Data is amazing. You know that already. You’re told it every moment of every day. We are literally told or shown by ESPN, our kids report cards, our treadmills, our wristwatch, our Alexa, our Google, our Siri, our phones, and our apps that data is here and has the answer.
In some cases this is accurate, but in many cases, data isn’t the answer. Actually, it doesn’t even describe the answer. Instead, it is a massive glut of content, numbers, and unstructured signals that require a lot more work to actually be useful.
For example, take the use of telemetry
“Telemetry is an automated communications process by which measurements and other data are collected at remote or inaccessible points and transmitted to receiving equipment for monitoring.” – Wikipedia
I remember being absolutely fascinated as a kid by telemetry when it came to “tagging” animals in the wild to learn more about them as they travel and live their lives. One example of this has been the tracking of sharks as they traverse the oceans.
In the last couple of years, this data on sharks (and where they may be headed) has sparked a lot of interest along the East Coast of the United States. Here’s a great example of the yellow line indicating the various path waypoints for this 91.5lb female white shark named Amagansett, as she travels between her work in Montauk and her summer home in New Jersey. She is literally a baby shark.
Ocearch tracks whales, seals, sharks, and many other types of sea life as they make their way back and forth across the ocean. The tracking is available on their site and you can even sign up to follow certain sharks as they send in their telemetry data to the center.
All of this comes together as a ton of data including, location, speed, depth, heart rate and other metrics in the hope that we can better understand these animals in their own environment. By tracking and storing all of this data, environmentalists and biologists hope to analyze and improve the understanding of these magnificent creatures, without disturbing them.
The process of tagging an animal certainly has some momentary pain for the animal, and then they pretty much go about their lives and begin to transmit tons of data.
Fitness Trackers Are Telemetry Tags for Humans
In her book, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, Shoshana Zuboff does an excellent write up on telemetry and its origins and similarities to human tracking. Telemetry in the wild requires us to capture and forcibly attach a device to the animal. In the human realm, this happens in retail stores and on Amazon when we buy our fitness trackers. Sure, it’s a bit painful to pay the $600 for a top-notch tracker, but heck, I want to improve my fitness, so let’s go for it.
About ten years ago, this was my Timex watch. It was before fitness tracking really took off. There were a few Garmin and Polar trackers back then, but they were like strapping a microwave to your wrist, so only the most die-hard triathletes used them. Most of us just used this type of stopwatch to keep track of how long we worked out. This Timex was a higher-end model because it actually stored something like the last 20 workouts, which amounted to exactly 40 data points total. Start. Stop. Start. Stop. Start. Stop.
Today, I have this incredible Garmin Fenix watch. This watch tracks literally everything. Time, location, waypoints, direction, speed, cadence, pace, heart rate, elevation, VO2 Max (oxygen velocity), steps, weight, swim speed, and even your SWOLF. Never heard of SWOLF? It’s what you get if swimming and golf have a baby.
To think how far these devices have come in such a brief period is amazing. I imagine they learned many of their algorithmic tricks from the classic use of telemetry in other fields of biology.
Tracking all of this data is at first amazing to a data geek such as myself. However, after a few months of inspecting and analyzing the data, I came to understand two painful realizations.
First, I’m No Healthier
Gathering data is not the same as having an answer or an action plan. The reality is that data is helpful but it must be interpreted and turned into action by individuals. As it turns out, my findings personally are not that dissimilar to others, in that fitness tracking doesn’t have a drastic difference in the outcome.
The reams and reams of data available from Garmin are impressive but what I realized is that I would analyze that data for a pretty decent amount of time each week. At the end of each workout and at least a few times a week, I’d review the data for anywhere from 5 minutes to 10 minutes. Add that up, and I could have actually used that time to work out, resulting in roughly one additional 45-minute workout a week. Doesn’t sound like much, but to give credit to the Timex stopwatch, finding the most important data points and focusing on those is sometimes more important than tracking everything.
Second, I’m Producing a Ton of Personal Data
Being the geek that I am, I took the output from Garmin from one 8-mile run along the Jersey Shore and began to analyze it. Garmin provides these beautiful dashboard views of the data it gathers workout by workout. You can view some combination of screens on your phone or desktop through their App.
Not only am I not any healthier (point 1), but the amount of data I am producing and sharing back to Garmin is astounding. Additionally, Garmin reminds me regularly that, if I want to, I can link Garmin to my Apple Health App and other platforms to get the most out of all of this data.
So I decided to export all of the underlying data from this one workout into a CSV file, where I then converted the data to plain text. I wanted to see, in more human terms, just how much data am I providing back on a single workout. Naturally, I was expecting a few pages of content, but what I found was that this single workout, when imported into a Google document was 396 pages long (single-spaced text.)
Look, this is disturbing. One jog at the shore sent more pages of personal data to a third-party (and probably others) than all the pages in my first book, Data Leverage, Unlocking the Surprising Growth Potential of Data Partnerships. In fact, it was almost double the length of our book which is wholly depressing. Thank goodness I didn’t go for a stroll along the beach this time.
I love my watch. I am fascinated at the capabilities to track all of this data, but at this point, I am literally no different from any animal being tracked in the wild. And while the general understanding of tracking baby shark is to learn and help protect these creatures, the data we are all sharing with companies is used for little more than to target us with additional marketing and sales.
Again, I am not saying there isn’t value here. There is for sure. But at some point, you need to question whether we need all of this data. Ask yourself:
- Am I really better off because of this data?
- Am I making better decisions with this data?
- Would far less data be just as useful to me in my decision making?
- Who else is getting all of this data, and what are they using it for?
After a couple of decades in the data business, all I can say is that more data doesn’t always equate to better decisions. I’ve seen it time and time again. Sure, it can, but most of the time storing massive quantities of data ends up in piles of unused database records with little to no purpose for existence. In the old days, we were taught “Save everything!, Data storage is cheap!” but in this new age of data minimization and GDPR-like regulations, it is time that you evaluated all of the data you are creating and sharing as a baby shark.
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