Draft:Data banking
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Data banking is a concept introduced by Francesco Coacci and Lorenzo Coacci, founders of the company Necto. It refers to an automated, multi-component service that enables users to earn interest or dividends from their personal data by connecting third-party products. Users receive fractional interests based on their contributions to datasets that are sold to buyers through a dynamic pricing algorithm.
Overview
[edit]Data banking allows individuals to monetize their personal data in a controlled and transparent manner. By integrating with various third-party services and products, users' data is collected and aggregated into datasets. These datasets are then made available for purchase on a dedicated portal, where buyers can select and acquire the data they need.
Dynamic Pricing Algorithm
[edit]The pricing of datasets is managed by a dynamic algorithm that considers factors such as data demand, uniqueness, and the volume contributed by each user. This ensures that prices are fair and reflect current market conditions. Users benefit by receiving dividends proportional to their data's value and contribution level.
User Benefits
[edit]- Monetization: Users can generate passive income from their personal data.
- Control: Individuals decide which data to share and have the ability to manage their data contributions.
- Transparency: The platform provides insights into how data is used and the revenue generated from it.
Data buyers
[edit]Buyers access a portal where they can browse and purchase datasets relevant to their needs. The dynamic pricing model allows for flexible pricing, making data acquisition more efficient and cost-effective. This system caters to various industries, including marketing, research, and analytics.
Applications
[edit]- Market Research: Companies can obtain consumer insights by purchasing relevant datasets.
- Advertising: Advertisers can access targeted data to improve campaign effectiveness.
- Healthcare: Aggregated health data can support medical research while maintaining user anonymity.
- Machine Learning Models: The collected data can be used to train machine learning (ML) models, enhancing algorithms for tasks such as prediction, classification, and personalization.
- Large Language Model (LLM) Training: High-quality datasets are essential for training large language models. By providing diverse and extensive data, data banking facilitates the development of advanced natural language processing applications.
Privacy and Compliance
[edit]Data banking emphasizes user privacy and complies with data protection regulations. Data is anonymized and secured to prevent unauthorized access. Users retain control over their data, aligning with global standards like the General Data Protection Regulation (GDPR).
See Also
[edit]References
[edit]- "Necto Official Website". Necto. Retrieved 27 September 2024.