Big Data Analytics For Airlines


If the airline industry could be described in two words, it would be "intensely competitive". The airline industry generates billions of dollars every year and still has a cumulative profit margin of less than 1%. The reason for this lies in this industry’s vast complexity. Airlines have a multitude of different business issues that need to be solved at once, such as globally uneven playing field, revenue vulnerability, an extremely variable planning horizon, high cyclicality and seasonality, fierce competition, excessive government intervention and high fixed and low marginal cost.

To ensure the best chance for full economic recovery, airlines should fully leverage their most prolific asset - data.  Data used in conjunction with innovative Big Data technologies will provide the capabilities for a comprehensive intelligent management and decision-making system throughout the enterprise.

The ultimate benefits of big data analytics together with airline business acumen and experience would include timely responses to current and future market demands, better planning and strategically aligned decision making, and clear understanding and monitoring of all key performance drivers relevant to the airline industry. Achieving these benefits in a timely and intelligent manner will ultimately result in lower operating costs, better customer service, market leading competitiveness and increased profit margin and shareholder value.

Airlines throughout the world are currently facing an unprecedented financial crisis. Factors contributing to this crisis are low customer satisfaction, overtraded markets, insufficient and underutilization of aircraft capacity, poor labor relations, excessive government intervention, high labor costs, ever increasing oil prices resulting in spiraling fuel costs, and generally  high operational costs. The low profit to turnover ratio of airlines have been further exacerbated by growing low-fare competition, increasing security costs, and frequent dynamic shifts in air travel consumer behavior.

The historical business model of many network airlines now appears to be unable to support sustained profitability under any but the most favorable economic conditions. The industry is at a turning point.  The market dictates an “adapt or die” policy, and the airlines that wish to survive will face the challenge of having to make significant changes to their current archaic business model. To do this requires far more allowance for analytical technologies that would allow flow of consistent, repeatable and reliable enterprise wide intelligence needed to tackle all the challenges the industry is facing.

 Deployment is the process of delivering the right information to the right people at the right time, and in a format they are familiar with to ensure they are empowered to make the most knowledgeable intelligence decisions possible. The deployment of corporate intelligence – has many facets. It could be a report to a manager, a triggering of an email to unhappy customers, a mapping of metrics to a Scorecard or to a regulatory compliance report. Another facet is how quickly and accurately intelligence can be applied to a business problem or an emerging opportunity.

 Business challenges in airline industry

 Key to successful deployment of technological advances in airline industry is to be able to anticipate how the current business model will change to survive in tough market conditions. Some of the challenges that can be successfully addressed by Big Data Analytics are:

  • The need for accurate daily and weekly performance measurement reports (e.g. “flash/estimated” revenue, operating costs and net contribution reports for every aircraft’s actual flight per sector/route).
  • The Need to better manage all aspects of risk.
  • The Need for better impact analysis and more effective optimization of all resources as well as being able to produce accurate passenger-revenue forecasts,
  • The Need for a holistic, 360 degrees view of the airline industries customers, suppliers, service providers and distributors.
  • The Need for expense verification models in order to better control all industry cost aspects.


Performance Measurements

Airlines usually operate in a globally competitive environment and therefore require prompt and accurate enterprise performance measurements. Furthermore, airlines are volume driven and small variations (passengers flown, fuel spent/bought, load carried) can multiply into major effects – therefore appropriate and timely action is critical. They also suffer substantial difficulties to produce daily/weekly reliable performance measurements.

Current airlines “legacy” IT systems such as Revenue Accounting, require several weeks after a month end to generate revenue results for every flight per sector/route. Enterprise Intelligence Platform and Big Data Analytics can automate production of daily activity reports such as number of passenger flown per flight/sector, distance flown, etc which can be used to provide estimated performance measurements such as daily or weekly revenues for specific routes or sectors.


Risk Management

The global airline industry has been subjected to major catastrophes over the past years.  It is accordingly imperative for airlines to develop various risk management models and strategies to protect themselves from negative impact of these types of events. Furthermore, due to the global playing field, airlines often earn its revenues and pay its costs in different baskets of currencies (e.g. USD, Euro, GBP etc). As a result there is frequently a mismatch between the flow of revenue receipts and expenses of each basket of currency - creating risk exposure reports.

Control and Verification

Airline carriers require a number of control and verification models to be able to control costs arising from its various operational activities. To enable this, airlines have a pressing need for a complete and integrated repository of flight information data gathered from all its disparate business units. This will enable computation of various efficiency analytics - e.g. planed fuel usage compared with actual fuel usage per aircraft, crew utilization (roster optimization). These issues could also be fully addressed by consolidating and analyzing relevant flight and aircraft data. In turn this would help to create a 360 ° view of each flight and aircraft, allowing the business users to dramatically improve their control and verification systems.

Load forecasting

Airlines require the development of an effective and holistic forecasting model to regularly assess the impact of options and alternatives such as increasing aircraft seats available, adjusting fares, introducing new routes etc. Forecasts should also take account of actual statistical trends and results e.g. actual passengers carried and actual average fares earned. Such forecasts should then be compared against budgets and prior year performance.

 Holistic customer view

Airlines would greatly benefit from knowing and understanding its business environment along some of the key business issues, such as performance, behavior, risk, profitability, etc. Using customers as an example - the main objective would be to enrich the knowledge about individual customers leading to new strategic customer segments.

This intelligence would allow airlines to reap the host of benefits such as successful, targeted customer promotions, cross-selling and up-selling campaigns for different flights and booking classes leading to improved yield and revenue. For example, it would give airlines the power of knowing to limit discounts on flight routes which are usually over-booked, allowing the large number of passengers to compete for high profit seats immediately prior to departure. Such multidimensional views of the business can help the airline to better serve its customers through more effective, efficient and personalized service, receiving in return customer loyalty, support and market share, all leading to higher profitability.


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