Systems and expertise are barriers to exploiting big data, says Amadeus study

Fragmented technology and a scarcity of data scientists threatens to hamper travel’s attempts to exploit big data, according to a report released today by Amadeus.


The study entitled ‘At the Big Data Crossroads: turning towards a smarter travel experience’ was written by Harvard Business School professor Thomas H. Davenport.


He concludes that while there are obvious advantages to the big data evolution for travel firms the industry needs to make it a priority if it is to realise the benefits.


Davenport, who is the co-founder and director of Research at the International Institute for Analytics, said:


“The travel industry stands at a big data crossroads today, with new technologies and techniques offering the potential to translate increasing volumes of data into higher profits and more efficient operations.


“Some leading companies are pioneering the use of big data and already seeing a huge impact.


“Airlines, airports, hotels, rail companies and travel sellers need to ask themselves if they have a big data strategy in place, and if it will allow them to be at the forefront of this opportunity.”
 
Hervé Couturier, head of research and development at Amadeus, added: “We are committed to facilitating discussion on key trends in order to participate in the debate around how the future of our industry will be shaped, and the key talking point right now is undoubtedly big data.


“It is impossible to overstate the transformative potential of big data, both in terms of improving the travel experience and how the wider industry itself operates.


“With this in mind, it is perhaps the single biggest opportunity in a generation for travel businesses: to embrace the changing structure of data in order to maximise it.


“At the same time, big data also offers us the chance to ‘put the fun back into travel’, which at its very heart is about improving the passenger experience.”


The Amadeus study charts the emergence of new technologies and strategies for managing big data, and outlines how it can be harnessed to focus travel around customer needs and preferences, not industry processes. 
 
Case studies focus on the likes of Air France-KLM, Cathay Pacific, Eurostar, Facebook, Frontier Airlines, KAYAK, Marriott Hotels and Munich Airport.


Below are the key findings from the study.
1.    Now is the time to act: Professor Davenport calls on travel firms to start benchmarking their maturity against the industry whilst assembling the necessary data science skills and formulating an overall big data strategy for their organisations.
 
2.    Big data offers major opportunities for travel companies to improve both the business and experience of travel: the benefits of big data include better decision-making, greater product and service innovation and stronger customer relationships that will be delivered by new approaches to customer management, revenue management and internal operations.
 
3.    Pockets of innovation using big data are present in the industry today: the study includes examples of how leading travel firms are making use of big data today: from KAYAK’s price flight forecasting model, which presents customers with the likely change in a flight’s price over a seven-day window, to Air France-KLM’s use of Hadoop as the basis of a group-wide revenue management system.
 
4.    Emerging technologies will be key to the big data evolution: the onset of new open-source software for dividing data processing jobs across multiple commodity servers, together with new types of databases including ‘columnar’ and ‘vertical’ approaches, and emerging programming languages like Python, Pig and Hive, combine to deliver the potential to harness big data.
 
5.    The effective deployment of big data initiatives is not without challenges: the study finds that to access the big data opportunity the travel industry must overcome significant challenges, including: data fragmentation across multiple systems; co-existence of both big data and traditional data management architectures; finding and recruiting scarce big data science skills; and managing data responsibly and in the interests of all.

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