a scalable business model.
● Big data management capabilities and Italian companies.
They are not only an opportunity…