Data is the new oil or the new gold, pundits proclaim, hailing its transformative potential in the economy and within business models. However, a critical piece seems to be missing in the conversation. In quite a few cases, this data is being harvested with little concern for ethics, while those generating it in the first place are not fully aware of its collection. 

Even though data disclaimers do appear in fine print, most users skim over them or don’t fully comprehend what they are agreeing to. The situation becomes even more unfair when large corporations profit from data harvesting without any compensation to the consumer. 

While legislative efforts like the GDPR take measures to curtail the march of data capitalism, the business model requires major adjustments. Users need to be given the reins when it comes to data collection with the freedom to choose what data they want to sell, if any at all. 

Some in the crypto community, which is known for its passion for privacy, have come up with a solution: tokenizing sensitive data. Tokenization, in crypto terms, means creating a token representing a specific asset on-chain. In this case, such a token would represent the data describing our lives, the kind that marketers are very much after. Users would own the token, free to sell it to advertisers if they wish. Is this the silver bullet that brings us privacy, though?

Your data is not really yours

Even though Web2 companies often explicitly state in the user agreement that they will share your data, it’s pretty common for customers not to read the fine print. So let’s say our user, Mary, didn’t bother herself with that when she downloaded a retailer’s app to browse footwear. She went to the store, bought the shoes she liked and when she returned home and opened her web browser, it bombarded her with ads displaying similar footwear. 

What Mary didn’t know was that the retailer sent her data to advertisers. The advertisers then proceeded to flood her Facebook and Google with relevant ads. Although the companies technically asked permission, Mary wasn’t aware her data was being used. Even so, big companies used her data to try to facilitate another sale. 

Now, let’s take another hypothetical scenario, where Mary actually did read the fine print and chose not to approve the user agreement. In this case, she likely wouldn’t have been able to use the app at all, which makes for questionable freedom of choice. On the other hand, if she knowingly consented to share her data, it still seems ludicrous that she had to sacrifice her privacy for the convenience of using the app. 

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In a hypothetical scenario where Web3 prevailed, things would look a bit different. Sure, Mary browsed the shoes on the app, which makes for a neat little pool of behavioral data. Furthermore, her geolocation data throughout the tour to the brick-and-mortar outlet makes for another piece of the pie an advertiser would be interested in. But none of that made it further than Mary’s own wallet, which now holds tokens representing that data. 

Any advertiser looking to access that data, likely properly anonymized, would have to purchase the tokens from Mary first. Mary would therefore be able to monetize what is rightfully hers, which would make data tokenization much fairer. 

The flaws in the tokenization model

While data tokenization seems to be one of the few ways to make data harvesting fair, the model has flaws. First, much like with a terms-and-conditions agreement when installing new software, opting against selling your data could result in a sub-par user experience and even exclusion from the platform. It’s a Catch-22. 

Consumers don’t want to share their data, but they need to consent to share their information to experience the full services. Otherwise, with data unshared, it’s a lose-lose where the consumer gets irrelevant ads and advertisers don’t get the bang for their buck. 

Even with this major issue, it all boils down to one thing: choice. Giving consumers the choice to opt-in or not is key to making data harvesting fairer, even if it comes at a cost. Because, even if they opt out and receive a worse experience, it comes as a consequence of them exercising their free will. 

Ecosystems like DTSocialize empower users to share their data for profit. The data is then anonymized and assembled into big data stacks for market research. This makes data tokenization fair again, by giving the user the power to choose and make money from their data. 

Of course, there is another disparity here between the wealthier users who can afford to give up on the extra income from their data and those who can’t. 

Let’s say Mary works a minimum wage job, but her friend Debra is a wealthy businesswoman. Debra can afford to opt out of sharing her data, but Mary, who is living paycheck to paycheck, cannot. While Mary may share her data to make extra money, Debra will not, which will invariably skew the dataset. This ultimately creates a digital divide, where the wealthy can afford to opt out of data sharing, while the poor will share. 

There’s never a perfect solution but…

While there is no perfect solution to the issue, in order to make data harvesting fair, the power needs to be given back to the user. In a more just world, users have the right to choose what they are sharing and what they are not. Users also have the right to know what their data is being used for. 

Giving users the power to monetize their data shouldn’t be an exception to the rule, but the rule itself. Data tokenization can only be fair when the users are making the choices, not big companies with vested interests.

Tomer Warschauer Nuni is the chief marketing officer of Kryptomon and a serial entrepreneur and investor focused on the innovative blockchain gaming industry.

This article was published through Cointelegraph Innovation Circle, a vetted organization of senior executives and experts in the blockchain technology industry who are building the future through the power of connections, collaboration and thought leadership. Opinions expressed do not necessarily reflect those of Cointelegraph.

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