How much is your data worth?

Written By Rachel Abraham

I was running 9 minutes late on Sunday, October 6th, 2024. My Apple Watch knew I should’ve been out the door already. “It’ll take 27 minutes to get to . . .” it warned. There’s little Apple doesn’t know or couldn’t find out about me. A lot of my data grows in their walled garden. Like many people, most of the time I don’t mind. The value I get from the convenience is meaningful, but, as someone who’s worked in the data space, my comfort comes more from the knowledge that a lot of what I value isn’t tracked or quantified by most companies—at least not yet.

Invisible even to Apple was the thing making me late: narrative clarity. I’d been working for hours on a presentation titled “How much is your data worth?” for a talk I was about to give at my local Universalist Unitarian church. But, as I washed my face at the 5-minutes-late mark, it finally hit me. I’d built a deck tailored to my audience of mostly tenured or retired university professors and their progressive neighbors, but they weren’t the audience I could answer the question with. They’re smart and aware of data collection in the 21st century, but there weren’t any economists among them.

My deck included all the points I’d outlined to the organizer in my proposal, but it didn’t calculate the literal answer to how much data is worth because it couldn’t—I couldn’t—at least not in a couple of hours or the 30 minutes I had to present. And especially not when I was already running late.

Drying my face and rush-brushing my teeth, my mind raced trying to figure out how I would calculate the value of consumer data. The International Data Corporation valued the data market at $274 billion 2 years ago, but that hardly felt sufficient. Discontent with the outdated answer, I flipped off the vanity light and hurried to my car.

I spent the drive considering the familiar silver-haired folks who would fill the room and Zoom and how I could summarize and facilitate an engaging discussion with the materials and information I’d already prepared. I was relieved that it was well attended and that everyone in the room raised their hand at least once to ask a question or inject their thoughts. You can find the deck here.

How would I calculate the value of our data?

I believe a quantum computer would have a difficult time answering how much our data is worth in 2024—mostly because the economic factors involved are too big to be reliably estimated and boiled down to an individual level. Even answering subordinate questions like, “how much does it cost to store consumer data?” reveal unanswered questions about the technological cost to keep them secure and the environmental costs to sustain them.

If we want to build a formula to calculate the value of consumer data, we need to consider everything companies invest in data, including but not limited to its storage, collection, and analysis. I would posit that a powerful multiplying factor of this formula is the present market’s reliance on and preference for data-based decisioning.

Oil is a poor comparison for data.

Double entendre intended. Oil wells run dry. Data, captured and uncaptured, seems (to me) more abundant than fresh water. In just 60 seconds, people send 41.6 million messages on WhatsApp; watch 43 years of content, and search 6.3 million things on Google (check out Domo’s Data Never Sleeps report to learn more). The average person interacts with their phone 58 times a day, and these interactions create a trail of data that doesn’t die with their current phone. Each day, the world generates 2.5 quintillion bytes of data.

Although, I understand why media and analysts have drawn the comparison. Unlike fresh water, data is not being pumped from aquifers into micro-plastic-bleeding bottles destined for the landfill. However, some first-party data collectors fill forgotten spreadsheets with data that ages into history.

To me, data is as renewable as human creativity and technological innovation. Even old data has longitudinal value over time, and technology is getting better at preserving it long-term. Plus, data can be reused at the same time it is consumed. For example, the data Apple collects from my iPhone informs not only Apple but also their data partners or customers simultaneously. While oil has permeated every sector of the economy, like data, it is less-abundant, non-renewable, and less-recyclable than data. And there’s one more crucial distinction that my decision-obsessed mind has to address and that’s . . .

How is our data used?

Simulation theorists might argue that everything uses data. Everything is data. And, fair. Data is used to inform our decisions (which I argue is central to the creation of the universe). It’s used to run our government, economy, technology, and the entirety of the personal lives we build with our phones.

Data is used by world and business leaders to make better decisions.

Making the right choice is a pain point for the world’s most powerful and wealthiest leaders. Thus emerges the decision science industry complete with undergraduate to PhD degrees, above-average salaries, and “decision scientist” job titles at behemoths like Starbucks and Meta. But what is decision science? According to Harvard University, “Decision science is uniquely concerned with making optimal choices based on available information.” Available information begins with data collected about a consumer, citizen, employee, or competitor, etc. Leaders lean on mathematical, data-based decision science to help them predict outcomes and make the best choice possible. 

“According to a McKinsey survey of more than 1,200 global business leaders, inefficient decision making costs a typical Fortune 500 company 530,000 days of managers’ time each year, equivalent to about $250 million in annual wages.”

Society is increasingly trying to remove human biases from decision making for reasons ranging from outsourcing personal-professional liability to improving diversity, equity, and inclusion. But, one of the most sought after insights from decision scientists are the unimaginable forecasts or opportunities that data and tandem advancements in quantum computing and AI might reveal. Decoupling human intuition from decision making relies on exponentially growing datasets to make ever-better, quantifiably-reasonable decisions. However, data collection cyclically relies on human intuition to decide which data points are worth prioritization, consideration, and collection, so the outcome of a calculated decision remains relatively bias until the computer (whether they be an AI program or human analyst) can access a broader swath of data than the inquiring leader can imagine.

The world’s wealthiest class of corporate and political leaders rely on data to inform their strategies. So, weighing data’s importance to this powerful, decision-making sector of the economy is a necessary component in calculating the value of our data as individuals.

Nevertheless, while the normative reliance on data continues and the efficacy of data-driven decisioning grows alongside emerging technologies like quantum computing and AI, decision-makers will choose to collect more and better data to make scientific, economic, political, and even personal decisions. The powerful tenor of elite leaders who rely on mass quantities of data will continue to drive the growth of data collection while public concerns over data privacy remain unresolved.

Perhaps a savvy decision scientist and equally capable AI could help us make the right decision about how to preserve privacy while empowering better leadership?

Data powers the economy through marketing and attention grabbing.

The economy runs on data—no matter the form of economy. In the United States, most of us are aware that our data is used by companies to create targeted advertisements and increase the amount of time and money we spend on them. For time, let’s just look at 4 ways data is used to illustrate the quality and quantity of data we each produce.

Your data personalizes your ads.

Most of us know that when we google ‘Subaru dealership near me’ we’re likely to see car ads later in the day. How persistent these ads are depends on companies’ ad spend budgets and how well you fit the criteria of their target audience. For example, the local car dealer whose ads you see most may pay more to increase the number of times you see their ad because they leverage a dataset to target people in your geographic location and your income bracket.

Once mostly based on demographic information such as your sex, age, income, and geographic location, personalized ads now encompass more complex data points such as your online behaviors, outlook, and values.

  • Compulsory data collection

  • Thousands of attributes

Check out your Google dashboard to see what kind of data they collect about you to help marketers reach you with targeted advertisements.

Your data builds digital experiences one A/B test at a time.

Websites, apps, and platforms are constantly running what are called A/B tests to improve your experience as a user and improve their bottom line. You may not notice or think much of the slight change in the “Add to cart” button, but whether and how quickly you click on it are data points that companies use to incrementally increase sales. Similar tests are run by media platforms like YouTube, Netflix, Instagram, and X. The small changes that win more of your time and money prevail to become the new normal—at least for a while. While small changes of color, text, auto-play or auto-swipe might not seem important, when multiplied by millions of users, company gains are massive.

Your data builds predictive models.

Models are created and analyzed based on your data to predict how you will act based on how people with similar traits and behaviors have acted in similar circumstances. For example, let’s say you’re planning a Christmas vacation. You may not be aware that companies are tracking the volume of visitors travel sites are seeing and the amount people are saving instead of spending to estimate not only the volume of travelers over the holidays but also what their travel budget might be. Raw data like this is often supplemented with surveys and presented to businesses to develop products, services, and marketing campaigns to target consumer segments.

Similar customers are grouped into ‘segments’ before social or decision scientists analyze and model predictions. These segments are anonymized for privacy but can be reverse engineered to identify individuals. These models can also result in underhanded manipulation tactics such as surveillance pricing.

Government uses data to run surveillance.

The U.S. government uses citizens’ data for various purposes, including guiding Supreme Court decisions, enhancing national security, law enforcement, and public services, with agencies like the NSA collecting data for surveillance and intelligence gathering. Additionally, federal and local governments use data to improve public services, allocate resources, and monitor compliance with regulations. Meanwhile, authoritarian states and actors can use data to monitor citizens for compliance.

Data Privacy

Naturally many of these uses raise questions about the decisive disparity between informed data-owners and disenfranchised data producing consumers. In an attempt to clawback some of that control, consumers can turn to enhancing data and information privacy. Although, many of us don’t because apps, websites, and technologies don’t allow us to negotiate terms. Unless you count our relatively new ability to toggle certain cookie tracking settings on or off on some websites.

Regional data privacy laws govern whether and how data can be used—including cookies. The California Consumer Privacy Act (CCPA) and the EU’s General Data Protection Regulation (GDPR) are two of the world’s privacy benchmarks, but they fall short in some areas. Gramm-Leach-Bliley Act / CCPR / GDPR / Dobbs v. Jackson

The European Commission is proposing enhancements to Europeans’ data privacy with their Digital Decade campaign. China’s Personal Information Protection Law (PIPL) went into effect in 2021. Meanwhile privacy in the U.S. is a greater unknown concern in the post-Roe era. Anxious folks can always take steps to keep their data to themselves.

How to protect your data

Change your browser to DuckDuckGo.

DuckDuckGo's products are designed to protect users' privacy by not tracking their searches or browsing history, and by not saving their IP addresses or other identifying information.

Send and receive messages via Signal.

Signal is a free, open-source messaging app that focuses on privacy and security using end-to-end encryption. They’re endorsed by the Electronic Frontier Foundation (EFF), former NSA contractor Edward Snowden, and the European Commission.

Use a VPN.

A VPN, or virtual private network, is an online service that creates a secure connection between your device and a remote server. About 5% of people who don't currently use a VPN are considering getting one within the next year. In 2024, up to 10 million American adults could start using VPNs. I’ve used NordVPN and heard good things about ExpressVPN. Free VPNs are not likely to provide the same privacy benefits.

Vote with your dollar.

Spend money on services that use or protect your data the way you want. Donate to non-profits or campaigns that help you achieve your data and privacy goals. For example, you can give to organizations like Electronic Frontier Foundation or Future of Privacy Forum which advocate for better data privacy policies.

Vote with your voice and ballot.

Vote. Contact your representatives to tell them how you would like your data to be used or protected. Follow these bills on the hill in 2024: APRA / COPPA *passed!* / Online Privacy Act / Data Privacy Act of 2023

Resources:

What do you think your data is worth?

Do you feel you’re getting a fair trade?

Rachel Abraham
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SEO Content Writer