Crowdsourced Data Collection: Benefits & Best Practices
Data collection is a crucial stage in developing AI/ML models, as it directly influences their real-world performance. Whether you work with a data collection service or gather data yourself, it’s vital to execute this process correctly.
Here, we explore crowdsourcing as an effective data-gathering method, helping businesses select the best approach for their AI/ML projects.
What is crowdsourced data collection?
Crowdsourcing involves a large group of dispersed participants worldwide who produce goods and services in exchange for compensation or as a voluntary endeavor.
In AI development, crowdsourcing plays a pivotal role, particularly in data collection. The service provided by this crowdsourced network in the AI context is to assemble the specific type and volume of data a client requires for their AI development project. This data is critical because it forms the foundation on which machine learning models are trained, refined, and deployed across various applications.
How to crowdsource data?
Collecting AI data through crowdsourcing can be performed through the following steps:
1. Determine the type of data that will be collected
Begin by clearly defining the nature and format of the required data. This could range from images, videos, and text to more specific categories, such as personal data, medical data, or public datasets. It’s crucial to identify the exact data type needed to ensure accuracy and relevance in AI training.
2. Determine the participants
Identify the profile of participants best suited to provide the data you need. For example, if you’re collecting medical data, you might want to engage healthcare professionals or patients. Understand the demographics and expertise required to ensure data accuracy.
3. Create (or outsource) a platform
Establish a user-friendly and secure platform that enables participants to register, submit their data, and engage in the data collection process. The platform should be intuitive and enable easy data management, with features to sort, filter, and monitor incoming data.
4. Provide instructions to gather data
Clearly communicate the data collection process to participants. Offer detailed guidelines, possibly accompanied by examples or demonstrations, to ensure that they understand the expectations and the standards required.
5. Create a compensation method for the participants
Decide on a fair and motivating compensation mechanism. This could be monetary rewards, vouchers, recognition certificates, or other incentives. Ensure compensation aligns with the task’s complexity and volume.
6. Gather data through the platform
Facilitate data submission on the selected platform. Ensure the platform can handle high data volumes and maintain data integrity. Provide support mechanisms, such as help desks or chatbots, to assist participants with any queries or challenges they might encounter.
7. Regularly evaluate data collection procedures
Continuously monitor and assess the efficiency and effectiveness of the data collection process to ensure optimal results. Identify any bottlenecks, quality issues, or areas for improvement. Adjust procedures as needed to optimize the flow of accurate and relevant data.
Benefits of data collection through crowdsourcing
This section explores the advantages of data collection through crowdsourcing.
1. Improves data quality and relevance
Experienced crowdsourcing platforms have established standards for data quality inspection and a formal recruitment process to ensure the relevance and accuracy of the data collected. For instance, an experienced crowdsourcing platform should:
- Evaluate the skills of the data collectors to match them to different data collection tasks aligned with their abilities.
- Additionally, provide the necessary training and instruction to data collectors to improve their data collection activities. They should also receive any domain-specific knowledge that may be helpful for data collection.
Prioritizing price over data quality is not a good practice. If a crowdsourcing platform offers a cheaper rate, ensure they do not compromise on quality.
2. Helps save time
In the complete lifecycle of an AI project, around 80%1 of the time is spent on data-related tasks, from which data sourcing/collecting accounts for a significant portion. This only leaves about 20% of the time for core development tasks.
This is why developers typically delegate data collection to crowdsourcing platforms, allowing them to focus on development tasks. Data crowdsourcing platforms can recruit a large number of contributors quickly, making them well-suited for large-scale data collection projects.
It should also be noted that some crowdsourcing platforms offer annotation services to further reduce time spent on AI projects.
3. Helps gather more diverse data
The performance of an AI model depends on the quality of the data used to train it. A high-quality dataset yields a diverse, unbiased, and inclusive AI model.
Crowdsourcing enables convenient access to a large base of skilled data collectors. As a result, data can be collected quickly and easily from thousands of diverse sources worldwide.
4. Helps reduce costs
Collecting large datasets can be expensive because it is labor-intensive and error-prone. For instance, if a speech-to-text platform requires audio data from 20 different countries, each with a different language, it can be costly for the developers to collect it manually.
Crowdsourced platforms can perform data collection at significantly lower prices. This is possible because they work with a skilled workforce on a pay-per-task model and have a scalable business model.
Best practices to overcome the challenges of crowdsourcing data
While crowdsourcing data collection is an effective method, it can also create some of the most complex business challenges that should not be overlooked. In this section, we highlight key challenges and share best practices to help overcome them.
1. Lack of confidentiality
Lack of confidentiality is a drawback of data crowdsourcing. If the project is sensitive or secretive, maintaining secrecy can be very challenging when working with thousands of data collectors worldwide.
1.1. Recommendations
You can implement robust data protection policies and secure data transmission methods to ensure the confidentiality, integrity, and availability of your data. You can also leverage encryption and secure data storage solutions to safeguard the data. Additionally, all data collectors should sign confidentiality agreements before commencing work.
Suppose your data collection platform lacks built-in security features. In that case, it may be necessary to work with a third-party crowdsourcing service provider that already has data protection standards in place for secretive projects.
2. Difficult to track and evaluate data collectors
Another drawback of crowdsourced data collection is the difficulty of monitoring and assessing data collectors. For instance, data collectors may intentionally or unintentionally submit plagiarized data or produce lower-quality content.
2.1. Recommendations
To overcome this challenge, it is essential to regularly evaluate data collection methods or collaborate with experienced crowdsourcing companies that have established procedures for checks and balances.
3. Difficult to vet the competency
Evaluating skill levels can also be a challenge when recruiting the crowd. For instance, if a company wants to collect a large-scale Chinese-language dataset to train a large language model, it can be challenging to hire recruiters fluent in that language.
3.1. Recommendations
To address this, companies should implement a recruitment process with filters that distinguish between experts and amateurs. If companies opt for in-house data crowdsourcing projects, it is important to recruit skilled data collectors.
You can also work with a crowdsourcing data service provider with a large and diverse network of contributors from many countries.
Further reading
For guidance on finding the right tool/service for your project, check out our data-driven list of data collection/harvesting services.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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