We are constantly generating increasing volumes of data with everything we do. During a recent business trip, I started thinking about how travelling presents a great example of this. With the explosion of the Internet of Things into our lives, the amount of analysable data we leave behind us as we go about our day-to-day lives is growing exponentially. So I decided to try and identify some key bits of data I generated and left behind on a trip from my home in the heart of the English midlands (actually, the “smart city” of Milton Keynes) across the western ocean to the Big Apple.
Source for picture: click here
I am generating data before I even wake up – the sleep monitor on the fitness-tracking wristband I am wearing is uploading data on my movement, heart rate and skin temperature to a cloud somewhere, so I can anaylze the quality of my rest. Once I am awake and moving about the house, my Nest thermostats, fire detectors and cameras detect this activity and register that I am home and awake.
Although my home city is host to the UK’s first driverless car trials, this technology isn’t available to me yet, so I will have to drive myself. As I head down the motorway to the airport, the GPS in my phone is constantly updating an entire conglomerate of mostly California-based software companies on my location. Telemetry fitted to my car is passing information on the speed I am travelling in and the route I am taking to my insurers, which use this to dynamically assess the premiums I am paying according to the risks I take. Crawling along at 5mph on the M1 motorway this morning, the risks seem fairly low so I smile to myself in anticipation of the cuts to my premiums I can doubtless expect (right?)
On top of that, the networks of CCTV cameras fitted with ANPR catch me at key points in my journey – sometimes this information goes to highway authorities to help measure traffic flow, and sometimes to law enforcement where red flags will pop up if my car is uninsured, untaxed, reported stolen or if its driver is of interest to them (hopefully not!)
At the airport I check in using the smartphone app provided by the airline I am flying with. Now they know I am at the airport, but in reality they’ve been tracking me for far longer than that, and thanks to this they know that I like to sit in a window seat and the cabin crew may well know what refreshments to offer me on board thanks to data I’ve left behind on previous trips.
The aircraft I’m on will be generating a lot of data of its own, too. If (as is likely) it is one of the 50,000 which is fitted with a Rolls Royce engine, then data from over 100 sensors will be live streaming to engineers at the company headquarters in Derby, England. Air pressure, operating temperature, speed and vibration levels are all sent in real time and minor problems can be corrected, literally on the fly. More detailed data (several terabytes per flight) is stored on flight data recorders and transferred to HQ when the flight has landed.
After landing, I might be feeling a little peckish, and decide to grab a coffee and something to eat at the airport. For small transactions like these, I am increasingly becoming used to using digital payment services such as Apple Pay. But whether I go with that or decide to use a credit card, once again data on my location is being sent to whoever is handling my payment, as well as data on what I buy, which will be sold to other people to help them decide what to sell me. The franchise outlet selling me my food and beverage also adds this same information to the file it holds on me. Even if I don’t hold their loyalty card, it will most likely be building a profile of me using my credit card number or digital payment ID as an anchor. If I pay cash, I’m still generating data although it will only be tied more loosely to me as a relatively anonymous air traveller who has just arrived at New York JFK Airport.
Next I need to get to my hotel, and like a lot of other people these days I am finding that Uber is a convenient and cost efficient way of getting about in big cities. So I’ll be adding to the data which that company holds about me, with information on my location, the distance I am traveling, the cost of my journey and any personal preferences I specify while using the service.
Arriving at my hotel the staff already have a good idea of who I am, thanks to information I’ve left behind on previous visits. The international hotel chain I am using today records data on every detail of how its customers interact with its services, from how and when they made their booking to their room service preferences and information left on customer feedback forms. This is then used to accommodate me and meet my needs in a manner which is likely to make me want to return.
And that’s just one journey. As I’ve arrived late in the day and I’m free to enjoy the city until tomorrow morning I might choose to visit a theatre, museum or restaurant. All of these are likely to cause me to generate more data which will be recorded somewhere for someone to analyze. In fact there’s very little I can do without generating data! Even if I choose to wander the streets and sightsee, countless cameras will capture my image and my phone will transmit information which will enable my location to be recorded, from satellites in space through GPS and through radio waves directly to the nearby transmitter towers. RFID tags in shop window displays will register my presence and any sounds I make to make sure I’m not shooting anybody.
Everywhere we go and everything we do generates data, and it seems likely that this will be increasingly true as time goes on. It’s important to remember that a lot of this data is anonymized, in fact it should be hoped that this is true for all of it, except in cases where you have explicitly given permission to be identified individually. But with the efforts some companies take to secure those permissions, you’ve probably given them away already to more people than you think!
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science