Today's world is driven by data, and companies are increasingly dependent on data. However, traditional ways of acquiring data often face many challenges in terms of diversity, transparency, privacy, and cost. This article will review the current status of decentralized data collection, introduce the key steps to choose a data provider platform, and list five top platforms worth considering.

From centralized monopoly to decentralization

Traditionally, data collection involves sending data from various sources (such as applications, devices, or websites) to a central server or database controlled by a single organization. This data is usually collected through APIs, sensors, tracking tools, or manual input.

The biggest bottleneck of this model is that it is impossible to truly collect "global" and "diverse" data from different regions and cultures. Decentralized data collection solves this problem through blockchain technology, which makes small cross-border payments possible, thereby encouraging global users to voluntarily contribute data on the premise of receiving incentives - this is difficult for centralized or Web2 platforms to do.

Another key point is transparency. Centralized AI and data collection are often criticized as "black box operations" that lack transparency and accountability. People simply cannot understand how and where the data on these platforms is collected, and whether it is legal or ethical.

In contrast, decentralized data collection significantly enhances transparency by putting the data collection process on-chain and distributing and storing the data on multiple independent nodes rather than in the control of a single entity. This blockchain-driven structure not only allows users to track the use of data and reduce the risk of data manipulation, but also ensures that no single party can modify or monopolize the data without broad consensus.

Therefore, decentralized solutions are becoming an important alternative for enterprises to formulate data strategies. With blockchain technology, such platforms increase the diversity and verifiability of data and open the door to new data sources.

Key steps for enterprises to choose a decentralized data platform

If companies want to explore decentralized data collection methods, they should focus on the following points:

  • Assess data needs: Identify the types of data needed and their priorities in terms of access and privacy.
  • Evaluate platform capabilities: Gain an in-depth understanding of the technical capabilities and application scenarios of candidate platforms to determine their suitability.
  • Develop an integration strategy: Think about how to embed decentralized data sources into existing business processes.
  • Pay attention to industry trends: The decentralized data field is still developing rapidly, and we need to continue to pay attention to emerging solutions and trends.

Recommended five decentralized data platforms

1. Ocean Protocol

  • Core function: decentralized market for datasets for AI and machine learning
  • Advantages:
  • Datasets can be safely published and monetized
  • Data is retained by the provider, supporting privacy computing
  • Active community and corporate support
  • Applicable scenarios: Users who want to buy/sell datasets or run computing tasks on data
  • Example: Accessing a medical imaging dataset for training diagnostic AI while ensuring that the data provider has control over the data
  • Official website: https://oceanprotocol.com/

2. Sahara AI

  • Core functions: decentralized knowledge agent platform and AI data market
  • Advantages:
  • Focus on the interaction between AI agents and user data
  • Encourage users to contribute knowledge and participate in AI interactions
  • Emphasis on data sovereignty and local model fine-tuning
  • Applicable scenarios: Developers who want to build AI agents based on community or enterprise knowledge bases
  • Example: Collecting a large number of user reviews and training a sentiment analysis AI agent
  • Official website: https://sahara.ai

3. OORT DataHub

  • Core function: Provide decentralized data collection and annotation solutions for AI
  • Advantages:
  • Has a large global network of data contributors
  • Provide AI data full-process services, including collection, labeling, storage, preprocessing and calculation
  • Applicable scenarios: Enterprises that need diverse, real-world structured data to train or fine-tune models
  • Example: Collecting and annotating high-quality datasets in 50 languages for a multilingual NLP project
  • Official website: https://www.oortech.com/oort-datahub-b2b

4. Vana

  • Core function: A decentralized platform for users to control, monetize and share personal data
  • Advantages:
  • Users can own and sell their own data (e.g. social media, health, fitness, etc.)
  • Support data pooling and build community data sets
  • Built-in token incentive mechanism
  • Applicable scenarios: Building AI models with compliant and user-agreed data, especially suitable for social, health and lifestyle fields
  • Example: Users can control and monetize their personal data through Vana and contribute to community AI projects
  • Official website: https://www.vana.com

5. Streamr

  • Core functionality: Decentralized network for real-time data streaming
  • Advantages:
  • Supports real-time data streams from IoT, transportation, sensors, etc.
  • Based on peer-to-peer publish/subscribe protocol
  • Good at processing time series data
  • Applicable scenarios: AI systems that rely on real-time data, such as autonomous driving, smart cities, or trading robots
  • Example: If your AI business involves traffic prediction, you can use Streamr to access real-time data streams from connected cars and sensors.
  • Official website: https://streamr.network/

Data: The next hot topic in the AI era

As AI capabilities continue to grow, the real bottleneck is no longer the algorithm, but data. Whether high-quality, well-structured, and diverse data can be obtained in a timely manner will determine the success or failure of the next wave of AI innovation.

However, efficient data collection infrastructure is still in its early stages. Those companies that invest now in scalable, compliant, and AI-friendly decentralized data solutions will be the leaders of the industry's future.

The era of intelligent data acquisition is not a temporary trend, but a new main line of AI development.

Author: Dr. Max Li, Founder of OORT and Professor of Columbia University

Originally published in Forbes: https://www.forbes.com/sites/digital-assets/2025/05/02/top-5-decentralized-data-collection-providers-in-2025-for-ai-business/