Thursday, July 4, 2013

Big Data Stretches Up to the Sky

The “balanced scorecard” can be an effective visualization tool, which specified that organizations should work to understand what factors drive organizational performance, and report them on a simple scorecard.

There’s independent study released recently: “At the Big Data Crossroads: Turning towards a smarter travel experience”, authored by Professor Thomas H. Davenport, highlights that the travel industry is at a big data crossroads: Large volume, complex and unstructured datasets are beginning to reshape the industry. Big data is arguably the biggest opportunity in a generation for travel businesses to embrace the changing structure of data and maximize its use. It would be almost impossible to overstate the transformative potential of big data to the travel industry. What are the key highlight from the research?

  1. Big Data can be the foundation for greater industry-wide innovation; Big data offers significant benefits for all travel companies: The benefits of big data for travel providers and travelers are explored, including:
    • Better decision support
    • New products and services
    • Better customer relationships
    • Cheaper, faster data processing
The “balanced scorecard” can be an effective visualization tool, which specified that organizations should work to understand what factors drive organizational performance, and report them on a simple scorecard
  1. Big Data is about the speed and cost with which this conversion can be accomplished, as Big Data involves “machine learning”.  Usage of data has always been an asset in the travel industry and it was one of the first to embrace it for business advantage. Airlines pioneered the use of price, optimization analytics, and hotels adopted the same tools with great success. Airlines also optimized the details of crew scheduling and routings. The industry was also among the first to create and take advantage of loyalty programs. However, big data analysis is more likely to involve an approach called “machine learning”. The benefit of machine learning is that it can very quickly generate models to explain and predict relationships in fast-moving data. The downside of machine learning is that it typically leads to results that are somewhat difficult to interpret and explain.  
  1. Better decision support for both internal operational efficiency and business ecosystem effectiveness: Many travel firms are using big data not just to speed up decisions and data processing, but to make better internal or customer focused decisions. One class of uses for big data in the travel industry involves making internal operations more efficient and effective. As with other categories, the use of data and analysis in internal operations is not new. Airlines pioneered the use of analytics for routing, crew scheduling, and maintenance logistics decisions. There are other frontiers in human resource analytics that it has not explored. External big data also offers the possibility of improving other types of travel industry decisions, with benefits involving efficiency and safety. Forecasting consumer demand, for example, could be improved through the analysis of macroeconomic and weather data. A final process to which big data and analytics can be applied is financial performance management and the evaluation of capital investments. 
  1. Big data helps players in the travel industry form better customer relationships. New products and services for customers, one of the most exciting possible benefits from big data is the creation of new products and services for customers. Through predictive analytics, the most favored destinations, lodging and dining preferences, ancillary services needs, and tourism experiences can be identified for each passenger. That said, big data can provide insights that help deliver a more intelligent travel experience than has ever been possible before. There are many possible ways to increase personalization which may be valued by travelers, including:
·       Personalization based on customer behaviors or the absence of them
·       Personalization based on social media relationships
·       Personalization with regard to ancillary sales
·       Personalization involving the entire journey, not just a segment of it

  1. Revenue management—Optimize pricing for the perishable commodities offered by the travel industry, such as airplane seats and hotel rooms—is one of the most popular areas for application of analytics. It combined the two organizational processes that make up revenue management—pricing and capacity management—into one integrated process. The system contains considerably more data than previous systems, including all relevant passenger data over two years. The system calculates and optimizes the revenue for origin/destination itineraries, and bases pricing on passenger profiles. The company brought all relevant pricing aspects into its revenue management algorithm. Secondly, the company can make fast pricing changes. They enter a new price into an online tool, and it is immediately reflected in the price seen by consumers through all channels
  1. Cheaper, faster data processing: New generations of information technology have always been adopted in part because they offer better price/performance ratios. Clusters of commodity servers running Hadoop and other open-source software can process data at costs fifteen to twenty times lower than previous generations of data warehousing technology. The good news is that many big data technologies are free (as with open-source software) or inexpensive (as with commodity servers). The technology is also often available “in the cloud” and can be bought “by the drink” at relatively low cost. The downside is that big data technologies are relatively labor-intensive to architect and program. However, integrating Hadoop-based architectures with the existing “legacy” technology architectures will be challenging in some legacy travel companies.

  1. Big Data Challenges for the Travel Industry: Big Data will require the industry to address a number of challenges: Technology complexity; data accuracy and rights of use; business and technological alignment; the need for data specialists. 1) Technology Complexity: As the key data is often fragmented across multiple functions and units, in order to make effective decisions about how to promote offers to customers and recover from service failures, airlines need to combine all of this information into one data warehouse and one set of algorithms. This would require considerable investment, Creating an integrated source of customer information is not only expensive, but difficult no matter how large the available budget. Another result of the long-term use of information systems in large, established travel companies is that big data technology architectures will have to coexist with existing “legacy” tools. In addition, the real-time IT architectures used by many travel industry companies can’t run on Hadoop or other open-source environments; called TPF for Transaction Processing Facility.             2) Talent Challenge: Although mainstream travel firms do have employees with analytical skills in areas like revenue management they may not be familiar with the analytical approaches used with big data. Big data analytics tends to involve machine learning, visual analytics, and text processing—all skills that were not widely employed in, for example, revenue management.  
The research concludes that big data has the potential to dramatically reshape the travel industry. The key now, of course, is to move from potential to reality


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