Inside the Insight Economy

Never mind the data economy. Data is unfit to form a proper marketplace: copyable goods cause an infinite supply that leaves a deeply confused demand. Meaningful trade only happens when two sides believe and expect they both stand to gain from every exchange. With data, it feels like some are always giving and others always taking. Is that because a broken marketplace makes trade fundamentally impossible? Yes. But this post is not a plea for giving up; it’s a focused manifesto for doing better. What marketplace can we build where data benefits everyone through trade? The question carries the answer: don’t exchange raw data. Refine each data point into a tailor-made asset that loses purpose when copied, yet delivers value when traded. Change starts from the essential belief that healthy data flows can bring more opportunities for improvement than harm.

12 August 2025

Quid? Pro quo!

Nobody wants to share data. We lack any intrinsic desire to do so, and that fact shouldn’t surprise anyone.

Nobody wants to share money either. It just so happens I value pizza over money. And fortunately, the Delfour supermarket prioritizes differently: they rather take my payment than keep the frozen pizza. So it’s not that I fundamentally want to share money; it’s merely an efficient way of achieving my goal. And the supermarket’s, at the same time.

Nobody wants to share food. It just so happens that I value friendship over pizza, and pizza is a great way of connecting and investing in our relationship. It’s not the act of sharing food that drives me, but the anticipation of us sharing a nice moment together, which pizza supports.

So why would data be the exception?

We all know trade is not based on goodwill or charity. Already in 1776, Adam Smith observed in The Wealth of Nations that we expect neither the butcher, the brewer, nor the baker to act from benevolence but—like us—from self-interest. The Delfour supermarket and I both end up better off because of the transaction. I’d never share my money with them, like they’d never share pizza with me—and that’s okay. We don’t have to be friends to support each other’s goals; in fact, it’s better that way, because it doesn’t restrict our friend circle to those who make great pizza.

Why do businesses ask if people want to share data,
while they never ask if people want to share money?

People never just share. Economics always presumes an underlying motivation, a deeper goal that we’re actually trying to achieve or an incentive that pushes us. The data exchange is merely a means to an end.

So why do businesses keep asking whether people want to share data with them? They know it can’t possibly be something we want. They don’t ask anyone whether they want to share money either! Nobody does—and that’s okay. An impulse to share was never a precondition to trade. People still spend money, and in return, they receive a product or service.

So is it possible that people and companies are stuck when it comes to data because we collectively haven’t figured out the trade aspect? Or worse, is data a fundamentally untradable good that we should stop trying to exchange? Yes, and yes.

In outlining an alternative, I will advocate for companies as much as for people, which may cast doubt on my societal intentions. Yet my commitment stands firm: in today’s economic system, we can only do right by people in the long term if we also do right by companies (which really are groups of people). Trade has been sustainable for hundreds of years because of mutual benefit. Shoppers and supermarkets both need a place in the data world, like in any economy—or there would be none. If we don’t identify benefit for all involved, we’re literally better off not trading.

While a more exclusive striving for consumer rights might evoke a sensation of righteousness or even fairness, a zero-sum game without losers isn’t the same as a trade in which we all can win. In a zero-sum world, I get to keep my money but the supermarket holds on to its pizza; never mind that we both stand to gain from trade. I equally don’t want to end up in a world where supermarkets get unbridled access to the 5 coins of small change in my pocket, while I eat their stale bread. Which—if you think about it—is exactly the world we’re in today, where data works for virtually no one. Whether you’re a company or a person, today’s vain attempt at data trade hurts us all. So why is everyone still at the market?

No more David than Goliath

Whenever I suspect something’s wrong with our frame of reference, I try flipping things upside down. So instead of endlessly wondering:

Do I really want to share my data?

I decided to put a slightly more absurd idea to the test:

Do companies really want my data?

A couple of months ago, I was offered the unique opportunity to pitch my case to executives from several major retail chains. During my 5-minute slot, this is what I told them word for word.

Please write it down

Hi, my name is Ruben. I’m a professor of computer science, a data technology consultant, and a parent of two kids. But I didn’t need to tell you that: we already know each other. Because although we haven’t met before, we have a pre-existing relationship. A quite elaborate relationship, actually: I go to your supermarket to make decisions about food as I walk around and self-scan the products I’m picking.

Despite our longstanding relationship, the media love to cast us as enemies. I’m David, you’re Goliath. You’re trying to take my data away, I’m trying to keep it close. Your giant hands are trying to snatch it from my tiny fingers, or to secretly steal it from behind my back. That way, you can use it, abuse it, store it, sell it, share it, use it against me. They say that you want to have my data, and that I don’t want you to.

I don’t think that’s true. In fact, it’s quite the opposite.

While I have you all here, in this room, I want to share a piece of data with each of you. It’s not just any piece of data—it’s one that really matters. This data is so important to me, that I want you to write it down. Please, take your notebooks and write it right there, at the top. Ready? Here it comes.

My son has got diabetes.

I want you to have it. I want you to write down this piece of data, because I need you to remember it. I’m not giving it to you to be nice or because I owe you anything. I’m giving it, so you can help me not accidentally hurt or poison my boy. I’m giving it to you for my personal benefit, and mine only. This data lets you point me to the right products when I’m at your supermarket, and I’ll buy them. So it should be a no-brainer: here you go.

You’ll want to accept this data, right? It brings you value: it lets you nudge me toward specialized products with lower sugar content. Those tend to be the more expensive ones with a larger profit margin. Good for you; and that’s fine with me.

Please, write it down, and keep it. I want you to have it. You’ll sell more expensive products, which I’ll gladly buy to keep him healthy. We both win—and so does he.

So, what do you say?

Don’t you want it?

They didn’t say much, nor did they need to. Seasoned executives are wise people, so they immediately sensed a conundrum: somehow, we got this all wrong.

Every colleague they’d spoken to, every company lawyer they’d ever consulted, retells the media’s age-old David and Goliath story: no consumer ever voluntarily releases their data, and GDPR made the grab-and-go model legally tricky. That’s why many B2C businesses tend to focus more on cohort data now, as they experience first-hand the increasing slowdown of individual consumer data. Our current notion of a data-driven economy suffers from an irreparable fracture right at the fault line where business, law, and technology fail to meet.

The data economy is fractured at the fault line where business, law, and tech fail to meet.

Then I walk in, not withholding but freely offering my most personal data, practically begging them to take it. And they are not ready to receive. In front of their very eyes, they see a person who not only fully understands the consequences of giving, but someone whose persistent motivation to give is driven entirely by those exact same consequences.

In other words: they see a trader. And they realize they’re not ready to engage—because they had long dismissed any possibility of mutual benefit involving personal data.

They’re not confused by who I am, but by who they are in this interaction. A sinking realization hits me as I’m standing in front of those retail executives: the Big Data craze and its devastating aftermath has left them convinced they’re Goliath. They truly believe we’re each other’s worst enemy. So when I’m offering something they want, it rocks and shatters one of their most fundamental assumptions.

Of all societal crimes committed by Meta and friends, I find this one by far the worst: Big Tech stole our imagination. They robbed us of the belief that genuine win-wins with data exist and, hence, that trade harbors real opportunities for all of us. Retailers unironically forgot that mutual benefit was the reason why the Delfour supermarket and I started our relationship in the first place. You want my money, I want your pizza. Deal! And we’ll both search for ways to get the most from our various relationships, be it cheaper options or a wider selection.

But for some reason, when we add data into the mix, our formerly beneficial relationship morphs into a hostile situationship of grudging enemies. The diabetes example demonstrates some raw data is so deeply toxic, no sane company would dare touch it with a ten-foot pole. Retailers aren’t ready to receive this data point, because:

• It’s medical data, a special category under GDPR for which we can’t simply consent.
• Even if I could consent, this data isn’t about me, so the consent isn’t mine to give!

Supermarkets aren’t equipped to store medical data, nor should or will they ever be, as the liability cost is prohibitive. The fact that my son has diabetes is data their own DPO will never let them have, no matter how promising or mutual the benefit. How can we trade when my asset that generates value is the raw data you can’t touch?

What you really, really want

Let me tell you

Businesses will decidedly waste time and effort to determine whether I’m pregnant, and assess with 67% certainty I have 3 kids and 5 cats (one of which is 43% pregnant too). From there, some undoubtedly clever AI algorithm will conclude that I need cat food, diapers, and two different kinds of breast pump. Plus a blue grand piano, paired with a vintage orange synthesizer—you know, that pretty one I already bought 2 months ago. Or at least, that seems to be their conclusion based on what they’re trying to sell me. Unsure who’s making money here, but I bet it’s OpenAI.

This farce began with the audacity to ask me yet again:

Do you want to share your data with us?

Well no, because apparently you’re terrible at it. It costs me and it costs you.

But why even bother? Stop guessing whether I’m pregnant or dieting because my sugar habits have evolved. I’ll bloody tell you. I’ll say exactly what I need—no more, no less. Or better yet, I’ll tell you the thing that helps you match something you sell to my needs. My phone could happily look up any key pieces of information, so you can skip building an app that scrapes through irrelevant scraps of my data. When my device knows my needs, you can sell me the biscuits of my dreams while I’m asleep.

Unfortunately, none of that will happen until the Delfour supermarket and I solve the mystery of Schrödinger’s data vault. Raw data is too toxic for anyone to touch, yet unleashes huge value when we do. Not just once, but for every purchase I’ll ever make. How can this business simultaneously know about my son’s diabetes yet also very much not know anything about him? Well, imagine a third path where they don’t have to.

Real value lies outside the data

It struck me that companies merely think they want our data, because so far it’s been their only way to extract value. If they could obtain similar or greater value without receiving the data, such a path of least resistance would be a no-brainer. Why run the risk when the reward doesn’t depend on it? To cure this unproductive craving, we must dig deeper and find within the data something of more value than the raw data itself:

What does Delfour really want from its relationship with me?

For me to spend more so their profits grow.

What do I really want from my relationship with Delfour?

To buy products that make my family happy.

What facilitates an alignment between those two?

Smart matchmaking between the products they offer and those I want to buy.

You see, my B2C relationship with Delfour revolves around key interactions while I’m walking around their supermarket. I’m usually wielding one of their handheld self-scanning devices, hunting for barcodes so I can Scan & Go without queueing at the end. To get more value out of our relationship, Delfour neither needs nor wants to know:

Ruben’s son has diabetes.

What helps them drive better sales is something that changes my shopping behavior, a focused insight derived from the original raw data:

As Ruben picks up a handheld self-scanner at the Delfour entrance today, reserve some space at the top of its screen. Whenever Ruben scans one of our products, fill that space with a banner where he can track the product’s sugar content in grams per serving. For high-sugar products, suggest where he can pick up an alternative.

Delfour doesn’t care why I’m giving them this insight. Maybe I’m pregnant, maybe I’m curious about food science, on a diet, or visiting my aunt who dislikes the sugary stuff. The underlying cause or reason doesn’t matter to Delfour since the consequence is the same: I’m buying the more expensive product, which they want to sell and I want to buy. Similarly, they don’t need to care about how I obtain the income to buy frozen pizzas. As long as the insights flow and my card goes through, meaningful trade can happen.

But surely they could still use Big Data analytics with AI to figure out with high confidence the real reason I’m avoiding sugar? Of course they can. But what benefit could they possibly gain from taking that risk? Now they’re stuck with a toxic medical data point, while they could easily receive its insights instead. Where ignorance is bliss, ‘tis folly to be wise. This mechanism forms the heart of the Insight Economy I envisage: meaningful insights, not risky raw data. Thereby, we vastly increase the value we extract from personal data, without the cost and risk of touching or storing it. The incentives for data hoarding plummet to zero.

We’re worth more to companies as traders than we’ll ever be as data subjects.

So don’t aim to obtain sensitive data by guessing that I’m pregnant, then speculating about all the subtle ways this might influence my purchasing pattern. If only you let me, I will directly and specifically tell how you can make more money off of me! I’m worth much more to you as a trader than I’ll ever be as a data subject.

The Trinity of the Insight Economy

The hen with the golden omelets

Let’s stop pretending we can fix the personal data economy: copyable assets cannot sustain mutual benefit. My vision for an Insight Economy is one where we exclusively trade unique and specific insights rather than the raw data that generates their value.

Avoid raw data like raw chicken. Actually, see raw data as a hen that lays golden eggs. If you sell the magic hen, you’ll only make one sale. Besides, who in their right mind wants to touch it? Don’t sell the eggs either, who knows what may come out of those… Instead, let the chicken cook daily omelets to taste, then sell those. Soon you’ll have an eager queue of unsuspecting buyers, oblivious to how their omelet is made. Buyers are happy, because they never could have made such a great omelet from the chicken or its eggs alone. And you’re happy, because the hen generates new tradable assets every day.

Tailor each insight to maximize value for a specific recipient, context and time: turn generic and non-urgent cold data into a hot insight that delivers peak benefit when consumed right away. Having chronic diabetes is cold data; shopping for low-sugar products at Delfour today has urgency and relevance. In absence of situational urgency, the envelope accompanying an insight can impose legal restrictions on shelf life and usage, increasing the insight’s value as a tradable good for everyone involved.

None of this physically prevents insights from being misused in different ways, just like I can’t prevent buyers from reselling leftovers of yesterday’s omelet. Yet such concerns become largely irrelevant when we make it more profitable to request a fresh insight specifically tailored to each new context. The underlying raw data point (diabetes) that gave rise to my Delfour preferences today (low sugar), generates different insights in different contexts (shopping for an office party, at the pharmacy, at a music festival…). Storing hot insights for later turns them into cold data again. Don’t settle for scraps.

Aligning technology, law, and business

The paradox is that technology for exposing less raw data, reveals more of its value. Inside the Insight Economy, personal data technology and legal constraints do not oppose business drivers: technology, law, and business enable each other’s goals.

Instead of trying to make the old raw data flow, we turn the data into a unique and specific insight every time, instantly creating an on-demand supply with economic value. By contrast, raw data is a copyable good that spawns an infinite supply, and thereby immediately spoils the moment it enters a marketplace. Even though unique insights are technically still data, they don’t retain their economic value when copied because this value depends on a specific context and time. Sure, the pharmacy could peek at the insight that I’m tracking sugar contents at Delfour, but how will that lead to a sale for the precise meds my son needs? Better to request a fresh insight.

The unifying model below recognizes that economic incentives and legal considerations aren’t afterthoughts to technical data processing. On the contrary, the Insight Economy unlocks meaningful trade in a data-driven world by equating technological, legal, and business value, all at the same time. They’re not different kinds of value, but three core perspectives on insight-driven value creation.

We can characterize a successful refinement of raw data into an insight by a stable equilibrium in which derivation equals minimization equals activation:

From a technology perspective

AI and algorithms provide safe derivation of raw data at a secure location, refining them into unique insights.

From a legal perspective

Sensitive personal data undergoes minimization such that the resulting insight carries a lower risk, potentially even classifying as legitimate interest.

From a business perspective

Raw data strips sender and recipient of their bargaining power after at most one transaction. Unique insights enable activation of the raw data’s value.

From raw data to unique insight

To unlock the potential value of raw data points, each of the three perspectives invites us to consider a different viability test:

Possibility

What technologies can derive which insights from this data?

Feasibility

What constraints minimize the legal compliance burden for this data?

Viability

What business value can we activate through insights from this data?

Let’s exemplify the interaction between the three perspectives with the case of the Delfour supermarket and my son’s diabetes.

The derivation of this particular insight from the sensitive raw data simultaneously creates new value from each of the perspectives:

From a technology perspective

The data point my son has diabetes triggers a set of domain rules together with my other data points. The final insight is the result of algorithmic refinement specific to the Delfour supermarket at a certain moment in time. Thereby, the technical derivation turns generic data into an immediately actionable insight.

From a legal perspective

The GDPR’s Article 9 classifies this raw data point as special category data, necessitating an additional legal basis. The insight achieves an extensive minimization through a momentary preference restricted to the Delfour supermarket. Whereas raw data access would require consent and/or a valid medical circumstance, the resulting insight could qualify as legitimate interest and thus not need manual confirmation for every exchange. Technologies like personal data pods and trust envelopes let us trade insights without handing over raw data.

From a business perspective

Although content-wise a small leap from sensitive personal data Delfour already collects about me, the diabetes data point possesses far larger potential value in comparison. Due to its medical nature, the associated cost and risk are also disproportionately higher. On-demand actionable insights provide direct activation of commercial value without the associated pitfalls.

By creating such an alignment, each perspective strengthens the other two. No longer a blocker to business, legal minimization enables obtaining benefits from raw data without the associated risks of its actual access or storage. Legislation highlights noteworthy areas within the insight space; technology can explore their boundaries to ensure inputs and outputs of algorithms experience minimal friction from and to the real world. In turn, technology informs law and business with the art of the possible, providing a traceable path of where raw data ends and unique insights begin.

The future we couldn’t see in the past

No more crumbs

I imagine by now, the 2012 teen who fell victim to the infamous data mishap of Target allegedly spotting her pregnancy ahead of her parents has grown into a data-conscious adult with healthy offspring. The same cannot be said about those companies. Any and all judgment is reserved for Target—and frankly every business—for remaining stuck today in the state of the art from over a decade ago. We’re still mining data like it’s 1999, even though punk is dead and most data lakes have dried up as a result of overfishing.

It might have been how we saw the future in a distant past. But there’s no more future in there today. GDPR and related legislation have not only made “doing wrong things with data” more expensive, but even “doing right things”. Whichever those may be, the bottom line is that companies burn bigger budgets on even shoddier guesswork.

Without a sustainable alternative, we’ll continue to waste money hoovering up whatever data crumbs others have left for anyone to find. As 2025’s Hansel and Gretel cross the forest to the friendly gingerbread store, the old shopkeeper is too busy to talk to them. She’s hyperfocused on combing measly crumbs for any clues to increase her CRM’s probability of whether grandmother prefers new glasses over a wildlife magazine subscription. Meanwhile, the kids would’ve gladly mentioned that mee-maw loves those soft round biscuits. Easy on the sugar, please—and trust us, you don’t want to ask why.

Unique insights, unique value

The past decade, collective finger-wagging performances failed to generate enough wind to move companies a single millimeter. That’s because businesses don’t run on wind but on profit, as part of their societal duty towards shareholders. While we disagree with noble intentions, consumerist activism hardly slows down the broken data economy and instead prolongs its expensive demise. While I applaud any initiatives to transition into a just socio-economic system, in the meantime, waiting is not our only or best option. A pragmatic and constructive approach is to work with the existing economic system—whether we like it or not—to start building the healthy marketplace they want to see. Playing the trade game by the rules is how all of us can finally win.

While we’re at it, can we stop scaring people with the threat of their own data? Let’s empower them: what value do you want from your data? Encourage people to start trading; in a free society, everyone has the right to fair participation in the economy.

There should of course be laws that protect us. Again, that’s how the current economy functions. But we need to accept that such laws are not prescriptive. In most places, the law prevents me from selling my kidney. Yet it doesn’t tell me what I ought to be selling instead, nor should it. Similarly, GDPR and other legislation set boundaries based on what we don’t want to happen with our data. But laws don’t suggest or limit all the ways in which our data can support us. They indicate the lines; coloring them in is up to us.

When I say the Insight Economy should emphasize people as traders of insights, I don’t necessarily mean people should sell those for money. No one is stopping them either; I just think it’s one of the most boring things we could possibly do with our data. In my case, I’m not selling insights from my son’s diabetes. How much would I earn—10 cents per access? That’s worthless compared to direct causal value, which brings benefit derived from the data itself: make my data work for me! Insights are specialized, on-demand products; there’s little incentive to sell them by the pound.

I remember in the 90s, my mom used to deliberately misspell her last name when she’d sign up for supermarket loyalty programs. So when she’d receive junk mail, the offender would openly reveal themselves right on the first line of the envelope. In a way, she nailed back then what took us too many decades of Big Data to understand: unicity is crucial to trade in a data-driven world.

We’ve tried trading raw data and ended up hurting everyone. That’s where the data economy failed and always will, because raw data immediately poisons any market. Let’s skip ahead into the Insight Economy by trading something much more powerful: purpose-led insights make people and companies thrive through a declared expectation of mutual benefit. The hen with the golden eggs doesn’t exist to be sold. When she cooks fresh omelets to taste, no one eats anyone else’s anymore. I’m keeping the hen.