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Top 6 Data Collection Methods for AI & Machine Learning in 2024

Updated on Mar 26
9 min read
Written by
Shehmir Javaid
Shehmir Javaid
Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple.

He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers.

He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations.

He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.
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Top 6 Data Collection Methods for AI & Machine Learning in 2024Top 6 Data Collection Methods for AI & Machine Learning in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

The global AI market is in full swing (Figure 1) as more businesses develop and adopt AI-powered solutions to make their processes more efficient. Collecting data is one of the most important aspects of successfully developing and using AI in your business functions. However, data collection can become challenging if the right method is not selected. While some companies rely on AI data collection services, others gather their own data using scraping tools or another method.

This article explores the top 6 AI data collection methods and techniques to fuel your AI projects with accurate data.

Note: This article mainly focuses on AI data collection techniques. If you wish to learn about research data collection methods, check out this article on research data collection.

Figure 1. The global state of Big Data/AI1

The graphs shows that according to a survey of 116 businesses worldwide, many firms are finding it difficult to make progress in establishing innovative, data driven organizations. This makes knowing the right data collection methods for your AI project, more important.

1. Crowdsourcing

Online talent platforms, such as crowdsourcing platforms, have various benefits (Figure 2). Data crowdsourcing is done by assigning data collection tasks to the public, providing instruction, and creating a sharing platform. Businesses can also work with crowdsourced data collection agencies.

Figure 2. Projected global benefits of crowdsourcing and online talent platforms by 20252.

An illustration showing the future benefits of crowdsourcing platforms. This shows that more companies should use crowdsourcing platforms as a data collection method


  • Through crowdsourcing, developers can recruit a wide range of contributors in a short period of time, significantly accelerating the data collection process for projects with tight deadlines.
  • For an AI model to be unbiased, a wide range of data must be used to train it. Crowdsourcing enables data diversity since the data is gathered from all over the world. You can also gather multilingual data much more efficiently.
  • Crowdsourcing helps eliminate the costs related to data collection procedures such as hiring, training, and onboarding a team for in-house data collection. You also don’t have to purchase any equipment since the workers from the crowdsourcing platform use their own equipment.
  • Experienced crowdsourcing firms have domain specialists who can provide high-quality data collection specific to your project needs, ensuring that the data is not only diverse but also relevant and reliable.
  • This method can be used for both primary and secondary data collection, offering versatility in obtaining various types of information, from user-generated content to academic research data.


  • It can be difficult to track if the contributors have sufficient domain and language skills, especially when the project involves highly specialized or technical content.
  • It can also be difficult to track if the data collection assignments are being performed properly, since the workers are remote and in large numbers. They also may have varying interpretations of the tasks.
  • The data quality can also be difficult to track while using crowdsourced data collection, due to the variability in the contributors’ expertise and dedication.
  • Narrowing down the right contributors for the project can also be a challenge, requiring careful consideration of their qualifications and past performance.

You can check this guide to finding the most suitable crowdsourcing platforms on the market.

2. In-house data collection

The AI/ML developers can also collect their own data privately. This method is effective when the required dataset is small, and the data is private or sensitive in nature. This method is also effective when the problem statement is too specific, and the data collection needs to be precise and tailored.

AI developers can also gather data privately within the organization. This data collection method is effective when the required dataset is small, and the data is private or sensitive in nature. This method is also effective when the problem statement is too specific, and the data collection needs to be precise and tailored.


  • In-house data collection is the most private and safest way of gathering primary data.
  • A higher level of customization can be achieved through this method.
  • While conducting in-house data collection, it’s easier to monitor the workforce since they are physically present.


  • It is expensive and time-consuming to hire or recruit a data collection team.
  • It is difficult to find domain-specific information and data collection efficiency that crowdsourcing agencies can offer, for instance.
  • Multilingual data is also difficult to gather through in-house data collection.
  • The data collectors also need to perform data processing and labeling.

3. Off-the-shelf datasets

This data collection method is done through precleaned, pre-existing datasets that are available in the market. If the project does not have complicated goals and does not require a wide range of data, then this can be a good option. Prepackaged datasets are relatively cheaper than collecting and are easy to implement. 

For example, a simple image classification system can be fed with prepackaged data.

Most of the time, these pre-packaged datasets can cover 70-80% of the project requirements. However, in some cases, the 20-30% gap can cause issues.

Here is an extensive list of off-the-shelf or prepackaged datasets for machine learning models.


  • Less up-front costs for this data collection technique since you do not need to recruit anyone or gather the data yourself.
  • Quicker and easier to implement when compared to other data collection methods since the datasets were prepared in the past and are ready to use.


  • These datasets can have missing or inaccurate data. Therefore, they can require processing. They can cost more in the long run since more software and manpower might be needed to fill the 20-30% gap.
  • These datasets offer a lack of personalization/customizability since they aren’t created for a specific project. It is not suitable for models that require highly personalized data.

4. Automated data collection

Another trending data collection method is automation. This is done by using data collection tools and software to obtain data from online data sources automatically. Some commonly used automated data collection tools include;

  • Web-scraping: One of the most commonly used methods of data collection is web scraping. This data collection method focuses on gathering data from all online sources, such as websites and social platforms, using web scraping tools.
  • Web crawling: The terms web crawling and web scraping are sometimes used interchangeably; however, they have a slight difference. Web scraping only extracts data from specified sources. Check out our article on web scraping vs. web crawling to have a better understanding of the differences between them.
  • Using APIs: In this data collection method, the data is gathered through the application programming interface (API) provided for each data unit stored on different online sources


  • One of the main advantages of automated data collection is speed. Since the automated model does not tire, it can collect data much faster than other methods.
  • Automated data collection is one of the most efficient secondary data collection methods.
  • Automating data collection also reduces human errors that can occur when the data collection tasks become repetitive.


  • Maintenance costs of automating through web scrapers can be high. Since websites often change their design and structures, repeatedly reprogramming the web scraper can be costly.
  • Some websites use anti-scraper tools, which can limit the use of web scraping
  • While automation can improve the accuracy of the data collection process, it can only be used to gather secondary or existing data and can not be used for primary data collection
  • Raw data gathered from automated means can be inaccurate as well. To obtain accurate data, your team needs to analyze data after it is gathered.

Check out this quick read to learn more about data collection automation, its methods, and its top pros & cons.

5. Generative AI

Generative AI is taking over the tech industry, and it can also be used to generate AI training data. Generative AI, as its name suggests, is designed to generate content. This can be text, images, audio, or any other kind of data. When we talk about using it to generate training data for machine learning models, we’re talking about creating data from scratch or augmenting existing data to improve the model’s performance.

Here is how generative AI is being used to gather training data for healthcare tech:


Here are some advantages and ways of how the technology can be used to create data for machine learning models.

Filling data gaps

Sometimes, there might be scenarios or cases missing in a dataset. Generative AI can be used to simulate these missing scenarios.

Data augmentation

This is about making slight modifications to existing data. For example, in image recognition, we can slightly rotate, zoom, or change the color of images so that the machine learning model becomes robust and recognizes images even under varying conditions.

Synthesizing data

In situations where gathering real-world data is difficult, expensive, or time-consuming, generative AI can create synthetic datasets that closely resemble real-world data.

Privacy concerns

Sometimes, data can’t be shared because of privacy issues. Generative AI can create data that’s similar to the original but doesn’t contain sensitive information, making it safer to share.


Generating data using AI can be more cost-effective than traditional data collection methods, especially in cases where collecting real-world data is expensive or risky.

Diverse scenarios

Generative AI can create a variety of scenarios or cases, ensuring that a machine learning model is well-trained across different conditions.


While generative AI offers many advantages, there are also potential drawbacks when using it to create training data:

Data quality and authenticity concerns

One of the main risks of using generated data is that it might not always perfectly represent real-world scenarios. If the generative model has biases or inaccuracies, these can be transferred to the training data it creates. This means the machine learning model being trained might work well with the synthetic data but may perform poorly when faced with real-world data.

Overfitting to synthetic data

Overfitting happens when a machine learning model becomes too tailored to its training dataset and loses the ability to perform well with new, unseen data. If a significant portion of the training data comes from a generative model and doesn’t closely match real-world situations, the final machine learning model might become too optimized for the synthetic data and may not perform well in real-world applications.

Read more about AI overfitting and how to avoid it.


Since generative AI is a new technology, here are some recommendations to consider while leveraging generative AI to prepare AI training datasets:

  1. Ensure Data Diversity: When using generative AI to create training datasets, prioritize diversity in the data. This includes variation in demographics, scenarios, and contexts to prevent biases and ensure the AI model can generalize well across different situations.
  2. Regularly Validate and Update Data: Continuously validate the generated data against real-world examples and update the training set regularly. This helps in maintaining the relevance and accuracy of the AI model, especially in rapidly evolving fields.
  3. Monitor for Ethical and Legal Compliance: Keep a close eye on ethical and legal standards, especially regarding data privacy and intellectual property rights. Ensure that the generative AI is not replicating or perpetuating harmful biases or using protected or sensitive information without consent.

6. Reinforcement learning from human feedback (RLHF) 

Reinforcement Learning from Human Feedback or RLHF is a method where a machine learning model, especially in the context of reinforcement learning, is trained using feedback from humans rather than relying solely on traditional reward signals from an environment.

How it works:

Initial demonstrations

Human experts showcase the desired behavior through demonstrations, which could include a wide range of activities from playing a game to executing a specialized task. These demonstrations serve as a foundational dataset for the model, illustrating what successful performance looks like in various scenarios.

Model training

The model undergoes training using the demonstration data provided by human experts, learning to imitate the behaviors and decisions of the humans. This training process involves analyzing patterns in the data and developing strategies to replicate the expert performance as closely as possible.

Fine-tuning with feedback

After the initial training, the model’s performance is further refined through human feedback, where humans rank or score the different behaviors generated by the model. Based on this feedback, the model adjusts its strategies and decision-making processes to better align with human expectations and improve its overall performance.


Overcoming infrequent rewards

In many environments, it’s challenging to define a reward function, or the rewards are infrequent. RLHF can bridge this gap by leveraging human expertise to guide the model towards the right behavior.

Safety and ethical concerns

Instead of letting the model explore all possible actions, some of which might be harmful or unethical, humans can guide the model to behave in desired and safe ways.


Scalability issues

Continuously relying on human feedback can be resource-intensive. As the complexity of tasks grows, the need for human involvement can become a bottleneck, making it hard to scale the approach for large applications.

Introducing human biases

Human feedback can also introduce biases. If human evaluators have certain preferences, misconceptions, or biases, these can be inadvertently transferred to the AI model, leading to undesired behaviors or decisions.

FAQs for AI data collection methods

  1. Why is it important to choose the right AI data collection methods?

    Selecting the right data collection methods is crucial for the success of AI projects, influencing the data’s accuracy, quality, and relevance, which in turn affects the effectiveness and efficiency of the AI solutions developed.
    Accuracy and Relevance: Choosing the appropriate data collection method ensures the accuracy of the data collected, whether it’s quantitative data from online surveys and statistical analysis or qualitative data from interviews and focus groups. Accurate data collection is fundamental for building reliable AI models.

    Data Quality: High-quality data is the cornerstone of effective AI projects. Techniques like primary data collection methods and qualitative research methods help in gathering richer data directly related to the research question, enhancing the overall data quality.

    Efficiency: Utilizing the right data collection tools and techniques, such as online forms for quantitative research or focus groups for qualitative insights, can streamline the data collection process, making it less time-consuming and more cost-effective.

    Comprehensive Analysis: A mix of primary and secondary data collection methods, along with a balance of qualitative and quantitative data, allows for a more comprehensive analysis of the research question, contributing to more nuanced and robust AI solutions.

    Targeted Insights: Tailoring the data collection technique to the specific needs of the project, like using customer data for business analytics or health surveys for medical research, ensures that the collected data is highly relevant and can provide targeted insights for the AI model.

  2. Which method is most suitable for my AI project?

    Data Type and Quality: Determine whether your project requires image, audio, video, text, or speech data. The choice influences the richness and accuracy of the data collected.

    Dataset Volume and Scope: Assess the size and domains of the datasets needed. Larger datasets might require a mix of primary and secondary data collection methods, while specific domains may need targeted qualitative research methods.

    Language and Geographic Considerations: Ensure the data encompasses the required languages and is representative of the target audience, potentially necessitating diverse collection methods and tools.

    Timeliness and Frequency: Evaluate how quickly and how often you need the data. AI models requiring continuous updates need a reliable process for frequent and accurate data collection.

Further reading

For guidance on choosing the right tool/service for your project, check out our data-driven lists of data collection/harvesting services & sentiment analysis services, or reach out to us:

Find the Right Vendors

External resources

Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple. He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers. He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations. He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.

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