The End of the Journey—But Also the Beginning
You may recall that in chapter one we discussed a car park? More specifically, a car park with sensors and chargers for electric vehicles, which were “smart” and could therefore send and receive data over the Internet. This technology allows us to drastically expand the possibilities we have with the car park beyond the purely material qualities of a traditional car park.
Everything from the operation of the car park (how one finds available spaces, logs parking time, and pays) to considerations for future expansions of other car parks can be inferred from the data that goes in and out of the system. There may also be valuable insights in the data—about the use and the users of the car park—that have a commercial value to third parties.
There are of course a host of other considerations and qualities even with a car park, but at its core, it is a relatively simple example. And yet, there are many secondary effects here that one can identify and utilise.
So what if we upgrade the example to something more complex?
A “slice of life” at an airport—a data-driven mega project
There’s a lot happening at an airport. No, seriously—this is undoubtedly one of the most complex undertakings humanity has embarked on. There are many complex operations out there, but few have as many intersections between ordinary people, logistics, space considerations, laws and regulations, immigration, financial considerations, environmental considerations, and, and... well, you probably get the picture.
Let’s imagine a sequence of events at this hypothetical airport, a sort of “slice of life” moment, if you will.
- The plane: A plane lands on a runway outside a busy airport, exactly according to schedule. In the cockpit, lights flash on a dashboard full of critical information about fuel, altitude, weather, and everything else a pilot needs to know for a smooth landing.
- The control tower: Meanwhile, a few hundred metres away, numbers, graphs, and maps are being updated on a screen in real-time on a completely different digital dashboard. We are inside the control tower: Here are real-time visualisations of air traffic on a map. If two planes were to end up on a collision course, air traffic controllers are ready to instruct the pilots out of each other’s paths. Sensory data from wings, wheels, and engine are presented in a chart. All fields are green and indicate that everything is in perfect order.
- Duty-free: Inside the airport, in the back room of the duty-free shop, the number 94% lights up on a screen. The employees are soon reaching their daily sales target. Working from home, the person responsible for retail and dining in the international terminal can smile at his dashboard showing green numbers in the duty-free shop, but ponder over the steady decline in restaurant visits over the last quarter. Perhaps next year they should focus more on cafes and less on restaurants? Or, in business language: Adjust KPIs to secure new revenue streams?
- Logistics: In the logistics department of the company that supplies all these goods to the airport kiosks, a tool is used to visualise data on product flows and delivery capability, so they can identify bottlenecks. This company is forward-looking, using machine learning models that predict shift schedules and route optimisation.
- Operations: A team of engineers and data scientists are monitoring a digital twin of the entire airport. This includes data on flight routes, luggage tracking, security routines, passenger flow which—combined with all these other indicators—help ensure that air traffic operates as it should and that the hundreds of thousands of people, rushing through security checks towards gates, have safe and seamless experiences.
- Administration: In the administration offices, analysts and finance experts are preparing a presentation for the airport’s shareholders and investors about the year’s results and the next five-year plan. Here they have page after page of information, visualisations, and statistics that support their points and arguments. To find and create these, they have delved into the airport’s databases and registers, extracting data they trust and have quality assured. To do this, they have developed a system where the formats and units used are consistent, the data is synchronised across the different parts of the airport, and they know that they are up-to-date and complete.
- Digital collaboration: On the symbol for the finance director’s work chat application, a red dot lights up, but because her phone has learned through artificial intelligence that she doesn’t want to be disturbed by notifications during meetings, the alert doesn’t make a sound. However, when she checks the alert later, she will see a link to a Google Drive folder, showing the draft of the presentation. The folder is in the cloud and accessible as long as she is online.
All of this is data-driven work. If you take data out of the process, the house of cards quickly collapses. The person responsible for retail would have had to show up physically. The finance director would have had to request the presentation personally, and the presentation would have had to be shown on a whiteboard—perhaps flashing transparencies on an overhead projector? Remember those?
The data for the duty-free shop would have had to be manually found in logbooks. The control tower would have had to call each individual plane via radio and would not have had a precise overview of where the planes were at all times. The risk of errors, accidents, and poor communication would have been much higher. And the efficiency? Much lower. The pilots would have had to look at physical dials and analogue instruments to assess their landing.
The data keeping society aloft
Operations such as a car park greatly benefit from data-driven, modern workflows, gaining incredibly valuable insights in return. They operate better and more efficiently with data-driven processes in place. However, the car park could function without.
An airport, on the other hand, is so complex that without data-driven processes, it simply couldn’t operate as it does today. Without data, the aviation industry would be set back decades.
In such a complex operation as running an airport, there is also considerable secondary use of data, and without data-driven processes, this would be lost. Consequently, potential revenue, significant opportunities, and many jobs might disappear.
Insight
Primary and secondary use
Do you recall the example of the multi-storey car park from the beginning of the Data Journey? There, we imagined a data-driven car park with smart electric vehicle chargers, sensors monitoring whether spaces are occupied, access control and payment via an app, and so on.
The primary purpose of working in this way is to optimise one's own systems. But we remember that the car park’s data can also be valuable in other ways—for instance, providing useful insights for insurance companies, urban developers, the local council, and nearby businesses. This is an example of the secondary use of the car park’s data.
Similarly, in the data-driven airport, there will be a wealth of data about behaviour, movements, systems, and processes that can be valuable far beyond the airport itself.
From the very simple to the enormously complex—data has become an essential part of our modern lives. It is worth its weight in gold, and you are right to care about it. However, data is entirely dependent on technology, and we humans must understand the technologies and how they interact with the data to integrate them into our lives. Similarly, technology depends on data—and we modern humans depend on both.
It is also us humans who often create and collect the data, and also find out where it is. And crucially, what data we are allowed to use, and what we should stay clear of. The data itself, however, does not tell us how it should be structured and organised; that’s something we have to figure out—yes, along with a bit of code and analysis tools, of course.
Because ultimately, the goal with data is to be able to use it, whether it’s in everyday life, for professional purposes, or to find out which TV series we should watch now that we’ve finished the last episode of Game of Thrones for the second time. Perhaps it will be a series that the algorithm suggests will be just right for you?