9 Best Data Crowdsourcing Platforms for 2023

Data is the new oil. Today, data powers everything from machine learning models to business intelligence. But actually acquiring quality data can be challenging and expensive. This is where data crowdsourcing provides immense value. By breaking down data tasks and distributing them to a crowd of people, organisations can gather diverse datasets, run large surveys, moderate content and more at scale.

In this post, we look at the 9 leading data crowdsourcing platforms to consider leveraging in 2023 and beyond

What is Data Crowdsourcing?

Data crowdsourcing is a process of collecting data from a large group of people. The data is then typically used to improve a product or service or for research purposes.

Data crowdsourcing can be an effective way to collect large amounts of data quickly and cheaply. There are a number of online crowdsourcing platforms that businesses can use to collect data from a large number of people.

However, there are some potential drawbacks. For example, the quality of the data may be lower than if it was collected by professional researchers. Additionally, the data may be biased if the people who choose to participate in the crowdsourcing project are not representative of the population as a whole.

There are a number of factors to consider when choosing an online crowdsourcing platform, such as the type of data you need, the number of people you need to reach, and the budget you have available. To help you choose the right platform for your business, here are 9 of the top data crowdsourcing platforms:

1. Twine AI 

Twine AI specialises in providing audio and video datasets for computer vision models via its global community of freelancers for machine learning and AI research projects. Twine AI provides access to its global community of half a million experts from 190+ countries. Trusted by Synaptics, Ikea, Hypersentience, Synthesia and others.

Key Features:

  • Multimodal data collection– Twine AI specialises in image, text, audio, video and sensory dataset creation covering computer vision, NLP, speech recognition and more.
  • Scalable and diverse workforce – The certified half-a-million contributor pool scales to thousands allowing virtually limitless throughput. The global workforce from 190+ countries brings diversity to combat bias.
  • Data Compliance – Twine AI’s data collection and annotation practices adhere to ethical sourcing standards and localised AI regulations.
  • Dedicated Project Manager- Have your data collection project run by an experienced  Project Manager who can ensure all participants are following instructions and work with you to improve the collection process.
  • High Quality and Accuracy – Twine AI’s multi-staged Quality Assurance Process ensures data collected/annotated are of the highest quality and accuracy consistently.

Use cases: Image, audio and video dataset collection, data annotation, data labelling

2. Appen

Appen is a well-established data annotation company that leverages a curated crowdsourced workforce of over 1 million contractors. 

Key Features:

  • Large managed crowdsourced workforce
  • Specialised contractors for sensitive content moderation
  • Customised testing and training of annotators
  • Multi-stage quality assurance workflows
  • Secure cloud-based data handling

Use Cases: Data labelling and annotation, content moderation

3. Amazon Mechanical Turk

Amazon Mechanical Turk is the most well-known crowdsourcing platform from Amazon Web Services. It provides access to 500k+ global workers for microtasks like data labelling, surveys, transcription, content moderation and more. Has template workflows for common tasks and integrates with other AWS services.

Key Features:

  • Massive global on-demand workforce
  • Pre-built templates and workflows
  • API access to integrate with workflows
  • Tight integration with AWS services
  • Robust platform with enterprise security

Use Cases: Data labelling, classification, surveys, content moderation

4. Scale AI

Scale AI is a high-scale data enrichment platform with global workforce support and project management. Used by Oracle, Nuance, PubMatic and others.

Key Features:

  • API for custom workflows and integration
  • Real-time analytics and dashboards
  • Secure data processing and infrastructure
  • Per-project billing and quotas
  • Project manager assigned to each project

Use Cases: Data categorization, sentiment analysis, surveys

5. Lionbridge AI

Lionbridge AI is Part of the Lionbridge translation services. Offers crowd-based data annotation tailored for AI applications.

Key Features:

  • Specialised for text, image, video and speech datasets
  • Screened annotators for quality
  • Translation support in 300+ languages
  • Collaboration tools for large teams
  • Customised workflows

Use Cases: Multimodal data labelling for ML training

6. Summa Linguae Technologies

Summa Linguae Technologies is a language data services company specializing in multilingual data annotation for machine learning training datasets.

Here is a list of some of Summa Linguae Technologies’ data offerings:

7. Surge AI

Surge AI offers data annotation services focused primarily on computer vision training datasets for applications like autonomous vehicles, robotics, AR/VR and more.

Key Features:

  • Image and Video Annotation
  • AI Data collection
  • Sensor Data Labeling
  • Quality Assurance

8. Toloka AI

Toloka AI is a crowdsourcing platform used to gather and enhance AI training data. To improve machine learning models, they offer a range of services, including data labelling, data cleaning, and data categorization. 

Here is a list of Toloka AI’s data solutions:

9. Telus International 

Telus International is primarily concerned with digital IT solutions and customer experience (CX). A data annotation platform Playment powered by a global crowd acquired was by Telus International. It provides a vast array of services, but it also leverages a sizable workforce to provide data services. 

Here is a list of Telus International’s data solutions:

Best Practices for Crowdsourced Data

Follow these best practices when leveraging crowdsourcing:

  • Provide clear instructions, examples and FAQs for each task.
  • Implement qualification tests or screening to filter high-quality contributors.
  • Build in ongoing QA processes, spot checks and audits.
  • Have a process to quickly address low-quality work.
  • Treat workers ethically and compensate them fairly based on local costs of living.

Key Evaluation Criteria

When selecting a crowdsourcing partner, some key factors to evaluate include:

  • Workforce size and diversity – Larger and more diverse pools offer more scalability.
  • Experience – Domain expertise in specific data tasks needed.
  • Quality – What QA measures are in place to ensure high-quality work?
  • Security – How is data security, access and privacy handled? Is IP consent required in data collection?
  • Workflow – Are workflows customizable and optimised for quality?
  • Platform – Is the platform enterprise-ready and easy to integrate/manage?
  • Support – What customer support and expertise is available?

Conclusion

Data is a crucial asset, but acquiring quality data at scale remains challenging. Data crowdsourcing enables distribution of data tasks to thousands of people, unlocking immense speed, cost and scale benefits. Leading data crowdsourcing platforms like Twine AI, Appen, Scale and Summa Linguae Technologies provide access to managed on-demand workforces that deliver transformative value. With proper workflows, quality assurance and effective task design, crowdsourcing enables obtaining the labelled data, data creation, content moderation, market research and other critical data outputs needed to power data-driven business success.