data tracking

Your Data and Building a Better World

Reading Time: 9 minutes

These three stories depict a world in the not-so-distant future where technology has revolutionized our lives from the mundane day-to-day routines, to the broader city, state, and nationwide management of complex systems and infrastructure.

Each story is meant to showcase a potential benefit of large scale data gathering and artificial intelligence systems trained from our personal data. While these stories don’t cover the entire picture of data collection, artificial intelligence, and other computing technologies, they offer a glimpse of what could be real in the near future, if we play our cards right.

Data By Day

It’s 6:27am, and the lights slowly begin to fade on with a warm sunlight glow. Your alarm slowly begins chiming, starting off quiet and increasing in volume, while your wearable tech also begins pulsing rhythmically. You wake feeling rested, because the wearable tech determined that the appropriate time for you to wake up today was between 6:20 and 6:40am, after an appropriate number of REM cycles in your sleep and given the time you went to bed last night, all while factoring in your total sleep debt.

You get out of bed and the blinds in your bedroom have opened slightly, just enough to brighten the room without revealing anything to the outside world. Your smart home device beeps, having detected that you are out of bed now via your watch, and tells you good morning before giving you the weather and any upcoming meetings you have for work.

Realizing that it’s going to rain later today and your daughter’s soccer practice will be cancelled, you tell the smart home device to reschedule your 3:30pm meeting for another time. The device sends a meeting request to your coworker, whose calendar system adjusts accordingly and automatically accepts the new invitation. Your home device confirms the new date and time for your meeting, tomorrow at 10am.

As you walk out of the room, you can smell the coffee brewing in the kitchen. A cappuccino brewed by an automatic barista system that knows based on previous setups and your previous coffee shop purchases exactly how you like your espresso. A pleasant chime catches your attention and tells you that the Green Line you take to work is on a delay and the next train will be departing your local station a half hour later than usual.

You tell your home device to set an out-of-office alert and that you will be working from home today. The home device connects to your work account and notifies your coworkers that you will be working remotely today. The home device pings your work computer in the office, and it starts up automatically, pulling together a list of items to do based on priority and due dates, ready to be reviewed when you start working.

The smart home device chimes a final time, now that your morning routine has completed, it asks if you would like to continue listening to the podcast you started yesterday. You say yes, and the podcast starts playing automatically from where you left off, syncing between your phone, computer, tablet, and home device. As you leave the bathroom after brushing your teeth, the lights automatically turn off, having detected that nobody is in the room anymore.

An AI work assistant sends you a message on Microsoft Teams, asking if there are any tasks you need to offload. You type “respond to client emails that need follow up meetings”, and the large-language model AI writes emails to your clients asking for follow up meetings to be scheduled. Your client’s smart-calendars and their own AI assistants begin communicating back and forth until an agreement is reached that fits all parties schedules. Your calendar chirps as a new event is added for next week, and a record of the AI communications is attached to the event’s invitation.

While this is happening, you also ask the AI to organize the reports in your cloud storage by several metrics, and to generate an aggregate report in a single document. The task is done in a minute or so, placing the document neatly on your desktop in your field of view. You open the report and begin combing through the information, when the same AI automatically highlights changes in the budget trends for a client’s project.

You write a quick message to the project manager, noting the shift in trends found in the reports, and provide some recommendations to keep the project in budget. The AI annotates the email noting that the recommendations provided have a estimated 92% chance of remedying the budget issues if implemented by the end of the quarter.

Healthy Living

You’re on vacation in a new city, and you’re looking for a bite to eat. It’s 1:30pm and you open your phone to find a conveniently timed notification from Google Maps telling you there’s a popular food truck nearby with a 4.5 star rating. You walk the four blocks over, as the mapping app sends you step-by-step directions in the form of haptic feedback on your watch. The food is fantastic, the price just right, and you get to experience the local cuisine that you may have missed otherwise.

A few days later, while walking through a large park in the new city, you feel your wrist buzz. Checking your watch, you see an alert that your heart rate and rhythm is abnormal and it has detected a possible aFib event. Grabbing your phone, you see the information automatically pushed from the watch to the larger screen. There, a map widget shows you the nearest urgent care centers and hospitals. Additionally, there is a convenient button to alert emergency services if needed. You feel fine, but just to be sure, you think to check in with a doctor.

Opening the map widget shows you the route to the nearest hospital, and it shows you routes for driving, rideshare plus fares, walking, and public transportation. You tap the public transit tab and it shows that just outside the park is a bus stop that takes passengers directly to the hospital. A few short minutes later, you board the bus and use the digital wallet built in to your phone to tap your transit card, and you find your seat.

At the hospital, you open your health app and scan your unique Patient ID to check in. You consent to be seen and treated at the hospital on your phone using biometric sensors to unlock the device. After consenting, you hold your phone near the check-in reader and the hospital accesses your digital health records, assessing the latest information from your watch’s readings and previous health information. The hospital’s system finds you to be at risk of a cardiovascular event, and determines your place in triage. A nurse comes out and brings you to the back for examination.

After examination and tests, the doctor comes in with a tablet loaded with your health data. Powered by a supercomputer’s API and the doctor’s recommendation, having been given your ECG readings, your doctor recommends a medication to help control the aFib: Metoprolol. After entering the medication information into your chart, the Hospital’s AI notes that there may be a drug interaction between Metoprolol and an anxiolytic you take. The AI also notes that your watch recorded unusually high blood pressure over the past several days. Based on the information, the AI recommends a series of treatment and diagnostic approaches to the doctor. The doctor prescribes you a blood pressure medication in addition to the temporary dose of Metoprolol, and sends the treatment plan to your digital health record.

The prescription is filled, and the medication is added to your digital health record, where your phone will automatically remind you when and how you should take the medication. Both the doctor and the Hospital’s AI agree that the incident is due to high blood pressure, and your primary care physician is notified of the recent diagnosis.

After returning home from your trip, your doctor recommends that you make certain changes to your diet and get regular light exercise to help combat the high blood pressure. Your digital health record asks if you would like food and exercise recommendations that fit your treatment plan, and you agree. Over the next three months, your phone and watch coordinate to recommend taking small walking breaks during lunch and after work, giving you new routes to try out and new paths to take in your neighborhood park. Arriving home, your TV, which recognizes that you have entered the room, shows a small notification recommending what you could try for dinner given the food that you already have in your fridge and pantry. The meal is designed specifically to satisfy both your personal taste and your treatment plan, avoiding foods that are high in cholesterol.

Two months later, your digital health record reminds you of a follow up appointment with your primary care physician. The day before the visit, your watch and phone sync all of your health data and activity data and send it to your doctor for review. At the appointment, the doctor runs a rapid cholesterol test and checks your blood pressure, verifying the information against the blood pressure information provided by your watch. The doctor has determined that you no longer need the blood pressure medication, and the health AI concurs. You stop taking the blood pressure medication, but you stick with the changes to diet and exercise. A month later, your doctor sends a new item to your health record. Using aggregate data from tens of thousands of other patients, the AI system indicated that had you not made the changes to your lifestyle, your risk of a fatal cardiovascular event was high. The system has indicated that your current risk of any cardiovascular event is extremely low.


In your same home city, an elderly woman goes on her daily walk through the park for fresh air. The ground is damp from last night’s light rain, and the air is crisp with the scent of spring. She sits on a bench and watches as families pass by, joggers switch lanes to pass strollers, and cyclists ring their bells. She stands and begins walking down a dirt trail in the park towards a pond. At the end of the trail are concrete steps leading down to the water.

The moss on the steps, slipperier than usual, pose more of a challenge than the woman can safely handle, and she falls. Her watch immediately alerts emergency medical services. She is now speaking to a dispatcher who sees the woman’s vital information live on screen, noting an elevated heart rate, and minimum movement. To the left of the vitals is a map pinpointing the woman’s location and detailing the time and severity of the fall: how far she fell, how hard the impact was, and the approximate conditions of the environment around the woman based on live satellite imaging.

Emergency services arrive at the woman’s exact location within minutes. She is taken to a hospital where she is treated for a bruised femur and a hairline fracture in her hip. Had emergency services not found her quickly enough, the hairline fracture could have worsened if the woman had tried to move to a more visible location to be found.


On the other side of the country at a research facility in a university hospital, a doctor uses the hospital AI to aggregate information provided by an opt-in study of patients taking two medications. These specific medications have never been studied together in clinical trials, and the AI system notes that when combined at a certain dosage within patients between the ages of 20 and 35, the side effects of one of the medications are worsened, and in 10% of patients who opted in to the study, the side effects were severe enough to require medical intervention.

Using the information from patient’s digital health records, the AI combs through billions of data points about demographics, pre-existing conditions, contraindications, health vitals, and more to help paint a more complete picture of how these two drugs interact within the population.

The doctor compiles the results of the research over the course of several months and publishes a paper outlining the interactions on a biomolecular level. After publication, AI health systems around the world update their models of drug interactions and patient health to look for the need for intervention. A separate pharmaceutical AI also updates its model on drug interactions and begins running simulations on alterations to the drug formulas in order to prevent future interactions. After 8 months of running simulations, the pharmaceutical AI has a new compound that is ready for testing in animal trials. Two years later, the drug is ready for clinical trials and the side effects have been effectively eliminated thanks to the studies on drug interactions completed nearly three years ago.

Big Data & Big Infrastructure

The Chicago Transit Authority launches a new intelligent management system to recommend changes, updates, and manage day-to-day systems for the elevated mass transit rail system. The AI also manages the bus routes and broader city rapid transit lines. On any given day, the system makes adjustments to train speeds, bus routes, and arrival and departure times to maximize the utility for the most people.

A young father wakes up with his children and looks at the TV, where the smart home has projected the morning commute to both his children’s elementary school and daycare, and his work. A particularly impactful winter storm has blocked several bus routes before the city’s transport AI could deploy enough snow plows, so his commute has adjusted automatically to account for potential delays. Rather than taking the Blue line four stops and then transferring to a bus, the map on the TV recommends taking the Blue line for six stops, transferring to the Pink line for two stops, and then completing the commute via Bus. The new adjustment for the commute not only applies to this man and his children, but to hundreds of other Chicagoans in his neighborhood. The city’s transportation AI recommends to various neighborhoods individual and tailored adjustments for the morning commutes to schools, daycares, and offices, and as a result of the adjustments, there is effectively no delay.

The city’s AI system also notes that one of the trains on the Green line has a break-heating alert, indicating either the degradation of the brakes, or some other malfunction with the train. The system automatically re-routes the train off of the Green line, and to a maintenance line where it drives itself to a facility to be inspected by CTA officials and maintenance personnel.

The CTA’s AI system makes thousands of small adjustments to train speeds and routes, ensuring that the trains arrive precisely on time at every station, and maximizing the throughput of passengers throughout the city. Each train and bus is self driving, using data from passenger’s phones to determine capacity, and using city traffic cameras to determine best routes and speeds needed to maintain efficiency. Within the first year of implementing the system, the city’s overall revenue increased as a direct result of more people being able to access transportation into and out of the metro area. The increased revenue and savings made from the system allowed the city to make all public transit free for everyone, increasing revenue even further. The CTA’s AI system effectively paid for itself within the first five years.