Big Data, the unprecedented quantities of information and content we generate on a daily basis, has many uses in day to day life that perhaps go unnoticed, but are essential to the effective running of key services and industries.
In this series of blogs we’ll take a look at some of the less obvious uses of data collection and analysis in the world around us.
In this first blog we take a look at the way big data is used to improve and optimise performance in the aviation industry.
In Quarter 3 of 2012, UK airports handled 67.2 million passengers on 554,801 commercial flights (CAA Aviation Trends). The logistics of getting all these individuals to their destinations on time, fed, watered, and with their luggage, are complex, to say the least, and the quantities of data collated on a daily basis are incredible. As global travellers pass through UK immigration and security en route to worldwide destinations, make purchases in Duty Free, transfer to other transport networks, book into accommodation, and use their mobile devices, complex data is collected and compiled to ensure the efficiency and maximise the profitability of all aspects of the aviation industry.
In a time-critical industry where the scheduling of flights is done to the second, it’s essential that database management systems are agile and fast enough to handle petabytes of real-time data in an instant. When used in combination with historical data, complex algorithms are able to make predictions and enable rapid decision making in response to changing circumstances and new data coming into the system from multiple streams.
From passport scans, Air Traffic Control data, and purchase transaction records, to CCTV, mobile network data and live web content from news and social media, these data streams are live and can be largely unstructured, making them inherently difficult to deal with, particularly using traditional database management systems (DBMSs).
In the past, Business Intelligence tools simply collated and presented data linearly, following a process of cleansing and organisation. The presented data then had to be interpreted by data scientists, adding more time to the process and meaning data-derived decisions could not be rushed.
Modern DBMSs cleanse and sift data in real time and in its native format, meaning that airlines can offer the best possible service based on the flow of passengers, economic and political conditions, and competitor activity. Weather conditions like wind and storms can be instantly taken into account, enabling for agile route-planning, fuelling, and rescheduling where necessary. With constant updates informed by live data streams, pilots and in-flight maps and displays can give passengers constant, accurate updates about flight duration and progress.
With the advent of Big Data, Airlines are also far better placed to deal with disruptions as they occur [although it seems that, in the UK at least, we’re still largely unprepared for a bit of snow!]. When extreme bad weather hits, when a passenger is taken ill on flight, or when a delay means missed connections or lost baggage, agile algorithms are essential to provide instant recommendations of the best possible solutions, requiring minimal human input before a decision is taken and implemented.
Big Data analytics and insights also play a part in the build-up to travel with dynamic pricing depending on the time and place that you search for flights, combined with further data from your computer and historic information about purchasers who fit similar demographics. Airlines also tend to overbook flights to ensure all seats are filled, again using complex algorithms to forecast the number of no-shows.
But there is still more potential value for airlines to derive from the ever-developing field of big data.
As the cost of delays continues to rise, airlines must increase their control over the elements they can monitor and influence, but which are currently under-performing, such as mechanical maintenance. If data can be compiled and tracked from all fields such as hand-written pilot notes, engineer testing, manufacturer statistics etc., across the entire fleet, it should be possible to accurately anticipate when parts will need maintenance or replacement. This level of forecasting will mean that engineers can be ready at the next destination, with all required parts, in order to carry out any necessary repair during the standard vehicle turnaround period, instead of the pilot discovering problems while the vehicle is at the gate, preparing to depart, or even after that.
With potentially huge savings to be made from fewer mechanical faults and minimised delays, expect to see aviation industry leaders increasingly adopting big data analysis systems in the coming months and years.
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