![]() ![]() This anti-fraud solution looks to tackle the problems caused by how we handle data today by giving control of identifiable data back to the user and making personal data and communication part of identity. Perhaps the best way to explore this potential future is by looking through the lens of Self. ” What Does a World of Data Without Intermediaries Look Like? ![]() The current imbalance between the amount of data about individuals held by or accessible to institutions, and the inability of those same individuals to control the use of that data has created an asymmetry of power, resulting in a crisis of trust. Paul Mitchell, Senior Director of Technology Policy at Microsoft, who predicted the future of data symmetry (pre-Web3) back in 2014, said “ Data-driven economies are reliant on a dependable supply of data to be sustainable. When the data and services exchange becomes equal and both parties receive a balance of value, we will have achieved data symmetry. Instead, for access to your information, they’ll pay you directly. Web3 is decentralising data so that a world in which organisations no longer buy and sell aggregated data becomes a reality. Why are we not profiting and being rewarded? Is access to a service a fair exchange? Web3 says it isn’t. ![]() Google now has a lot of aggregated data that they can sell to third parties, typically for marketing and advertising purposes.Īs we become more aware of how our data is collected and sold, for many of us, the result is frustration. Aggregated over time, this data shows them when traffic surges, where the biggest bottlenecks are, and the general flow of cars at different times of the day. Google now knows how many cars are in the area, how bad the traffic is, and where you are all going. You use Google Maps for directions and location services to get from point A to point B. The lack of fairness in this exchange is a key concern for Web3 developers. Essentially, the steward of the data is able to unlock more value than the contributor. The Current Asymmetrical Face of Dataįor those unfamiliar with the concept of data asymmetry, in layman’s terms this is where there is a data accessibility disparity between two entities. But like the platforms of Web2.0 these things are still still too siloed. We’re seeing disruptions that change the role of data intermediaries and put the power and control back into the hands of the user in the form of things like data unions and Self Sovereign ID. Web3 isn’t happy about the old deal, and for good reason. Our data is valuable and often nowhere near as benign as we think when it’s in the hands of a bad actor. That allows them for example to know they have the right address for you from the location data on the cat pictures you post publicly to instagram. ![]() Fraudsters use legitimate datasets gathered from the big social, marketing and ecommerce platforms to correlate the sets of data stolen by hackers and sold on the dark web. Intermediary data isn’t always used honestly either. Let it not be forgotten that the collection of your data goes beyond your details and behaviour, but also includes information from facial recognition and voice messaging. The traditional Web 2.0 exchange of your data in exchange for access to digital services has often unknowingly turned the consumer into a marketable product. The more you think about how many online services and apps hold your personal data, the more you consider how much of the advertising you see has been tailored specifically to you. Some data intermediaries you’ve likely already given your information to include Google, Facebook, Instagram, Tinder, Uber, Strava, PayPal and WhatsApp. Of course, you wouldn’t just give anyone your personal data, so there needs to be an exchange of services. They govern your data, chop it up into data sets, and sell it or make it accessible, all while convincing you that they can be confidently trusted to take care of that data. These intermediaries, or middlemen, are the mediator between those who make their data available (you), and those who want to leverage that data for profit (companies). ![]()
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