Collecting high-quality datasets is the fuel that powers artificial intelligence. Without abundant, relevant data to train machine learning models, even the most advanced algorithms falter. That‘s why leading companies across industries – from tech giants developing self-driving cars to pharmaceutical researchers creating life-saving drugs – increasingly rely on crowdsourced data.
In this 3,000 word guide for data analysts and AI developers, we‘ll explore the transformation that crowdsourcing is bringing to enterprise data strategies. You‘ll learn:
- What is crowdsourced data and how does it work?
- Innovative examples of companies leveraging crowdsourced data
- 5 key benefits of crowdsourced data collection
- Best practices for ensuring high-quality contributions
- How crowdsourcing leads to more diverse, unbiased datasets
- Comparing crowdsourcing to other data collection methods
- Overcoming confidentiality and quality control challenges
- The future of crowdsourcing: growth forecasts and emerging use cases
By the end, you‘ll understand why crowdsourcing is becoming an essential component of cutting-edge data strategies – and how your organization can harness networked intelligence to gain a competitive advantage.
What is Crowdsourced Data and How Does it Work?
Crowdsourced data is information gathered from a large, decentralized network of contributors around the world, known as "the crowd." Companies submit data collection tasks to crowdsourcing platforms, which then distribute the work to registered contributors. These distributed workers upload relevant data, often receiving monetary payments in return.
For example, an autonomous vehicle company might use a crowdsourcing provider like Amazon Mechanical Turk to gather millions of images of street signs. These images are then used to train computer vision algorithms that can recognize signage in real-time. Other examples include using crowdsourcing to gather training data for machine translation systems, sentiment analysis tools, medical diagnosis applications, and more.
Crowdsourcing platforms act as marketplaces, matching companies needing data with global networks of potential contributors.
Unlike centralized or in-house data collection, crowdsourcing offers extreme scale, cost efficiency, and diversity. When managed properly, it also provides reliable quality through redundancy and statistical aggregation.
Next, we‘ll look at real-world examples of enterprises leveraging crowdsourced data, and dive deeper into why it provides a strategic advantage over conventional data gathering techniques for ML/AI systems.
Real-World Examples: Companies Leveraging Crowdsourced Data
Leading organizations across many industries have incorporated crowdsourcing into their data strategies. For instance:
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General Motors uses crowdsourcing to quickly gather annotated images of traffic scenarios. These datasets help GM train autonomous driving systems to interpret complex real-world environments.
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Facebook leverages crowdsourcing to expand its human language capabilities. By sourcing text translations from worldwide contributors, Facebook rapidly supports 100+ tongues on its social platforms.
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Pfizer, the pharmaceutical company, employs crowd workers to analyze biochemical interactions between drug compounds and proteins – accelerating drug discovery.
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Yelp supplements its site content with crowdsourced local business reviews and images. This user-generated data powers its recommendations engine and map functionality.
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Duolingo has built one of the world‘s most popular language learning platforms powered by crowdsourced translations and lesson content. The startup vastly outpaced competitors through harnessing external contributions.
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Meetup unlocked exponential growth by having members crowdsource organizing local events rather than traditional manual curation. This marketplace model allowed infinite ideas and scale.
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Patreon connects artistic creators with fan financial backers directly. Its crowdsourced patronage funding empowers niche indie media otherwise unsustainable.
And the list goes on covering diverse domains… These examples demonstrate how players across tech, automotive, healthcare, consumer goods, and other verticals deploy crowdsourcing to expand capabilities. Now, let‘s examine why crowdsourcing offers such a strategic advantage for data-centric ML/AI systems by detailing key benefits.
5 Key Benefits of Crowdsourced Data Collection
Crowdsourced data offers significant advantages over conventional, centralized data gathering:
1. Extreme scale – Crowdsourcing providers connect enterprises with contributor pools numbering in the hundreds of thousands to millions. For data-hungry ML systems, crowdsourcing offers virtually limitless data generation potential.
2. Cost efficiency – By compensating contributors on a piecework basis, crowdsourcing averages 50-75% savings versus in-house data collection costs. It also avoids overhead from maintaining internal labeling teams.
3. Speed – Distributed global workers enable assembly of huge datasets with low latency. For time-sensitive projects, crowdsourcing accelerates data collection from months to weeks or less.
4. Quality – Leading crowdsourcing platforms employ robust QA measures including statistical validation, contributor ratings, and accuracy-based pay incentives. The best services produce data that meets or exceeds expert-level standards.
5. Diversity – Crowd workers introduce geographic, cultural, linguistic, and demographic diversity difficult to achieve otherwise. This translates to less biased, more representative training datasets.
Let‘s explore a few of these advantages in greater detail…
Ensuring High-Quality Contributions
Given its distributed nature, how can crowdsourcing yield reliable datasets comparable to professionally-generated content? Leading platforms implement several reliability mechanisms:
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Statistical consensus – Collecting multiple redundant submissions for each data item allows identifying outliers. Aggregating contributions builds confidence in the resulting output.
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Contributor testing & ratings – Platforms continually evaluate participants, building profiles regarding areas of expertise, data quality, and productivity. Highly-rated contributors may receive premium access to lucrative jobs.
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Accuracy-based compensation incentives – Pay rates tied directly to submission accuracy motivate careful, diligent work. Contributors delivering erroneous or duplicate data see declining income.
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Automated validation – Data submissions undergo both statistical and software-based analysis assessing factors like formats, labels, completeness, duplicates, etc. Flagged entries get routed for human review.
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Support resources – Platforms furnish data suppliers, job instructions, access to sample work, online communities, and help channels. These aid participants in fulfilling jobs properly.
By combining redundancy, inspection, collaboration, and accuracy-based pay incentives, well-managed crowdsourcing achieves both high throughput and reliability – exceeding what any individual could produce alone.
For example, researchers found crowdsourced image labels matched expert quality 96.6% of the time on average. However, aggregating just 10 judgments per item boosted accuracy to over 99% – demonstrating the power of "wisdom of crowds" effects with proper oversight.
Additional Quality Control Best Practices
Beyond those core quality mechanisms, data leaders can implement additional protocols optimizing crowdsourced results:
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Perform spot audits by manually reviewing random subsets of collected data, gauging items like relevance, accuracy, uniqueness, and consistency. Feed findings back into improving platform policies.
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For complex jobs, break down tasks into multiple stages based on skills/roles. This ensures matching specialist contributors to each phase of data construction appropriate for their capabilities.
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Analyze agreed-upon metrics between data requesters and suppliers, ensuring alignment. Treat platform operators like partners rather than simple vendors.
Adhering to structured crowdsourcing excellence disciplines achieves superior outcomes comparable or exceeding conventional resourcing.
Crowdsourcing Leads to More Diverse Datasets
For machine learning systems processing fuzzy real-world data, diversity is crucial for performance. Unfortunately, many datasets suffer from demographic and geographic skew making models less robust.
Crowdsourcing offers a remedy, enabling access to contributors across age, gender, ethnic, linguistic, ability, and geographic spectrums. This translates to data variability better approximating the application environment – yielding more widely usable models.
For example, natural language processing and speech recognition systems trained mostly on North American English accents struggle understanding other dialects. Leveraging crowd workers to source diverse global speech data leads to much higher cross-geo accuracy.
Researchers even suggest diversity should be a primary metric for dataset quality – not just volume and accuracy. Thankfully leading crowdsourcing platforms allow filtering contributors by various demographic factors to intentionally manufacture diversity.
One study discovered that simple majority vote crowd labeling significantly outperformed individual expert annotations for image tagging accuracy. This demonstrates the power of crowdsourcing‘s built-in diversity for data tasks requiring subjective interpretation.
In summary, diversity makes for more robust ML models while combating unfair bias – and crowdsourcing offers efficient access to an abundance of diversity.
Comparing Crowdsourcing to Surveys, Web Scraping and Other Approaches
Now that we‘ve covered crowdsourcing‘s capabilities in depth, how does it compare with other popular data collection methods? Here‘s a quick rundown…
| Data Collection Method | Scale Potential | Cost Efficiency | Collection Speed | Result Quality | Inherent Diversity | Data Type Flexibility |
|---|---|---|---|---|---|---|
| Crowdsourcing | Extremely High | High | Very Fast | High | High | High |
| Surveys | Low | Medium | Slow | Varies | Medium | Low |
| Web Scraping | High | High | Fast | Medium | Low | Low |
| In-house Generation | Low | Low | Slow | High | Low | High |
| Outsourcing | Low | Medium | Varies | High | Medium | Medium |
Surveys engage respondents in structured data contribution scenarios (e.g. product feedback). However, they lack scalability and random sampling control. Crowdsourcing enables more flexibly acquiring diverse datasets orders of magnitude larger.
Web scraping uses bots to harvest online information at massive scale. But it depends on finding target data published somewhere – rather than users generating fresh samples. Combining scraping and crowdsourcing allows creating benchmark training sets then continuously expanding them.
In-house generation directly employs skilled labeling staff internally. Yet boutique teams can‘t match crowdsourcing‘s global contributor population for scale or economical cost. Crowdsourcing also offloads administrative overhead.
Outsourcing pays external vendors like BPOs to handle defined data tasks. It struggles with speed, flexibility, finding rare domain expertise niches, and novelty scenarios. Crowdsourcing‘s vast on-demand talent access resolves these issues.
While every approach has advantages, none match crowdsourcing‘s versatility. It enables responsive procurement of specialized, large-scale datasets unmatched elsewhere. Blended with complementary methods like surveys and web scraping, crowdsourcing should anchor any modern data strategy.
Overcoming Crowdsourcing‘s Biggest Challenges
For all its strengths, effectively managing crowdsourced data requires mitigating some inherent challenges:
Maintaining data confidentiality – By externalizing sensitive data collection, confidential leaks may occur. Robust security protocols, contributor NDAs, data encryption, access controls and audits help minimize risk. Still, crowdsourcing isn‘t suitable for all kinds of restricted corporate data.
Vetting contributor capabilities – Not all crowd participants possess the skills a project demands. Screening based on profiles, test submissions, certifications, demographics, and prior ratings allows optimizing the worker pool. However, low barriers to entry mean some mismatch is unavoidable.
Preventing plagiarism – Submitting copied or falsified data is a constant concern with anonymized external contributors. Checking samples against public sources, assessing unique perspectives, and requiring evidence sources combat this issue.
Rating submission quality – With no firsthand access to contributors, gauging submission quality at scale seems implausible. Yet combining trained internal reviewers with platform inspection mechanisms makes viable large-scale content rating.
Despite challenges, disciplined usage policies focused on security, talent selectivity, fraud detection, and results validation keep risks reasonably contained. Weighing these tradeoffs, most companies find crowdsourcing‘s efficiency and capabilities provide substantial net benefit.
Advanced Techniques for Mitigating Platform Risks
Mature crowdsourcing programs develop sophisticated governance for managing decentralization complexities, such as:
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Phase project data components according to sensitivity, only externalizing unrestricted elements. Maintain tight access control for certain tasks.
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Build rich multimedia digital profiles of contributors, including background checks, psychometric assessments, and capability demonstration records. Leverage for screening.
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Use blockchain-style tamper-resistant logging of all crowdsourcing transactions, facilitating auditing and root cause analysis for finding weaknesses.
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Continuously sample labor marketplace options beyond incumbent partners. Multi-sourcing data tasks increases supply security.
With deliberate effort, firms effectively constrain inherent platform uncertainties, realizing immense advantages.
The Future of Crowdsourced Data: Growth Projections and Emerging Use Cases
Crowdsourced data constitutes one of the most transformational innovations around enterprise machine learning in decades. But in many ways, adoption still remains at early stages – what does the future look like?
Industry forecasts call for explosive growth… Analysts predict the crowdsourced data market growing upwards of 30% annually, reaching $12B+ by 2027. Driving this surge? Wider enterprise AI/ML adoption plus increasing data complexity demands.
Use cases scale across verticals… While tech firms pioneered crowdsourcing for ML training data, platforms are now targeting verticals like automotive, life sciences, retail, financial services, government/social services, and more with tailored solutions. Expect boundaries constantly expanding.
Techniques grow more advanced… Already researchers have demonstrated the viability of aggregating crowd feedback not just for labeling, but also for generating synthetic data, cleansing datasets automatically, building ML annotation tools, and evaluating model performance.
Platform capabilities rapidly evolving… Expect expanded support for multimedia data types like 3D images, video, and audio. There‘s also a major push towards tools for automation, analytics, collaboration, and building domain-specialized human-in-the-loop hybrid AI systems.
Far from peaking, crowdsourcing remains early in hopefully realizing its full potential helping enterprises harness external contributions advancing AI for everyone‘s benefit. No organization seeking to lead its industry through cutting-edge data science can afford ignoring this paradigm shift.
Key Takeaways on Crowdsourced Data Collection
We‘ve covered a lot of ground exploring the explosive rise of crowdsourcing and its key lessons that every data leader should internalize:
- Crowdsourcing allows rapidly amassing niche labeled datasets through on-demand access to vast flexible contributor pools.
- Global tech and Fortune 500 leaders already employ crowdsourcing to expand capabilities – especially in ML-powered products.
- For machine learning systems, crowdsourced data provides extreme scale, higher quality, improved diversity, and significant cost savings versus conventional collection.
- Leading crowdsourcing platforms implement statistical confidence measures, contributor ratings, compensation incentives, and fraud detection establishing reliable quality at scale.
- Crowdsourcing advances faster model development, saves costs, and fights unfair bias – while supporting more diverse use cases than alternatives like web scraping or surveys.
- Despite confidentiality and vetting challenges requiring precautions, crowdsourcing yields substantial net efficiency advantages according to most adopters.
- Analysts forecast over 30% annual growth for the crowdsourcing market as more companies wakes up to its disruptive power.
- Emerging techniques even demonstrate using crowdsourcing beyond basic data labeling, towards synthetic sample generation, automatic dataset enhancement, building annotation tools, and evaluating model performance in the loop.
I hope this guide provided a comprehensive introduction to the paradigm shift that crowdsourced data represents. You now possess actionable insights on harnessing its potential to gain strategic machine learning and AI competitive advantages for your organization.
To dig deeper, don‘t hesitate contacting our team of data experts at Aimultiple.com. We stand ready to offer additional crowdsourcing guidance, platform recommendations, custom dataset buildouts, and strategic roadmapping tailored to your analytics objectives and industry.
