Synced Services #1
THINK-IN Vol. 1 outputs.
Synced Services. Group 1.
Following on from the first instalment of outputs from our inaugural Think-In event: Hyper-personalisation – How can we deliver seamless, personalised experiences without crossing the line on privacy and ethics? We’re now moving on to Synced Services – What does it take to truly integrate payments, transport, and digital services into one cohesive user experience?
Let’s get into it.
How do we enable the availability and quality of data across different industries and geographies who all want to be paid in order to derive new mobility services?
Enabling the availability and quality of data across industries and geographies—especially when stakeholders want to be paid for their data—requires a strategic approach. Here are three well-structured solutions:
1. Decentralised Data Marketplaces (Blockchain-Enabled)
💡 Concept: Establish a decentralised, blockchain-powered data marketplace where data providers can tokenize and sell access to their data under smart contracts.
🔗 How it Works:
Data providers (e.g., mobility operators, payment providers, and cities) list their datasets on a blockchain-based marketplace.
Smart contracts automate payments based on access, frequency of use, or value creation.
Buyers (e.g., startups, mobility platforms, and AI firms) purchase data under transparent and auditable conditions.
🛠 Key Tech: Blockchain, tokenisation, self-sovereign identity (SSI).
🎯 Strategic Impact: Fair revenue distribution, tamper-proof transactions, and scalable cross-border adoption.
2. Federated Learning with Privacy-Preserving Computation
💡 Concept: Instead of moving data across jurisdictions, use federated learning to train AI models directly at the data source without exposing sensitive information.
🔗 How it Works:
Data remains with the provider but contributes to AI models in a privacy-secure way.
Differential privacy and homomorphic encryption ensure that insights can be shared without exposing raw data.
Revenue models are based on “data insights as a service” rather than raw data sales.
🛠 Key Tech: Federated learning, homomorphic encryption, AI/ML.
🎯 Strategic Impact: Regulatory compliance (GDPR, CPRA), enhanced security, and a sustainable monetisation model.
3. Open Data Syndicates with Revenue-Sharing Models
💡 Concept: Create an industry-led, open-data syndicate where multiple stakeholders pool non-sensitive mobility data and agree on revenue-sharing mechanisms.
🔗 How it Works:
Members contribute standardised, non-sensitive mobility and payment data (e.g., aggregated transaction trends, anonymised vehicle movements).
A neutral entity governs data usage and ensures revenue-sharing aligns with contributions.
Premium tiers offer deeper analytics for paid access.
🛠 Key Tech: API standardisation, governance frameworks, revenue-sharing algorithms.
🎯 Strategic Impact: Industry-wide collaboration, reduced data silos, and monetisation without regulatory friction.
Next Step?
Which of these solutions best aligns with your strategy? 🚀 Let’s refine and implement it!
Answer Attendees: 2
LinkedIn Post: Unlocking Mobility Data Without Losing Control
💡 How do we make high-quality mobility data accessible across industries and geographies—while ensuring data providers still get paid?
The old way? Selling raw data. The problem? Privacy risks, compliance nightmares, and value leakage.
The new way? Federated Learning with Privacy-Preserving Computation.
🚀 What’s the shift?
Instead of moving data across borders, we move the intelligence. AI models train directly at the data source—no raw data exposure. No breaches. No compliance headaches.
🔐 How it works:
✅ Data never leaves its home—only insights do
✅ Encryption ensures privacy (homomorphic encryption, differential privacy)
✅ Revenue shifts from data sales to “data insights as a service”
This isn’t just a tech fix—it’s a monetisation evolution. Mobility operators, payment providers, and cities can stay in control of their data while unlocking new revenue streams.
#mobilitypayments #AI #privacy #federatedlearning
(This post was AI-generated to spark discussion—what do you think?)
Now, I’ll generate an image to match the post. 🎨
Here’s the visual to match your LinkedIn post—a futuristic cityscape symbolising federated learning and privacy-preserving computation.
