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The world is data-based, and businesses are increasingly dependent. However, traditional data sourcing methods often present challenges related to diversity, transparency, privacy and cost. This article reviews the current state of distributed data collection and provides an overview of key steps to wisely selecting a distributed data provider.
It is now possible to control centralization to decentralization
Traditionally, centralized data collection involves collecting data from a variety of sources, such as apps, devices, or websites, and sending it to a single central server or database controlled by one organization. This data is collected via APIs, sensors, tracking tools, or manual input. The biggest bottleneck of this model for AI future and businesses is its inability to collect truly “global” and “diverse” data from different regions and cultures. Decentralized data collection addresses this by leveraging blockchain technology. This allows small cross-border payments that allow global users to voluntarily contribute their data in exchange for incentives.
Another important aspect is transparency. Centralized AI and data collection are often criticized for acting as a “black box” that is not transparent and accountable. People don’t know how and where to collect this data for their business. Furthermore, it is difficult to see whether data is collected legally and ethically. In contrast, decentralized data collection improves transparency by recording the data collection process on the blockchain and storing the data on multiple independent nodes rather than a single authority. This blockchain-driven structure allows users to track how and where data is efficiently used, reduce the risk of hidden operations, and ensure that a single party cannot modify or monopolize the data without extensive consensus.
As a result, decentralized solutions have emerged as a powerful alternative for businesses looking for a more robust data strategy. By leveraging blockchain technology, decentralized data collection improves both data diversity and verifiability, opening up access to new, previously undeveloped data sources.
Major distributed data platforms for business
Companies interested in investigating distributed data collection should:
Evaluate data requirements. Determine the specific type of data you need and the sourcing and privacy priorities. Evaluate platform capabilities: Investigate the features and technologies of the identified platform to determine its suitability. Consider an integration strategy. Plan how to incorporate decentralized data sources into existing business processes. Monitor industry development: Decentralized data landscapes are evolving and need to be aware of new solutions and trends on a continuous basis.
Below is a summary of core features and potential business applications on five notable platforms that operate in a distributed data collection space.
1. Ocean Protocol
Core Products: Distributed Data Market for AI and ML Data Sets.
Strengths:
You can safely publish and monetize your datasets. The data remains with the provider, allowing for private calculations. Strong community and corporate traction.
Best for: People who buy and sell data sets and run Compute-to-Data workloads.
Example: A data provider that accesses a specific medical imaging data set and maintains control over the data itself will train diagnostic AI.
Website: https://oceanprotocol.com/
2. Sahara Eye
Core Products: Decentralized Knowledge Agent Platform and AI Data Marketplace.
Strengths:
It focuses on building AI agents that interact with user-managed data. Provides incentives for users to contribute to knowledge and interact with AI. It focuses on sovereign data ownership and fine-tuning local models.
Best for: AI developers are trying to build autonomous agents trained on community-owned or enterprise-specific knowledge bases.
Example: Collect large, diverse datasets of user reviews to train sentiment analysis AI agents.
Website: https://oceanprotocol.com/
3. OortDatahub (provided by my own startup)
Core Products: AI’s distributed data collection and labeling solutions.
Strengths:
Many global data contributors. A complete stacking solution for obtaining high quality AI-enabled data: data collection and labeling, storage and computing (e.g. data cleaning and preprocessing).
Optimal: Companies that require diverse, real-world, and structured datasets to train or fine-tune AI models.
Example: Collect 50 languages and high quality datasets for special natural language processing AI.
Website: https://www.oortech.com/oort-datahub-b2b
4. VANA
Core Products: A decentralized platform for users to control, monetize and pool AI personal data.
Strengths:
Users can own and monetize personal datasets (social media, fitness, etc.). Create community-driven AI datasets with support for data pooling. Built-in token incentives for users who share their data.
Optimal: Ethically sourced users use mean personal data to build AI models, especially in the areas of social, health and lifestyle.
Example: Users can leverage VANA to own, control and monetize personal data by contributing to community-driven AI projects
Website: https://www.vana.com
5. streamr
Core Product: Real-time data network for distributed data streams.
Strengths:
Focus on real-time streaming data (IoT, mobility, sensor data, etc.). It is built on a peer-to-peer publish/subscribe protocol. Scale to meet the needs of time series data.
Best for: AI systems that rely on live data feeds such as self-driving cars, smart cities, trading bots and more.
Example: If your AI business is focused on predicting traffic patterns, Streamr can be used to access real-time data feeds from connected vehicles and sensors.
Website: https://streamr.network/
Data is a new frontier
As AI continues to expand, the true bottleneck will not become algorithms. It will be data. Success in the upcoming wave of AI innovation depends on timely access to high-quality, well-labeled, diverse data sets. However, efficient data collection infrastructure remains in its early stages. A leading organization investing in scalable, ethical, AI-ready, decentralized data collection solutions will be the industry leader tomorrow. The era of intelligent data procurement is not a trend, it is the next mainstream.
