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

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

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

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

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

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=1.048.576 B 106=1.000.000 gigabyte GB 230=1.073.741.824 B 109=1.000.000.000 terabyte TB 240=1.099.511.627.776 B 1012=1.000.000.000.000 petabyte PB 250=1.125.899.906.842.624 B 1015=1.000.000.000.000.000 exabyte EB 260=1.152.921.504.606.846.976 B 1018=1.000.000.000.000.000.000 zettabyte ZB 270=1.180.591.620.717.411.303.424 B 1021=1.000.000.000.000.000.000.000 yottabyte YB 280=1.208.925.819.614.629.174.706.176 B 1024=1.000.000.000.000.000.000.000.000 brontobyte BB 290=1.237.940.039.285.380.274.899.124.224 B 1027=1.000.000.000.000.000.000.000.000.000 geopbyte GeB 2100=1.267.650.600.228.229.401.496.703.205.376 B 1030=1.000.000.000.000.000.000.000.000.000.000 Bit (b) – 0 ili 1 Bajt (B) – 8 bita Kilobajt (KB) – 1024 bajta

Šta je Big Data?

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.

Uticaj Web generacija na turizam

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 ...

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

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

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: http://upetd.up.ac.za/thesis/available/etd-09242009-225847/ (accessed: 7.05.2014)

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

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

Strategije u razvoju vebsajta

Uticaj distributivnih sistema Trenutno stanje distributivnog lanca putovanja Source: European Commission (2013) Business Model TourismLink. Available at: http://www.tourismlink.eu/wp-content/uploads/2013/01/business-model-pres-to-AB-6th-Nov-final.pdf (accessed: 7.05.2014.)

Postojeći distributivni sistemi IDS – Internet Distirbution Systems OTA – Online travel agency Source: European Commission (2013) Business Model TourismLink. Available at: http://www.tourismlink.eu/wp-content/uploads/2013/01/business-model-pres-to-AB-6th-Nov-final.pdf (accessed: 7.05.2014.)

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.

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.

Studija slučaja: German National Tourist Board (GNTB)

Studija slučaja: GNTB

Studija slučaja: TOB

Studija slučaja: TOB