1. IT biznis model za plasman turističkog prozvoda
Prof. dr Angelina Njeguš
Vanredni profesor Univerziteta Singidunum

2. Pregled top 10 tehnologija u 2014.
Mobilne tehnologije
Big Data tehnologije
Inteligentna rešenja
Cloud Computing
Društveno umrežavanje
Bezbednost
Green tehnologije
...

3. Evolucija turizma 1980s 1990s 2000s 2010s Servisi: Centralizovani
Cloud
Computing
Service-Oriented Computing
Cloud turizam
Internet Computing E-Business
Client-Server
Computing
e-Turizam
Mainframe Computing
Servisi:
Centralizovani
Deljeni
Samouslužni
Softver:
Centralizovan 
Decentralizovan
Distribuiran

4. Cloud Computing servisi u turizmu
SaaS
Software as a Service
Micros
Cloud POS
Fidelio
WebSuite
Sabre Airline Solution
Amadeus
e-Ticket
PaaS
Platform as a Service
Database
Development tools
Middleware
IaaS
Infrastructure as a Service
Storage
Servers
Network
Hotels & Resorts
Restaurants and Foodservice
Cruise
Airline
Destination Service Providers
Travel Agencies

5. Big Data
Rast cloud, mobilnog i društvenog (Social) računarstva dovodi do toga da su organizacije danas preplavljene:
ogromnim obimom podataka
velikom brzinom podataka
Raznolikim podacima
Digitalni univerzum će narasti do 8 ZB u 2015
Rast cloud, mobilnog i društvenog (Social) računarstva dovodi do toga da su organizacije danas preplavljene:
ogromnim obimom podataka (svakodnevno obrađuju čak do petabajt raznih vrsta podataka)
Svakodnevno se kreira 12 terabajt tvitova, koji se prate radi analize klijenata, potreba tržišta ili analize proizvoda
Godišnje se sa brojila očitava 350 milijardi podataka kako bi se bolje predvidela potrošnja električne energije
velikom brzinom podataka (ponekad je i 2 minuta prekasno, npr. potrebno je što pre uočiti računarski kriminal, kao što je krađa osetljivih podataka o gostima hotela)
Dnevno se nadgleda 5 miliona transakcija kako bi se identifikovale potencijalne prevare
Svakodnevno se analizira u realnom vremenu oko 500 miliona zapisa o pozivima klijenata kako bi se na vreme uočile bilo koje promene kod klijenata
Raznolikim podacima (bilo da su struktuirani ili nestruktuirani podaci, kao što su tekst, podaci od različitih senzora, audio, video i klik tokovi podataka, log fajlovi i dr)
Uživo se nadgleda oko stotinak video zapisa sa kamera
Rast od 80% podataka u slikama, video zapisima ili dokumentima koristi se radi poboljšanja zadovoljstva klijenata

6. Veličine Big Data Binarni prikaz Naziv Ozn Decimalni prikaz byte B
100=1
kilobyte KB
210=1.024 byte (B)
103=1.000
megabyte MB
220= B
106=
gigabyte GB
230= B
109=
terabyte TB
240= B
1012=
petabyte PB
250= B
1015=
exabyte EB
260= B
1018=
zettabyte ZB
270= B
1021=
yottabyte YB
280= B
1024=
brontobyte BB
290= B
1027=
geopbyte
GeB
2100= B
1030=
Bit (b) – 0 ili 1
Bajt (B) – 8 bita
Kilobajt (KB) – 1024 bajta

7. Šta je Big Data?

8. Inteligentni sistemi
At a fundamental level, it sounds like a relatively simple recipe: Take one tiny, very powerful microprocessor, connect it wirelessly to the cloud, and add a generous portion of very smart software.
According to many experts, these are the primary ingredients of an intelligent system, a place where everyday objects provide information to a centralized computing infrastructure where this data is aggregated, sliced, diced and analyzed.
Expand that recipe 50 billion times, add the immense storage capacity and analytics functionality provided by cloud services, and the next big intersection of technology and society begins to emerge. It’s a very real place where smart devices share their experiences wirelessly with large data warehouses, and software then connects that data in logical ways to portray the hidden patterns and trends that allow organizations to see business intelligence faster than ever before — from individual buying habits, to oil consumption by region, to the epidemiology of diseases across continents.
As the cost of powerful microprocessors continues to decrease, the economic argument for embedding them into everyday objects becomes more and more practical. Consider how much smarter a soft drink company would operate if every single can of soda in the United States could relay information on where it was purchased and where it was consumed.
If it sounds far off, it’s not. The technology is already available, and shipments of microprocessor-enabled devices capable of handling sophisticated software and connecting wirelessly are already outstripping shipments of mobile phones and PCs. By 2015, shipments of embedded devices will surpass those of phones, PCs and servers combined. Developers around the world are building intelligent systems for manufacturers, retailers, healthcare providers and a host of other industries.

9. Uticaj Web generacija na turizam

10. Digitalni klijent
Google analitičari ukazuju da putnici pre bookinga posete 22 veb sajta
Digitalni putnici imaju kontrolu i žele više
Više od milijardu korisnika ima facebook nalog
Više od 200 miliona tweet-ova dnevno se postavi na Twitter
Apple je prodao više od 2 miliona iPhone 5 pamentih telefona
Putnici više vremena provode na internetu nego gledajući TV ili druge medije
Putnici sve više koriste mobilne uređaje za planiranje i rezervisanje putovanja ...

11. IT tehnologije prema životnom ciklusu klijenta
Šta klijent želi?
Različiti profili klijenta
Leisure (adventure, eco, heritage, wine, packaged beach vacation ...)
Business (MICE)
Health (spas, fintess, medical intervention ...)
Educational/study
Visiting firends or relatives (VFR)
Religion
Sport
Digitalni klijent
Mobilne aplikacije
Profil prema putanji pretrage informacija
Deljenje informacija na društvenim mrežama i drugim e-medijima
CRM

12. Kastimizacija poslovnog modela prema percepciji klijenta
Source: WTO (2007) A Practical Guide to Tourism Destination Management

13. Faktori koji utiču na imidž destinacije
Source: Ndlovu, J. (2009) Branding as a Strategic Tool to Reposition a Destination. Faculty of Economic and Management Sciences. University of Pretoria. Available at: (accessed: )

14. e-Marketing aktivnosti
Source: WTO (2007) A Practical Guide to Tourism Destination Management

15. Promotivne aktivnosti prema životnom ciklusu klijenta
Source: WTO (2007) A Practical Guide to Tourism Destination Management

16. Strategije u razvoju vebsajta

17. Uticaj distributivnih sistema
Trenutno stanje distributivnog lanca putovanja
Source: European Commission (2013) Business Model TourismLink. Available at: (accessed: )

18. Postojeći distributivni sistemi
IDS – Internet Distirbution Systems
OTA – Online travel agency
Source: European Commission (2013) Business Model TourismLink. Available at: (accessed: )

19. Market Share of Internet Distribution Systems in region D-A-CH
Source: Schegg, R., Fux, M. (2012). The Power of Internet Distribution Systems (IDS). Institute of Tourism. University of Applied Sciences of Wester Switzerland Valais Sierre.

20. Kriterijumi za izbor kanala prodaje
Source: Schegg, R., Fux, M. (2012). The Power of Internet Distribution Systems (IDS). Institute of Tourism. University of Applied Sciences of Wester Switzerland Valais Sierre.

21. Studija slučaja: German National Tourist Board (GNTB)

22. Studija slučaja: GNTB

23. Studija slučaja: TOB

24. Studija slučaja: TOB