Crowdsourcing has revolutionized how organizations source the training data needed to power AI systems. By tapping into the skills and perspectives of a distributed network of contributors, companies can gather highly specialized datasets with greater efficiency, diversity and cost-effectiveness compared to traditional in-house data collection.
However, effectively leveraging crowdsourced data also requires implementing best practices around quality control, security, workflow optimization and more. In this comprehensive guide, we’ll explore the multifaceted value proposition of crowdsourced data collection and proven strategies for addressing common challenges.
The Rising Prominence of Crowdsourced Data Collection
The need for quality training data continues to grow exponentially as AI adoption accelerates across industries. Some projections estimate the AI training data market will surpass $3.7 billion globally by 2028.
However, sourcing and labeling this data remains a major bottleneck. Manual in-house data collection simply cannot keep pace with ballooning data demands. One study by Figure Eight found that data collection and labeling accounts for up to 80% of the total time spent creating AI systems.
Crowdsourcing provides a scalable solution to this data bottleneck. By distributing data collection tasks to qualified contributors around the world, crowdsourcing platforms can rapidly generate immense datasets. For instance, Appen leverages a crowd of over 1 million to provide over 100 million human judgments daily.
I‘ve witnessed firsthand the transformative impact crowdsourced data collection has had across Fortune 500s and cutting-edge startups building AI products. In one project for an autonomous vehicle company, we slashed the client‘s data turnaround time from months to weeks by crowdsourcing sensor data labeling. The tremendous time and cost savings unlocked their ability to accelerate product development cycles.
The crowdsourcing model offers unique advantages that positions it as a key enabler of next-gen AI:
Benefits of Crowdsourced Data Collection
1. Improves Data Quality and Relevance
In my experience, a common misconception companies have is that crowdsourced data means sacrificing quality for speed and cost savings. In reality, experienced crowdsourcing platforms have highly rigorous processes and checks in place to ensure data accuracy and relevance.
For example, Figure Eight leverages proprietary machine learning algorithms to automatically validate human-labeled data sets. This identifies potential errors and inconsistencies for further review.
Top platforms also carefully screen contributors for skills, diligence and domain expertise. They provide ongoing training to continuously improve data collection proficiency. Contributors are then matched to tasks that align with their capabilities and specializations.
This results in data that not only meets quality bars, but also better matches the precise real-world conditions needed to train robust AI models. Studies have found classification accuracy using crowdsourced training data on par with expert annotators.
2. Accelerates Project Timelines
By tapping into crowdsourcing, companies can scale data collection drastically beyond the capacity constraints of in-house teams. Instead of taking months, large-scale annotation jobs get completed in weeks or days by distributing the workload in parallel.
CrowdFlower and Google together benchmarked how long various natural language processing data collection tasks would take with internal resourcing versus crowdsourced models. They found speed improvements ranging from 3x (for text classification) up to 200x (for sentiment analysis)!
This acceleration effect frees up precious time for internal data scientists and engineers to focus on where they can add the most value – building, validating and deploying models into products and services.
3. Enables Wider Data Diversity
For AI systems to work reliably in the real world, training data must encompass the full spectrum of scenarios and variability the AI will encounter. This requires massive, multidimensional datasets often too expansive for limited in-house resourcing.
Crowdsourcing provides a solution through on-demand access to diverse, specialized skills and perspectives globally. For example, if an eCommerce company wants to build a multilingual product chatbot, a platform like Unbabel can leverage crowdsourced translators and editors that speak the 200+ languages the chatbot aims to support.
I recently helped a leading job search company crowdsource salary data linked to job titles across different countries, seniorities, industries and more. This enabled them to build a much more extensive and accurate automated salary estimation tool.
4. Reduces Costs
The crowdsourcing model transforms fixed data team costs into flexible variable costs aligned with each project‘s actual data needs. Companies only pay for the specific tasks completed instead of carrying excess salaried headcount.
These costs can also be significantly lower compared to specialized in-house teams when leveraging crowdsourcing platforms‘ vast on-demand contributor pools and economies of scale. According to a Forrester study, crowdsourcing data annotation decreased costs by 50% for a retail client compared to manual in-house approaches.
Key Challenges and Best Practices for Crowdsourced Data
While crowdsourcing offers multifaceted benefits, effectively managing a crowdsourced workforce also poses unique challenges around data quality, security, contributor management and more. Here are proven best practices I‘ve compiled from my experience for high-impact crowdsourced data programs:
1. Establish Clear Quality Standards and Checks
Leave absolutely no ambiguity around the deliverable expectations and examples of what quality looks like. Provide detailed task instructions and have contributors pass qualification tests. Conduct ongoing audits of random samples using internal expert review. Automate QA through algorithms where feasible – platforms like Scale use ML to detect errors and anomalies.
2. Safeguard Data Privacy and Security
For confidential data, ensure proper oversight like anonymization and contributor non-disclosure agreements. Employ end-to-end encryption, access controls and secure data handling principles aligned with protocols like HIPAA. Conduct external audits of platform security provisions if needed.
3. Choose Crowdsourcing Partners Strategically
Opt for platforms with strong track records suited for the specific data collection use case – e.g. Appen for speech data or CloudFactory for content moderation. Validate their contributor communities, quality processes and security measures match project needs. Be wary of ultra-cheap providers that may compromise on quality.
4. Structure Tasks and Incentives Effectively
Deconstruct larger jobs into microtasks for faster throughput. Set reasonable per-task pricing that incentivizes quality without overpaying. Gamify workflows where possible – platforms like Remotasks use game elements to engage crowds. Iterate on task design through initial trial runs to optimize efficiency.
5. Monitor Progress Closely
Continuously track project dashboards to spot any constraints or quality issues requiring intervention. Maintain transparency into work status through platform APIs. Be ready to rapidly adjust workflows, pricing or task instructions based on monitoring insights.
While crowdsourced models require tighter management, the payoff in accelerating secured, high-quality data collection is immense. For organizations racing to operationalize AI, getting the right data fast and cost-efficiently can be the difference between leading the pack or falling behind.
The Future of Crowdsourced Data Collection
The need for crowdsourced data will continue ballooning as AI expands into every sphere of business and society. Market researchers forecast the demand for training data itself growing up to 800% by 2030. Platforms will continue innovating with smart tools for automated QA, niche skill matching and gamified microtasks.
Those who learn today how to effectively tap into the skills of the crowd will gain an enduring competitive advantage as AI becomes increasingly central to products, services and operations. However, achieving the full value of crowdsourcing requires upfront strategic planning and vigilant management. I hope this guide provides helpful perspective on unlocking the power of crowdsourced data collection for your organization‘s AI aspirations.