With the emergence of crowdsourcing platforms such as Amazon Mechanical Turk, more and more companies are making crowdsourced data a key component of their machine learning strategy. Working with crowdsourcing service providers grants access to a relatively inexpensive and scalable workforce.
By engaging a group of crowdworkers, companies can distribute hundreds of thousands of machine learning microtasks quickly and cost-effectively. Listed below are just a few of the many advantages of using crowdsourced data in machine learning.
1. Crowdsourcing Data Accelerates Time to Market
A recent study by AI market research firm Cognilytica found that nearly 80% of time spent on AI projects revolves around collecting, cleaning, and labeling data. That leaves only 20% for model development, training and calibration. This is precisely why so many machine learning companies decide to offload data tasks to crowdsourcing platforms. Outsourcing the initial data preparation allows you to focus your efforts on core development tasks, freeing up staff with strategic mindsets and technical skills to perform work more suited to their skillsets.
Crowdsourcing allows for many contributors to be recruited in a short period of time, thereby eliminating traditional barriers to data collection. Furthermore, crowdsourcing platforms usually employ their own tools to optimize the annotation process, making it easier to conduct time-intensive labeling tasks. Crowdsourcing data is especially effective in generating complex and free-form labels such as in the case of audio transcription, sentiment analysis, image annotation or translation.
2. Crowdsourcing Increases Data Diversity
An algorithm is only as good as the data you put into it. Likewise, for a model to produce unbiased results, a diverse training dataset with a balanced frequency of classes is crucial.
Crowdsourcing opens up convenient access to a large base of qualified workers. As a result, data can be gathered from thousands of diversified sources with relatively little effort upfront. By working with an experienced sourcing team, you can obtain data from historically hard-to-reach demographics. For example, companies like Lionbridge AI is able to source data in hundreds of different languages, dialects and geographic markets.
3. Reduce Project Costs with Crowdsourced Data
Generally speaking, companies spend five times as much money on internal data labeling as opposed to third party companies. If you’re conducting data operations in-house, a lot of resources have to go into recruiting, training and on-boarding workers. Crowdsourcing effectively eliminates these costs by employing a skilled workforce on a pay-per-task model.
In many cases the cost savings can be substantial. Especially when budget is a concern, using a traditional, in-house approach might not be feasible. A crowdsourcing company can help make sure that costs align with your predefined budget and goals.
4. Obtain Quality Data at Scale
Finally, crowdsourcing is a good way to scale data operations while maintaining quality results. The most reliable crowdsourcing platforms implement a variety of QA techniques to ensure that results comply with all quality requirements. Furthermore, specialized data annotation companies will have greater access to domain specialists experienced in annotating all kinds of data. All in all, outsourcing to a trusted partner greatly increases the chances that data will make a positive impact on your algorithm.
Crowdsourcing is changing how we look at the quantity, quality, and the diversity of data available. At Lionbridge, we are continually refining how we use crowdsourcing to get the best results possible. With a global pool of 500,000+ contributors on our platform, we process large datasets quickly while maintaining quality. Contact us today to learn more about how to crowdsource data with Lionbridge.