Data Symme: A New Way to Monetize Your Data

• The traditional Web 2.0 exchange of data for access to digital services has unknowingly turned consumers into marketable products.
• Fraudsters are using legitimate datasets gathered from the big social, marketing and ecommerce platforms to correlate stolen data.
• There is an imbalance in data accessibility between two entities, where the steward of the data is able to unlock more value than the contributor.

Data Intermediaries

Data intermediaries, or middlemen, are mediators between those who make their data available (you), and those who want to leverage that data for profit (companies). They govern your data and make it accessible while convincing you they can be trusted with your personal information. Examples include Google, Facebook, Instagram, Tinder, Uber, Strava, PayPal and WhatsApp. Data is also being collected beyond just details and behaviour through facial recognition and voice messaging.

The Exchange of Services

You wouldn’t just give anyone your personal data – there needs to be an exchange of services in order for this relationship to work. Typically this involves you giving away your data in return for access to digital services which often results in tailored advertising specifically targeted towards you.

Data Misuse

Unfortunately not all use-cases involving intermediary data are honest ones; fraudsters use legitimate datasets from social media platforms to correlate stolen sets of information sold on the dark web which allows them access to sensitive details such as address information associated with pictures posted publicly on Instagram.

Web3 Disruptions

In response to these issues surrounding our current handling of user-data Web3 developers have been making moves that put control back into the hands of users through initiatives such as Self Sovereign ID’s and Data Unions. However these solutions remain siloed so real control requires further changes.

Data Asymmetry

Another concern for Web3 developers is Data Asymmetry – where one entity has more accessibility over a dataset than another – often resulting in unfair exchanges where the steward unlocks more value than the contributor does from their own personal information.