V2.0 updated 11 Sep 2025

Introduction

Markets have always been shaped by the ability to prove trust, transact efficiently, and match supply with demand. In today’s digital economy, data is central to these functions — yet most data sits siloed, inaccessible, and poorly structured for use in market design. The Dataswyft Wallet solves this problem by transforming raw, siloed data into Smart Data, enabling proofs (badges) that can be used across different markets.

The Dataswyft Wallet Story

Every badge in the Dataswyft Wallet begins as a template created by a trusted issuer — a university, government, or NGO. Think of it as an empty form: Student, Over 21, Volunteer.

A holder “claims” the badge by filling the template with their own data from a verified source (e.g. transcript, ID, service record). At that moment, the badge itself is the proof — cryptographically valid, fraud-resistant, and stored privately in the wallet.

From there, four markets emerge:

1. Audience Market
   • As more holders mint badges, audience counts build up (e.g. 20,000 Students).
   • Advertisers price access to these audiences without identity. They see “how many”, not “who”.
2. Proof Market
   • Market of counterparties that would benefit from digital proofs (and just the proofs) and pay for it.
3. Data Market
   • Some badges contain underlying content data (transcripts, certificates, hours volunteered).
   • When a holder chooses to share, validators and recruiters pay for access to this data inventory, already proven by the badge.
4. Insight Market
   • Issuers and partners never see who claimed or how badges are used. If they wish to have identifying information, they would have to obtain it separately as a validator/merchant
   • What they buy instead are aggregate insights across claims: adoption, overlaps, conversions.
   • This is the metadata layer — priced for intelligence rather than identity.

Because the badge is a triple double-faced label — template ↔ claim, claim ↔ offer, proof ↔ data — each market can inventory separately and at its own pace. Audiences can thicken fast, data demand can grow slower, and insights can be monetised even before the other markets mature.

Smart Data Schemes: Structuring Data for Markets

At the core of Dataswyft’s market design is the Smart Data Scheme (SDS).

• Definition: A Smart Data Scheme is a structured framework that defines how raw data is collected, standardised, and transformed into usable Smart Data.
• Function: It provides the rules of the game — who issues the data, how it is validated, and how it is accessed or shared.
• Outcome: By applying SDS, organisations create interoperable proofs that can be used in any Dataswyft-enabled market.

Example: A health-tech SDS might define the rules for storing fitness data (e.g., steps, heart rate, calories burned) so that it can be converted into badges for insurers, gyms, or wellness advertisers.