6-Step AI Data Collection Process & Roadmap in 2024

Gathering relevant training data for your AI models can be challenging (Figure 1). While some companies work with AI data services or data crowdsourcing platforms, others gather their own data. If data preparation and collection while training and deploying AI is done improperly, it can lead to low model performance and failure of the entire project.

This article will provide a 6-step roadmap to help you improve your data collection methodology for your AI/ML projects.

Figure 1. AI adoption barriers.

1. Planning and need identification

This will be your first step towards acquiring relevant data. The planning phase is one of the most critical stages of the data collection process since it sets the groundwork for the whole project. Consider the following in your data collection plan:

1.1. Define objectives

Before collecting data, it’s essential to have a clear understanding of what you intend to achieve with the AI or ML model. Defining data requirements will guide you towards accurate data collection, ensuring that you are gathering data that will be useful for your specific use case.

For instance, if a computer vision system is required to perform quality assurance for apples on a well-lit conveyor belt, it will not benefit from being trained with apple images in different lighting environments. That’s because, in practice, the light above the conveyor belt will remain stable:

Figure 2. Images of different types of apples separated by type¹

1.2. Identify data sources

One of the first steps is to identify where your data will come from. Depending on your objectives, different data sources may be more relevant than others. Whether it’s from online sources, customers, or other platforms that generate new data on demand, the source should align with your project goals for the most accurate data collection possible.

For instance, a facial recognition system at the airport should analyze faces of different shapes, colors, and sizes. This requires a diverse and large dataset. Collecting such a dataset in-house can be expensive and time-consuming; hence, the crowdsourcing method might work better for such a dataset.

1.3. Consider the resources

If your project requires a specialized data collection method, such as sensors for IoT devices, video cameras for object detection, or microphones for speech recognition, you’ll need to identify and prepare the necessary data collection equipment well in advance. This preparation is crucial for ensuring the data’s quality and relevance.

1.4. Consider the legal and ethical factors

In today’s data-driven world, you must also consider the legal and ethical ramifications of your data collection methods. This is particularly important when dealing with sensitive or personally identifiable information. You should make sure you have the rights to use the data you are collecting, and you should follow best practices for data privacy and security.

Learn more about data collection ethics here.

2. Design and preparation

In this phase, you select the right data collection methodology and prepare the necessary tools or resources that might be required.

2.1. Choose the right data collection method

Now that the type of data has been determined, you can identify the method through which that data will be collected. There are 4 key methods of collecting data for your AI/ML projects:

1. Crowdsourced data collection: Where the data is sourced from the crowd in the form of microtasks. While being done in-house, this can be costly and expensive; however, third-party data collection/harvesting service providers can offer it efficiently. 
2. Private / In-house data collection: This method is good for small datasets with sensitive or personal projects.
3. Precleaned and prepackaged data: When the project does not require a highly personalized dataset, readily available datasets can be the way to go.
4. Automated data collection: To gather secondary data through automated means, you can use web scraping and crawling tools. Web scraping involves leveraging bots to extract data from websites of a specific domain. Click here to learn more about web scraping.

To learn more about these four data collection methods, check out this quick read.

2.2. Prepare tools and infrastructure

Once you’ve decided on the data collection techniques, you need to set up the necessary data collection tools and infrastructure to gather data effectively. 

This could range from purchasing web scraping tools to obtaining the equipment to generate data in-house. The tools or resources should be tested rigorously to ensure they collect accurate and relevant data according to your predefined methodologies.

3. Quality assurance

Performing QA and QC during and after the data has been gathered is paramount. This phase ensures the data is reliable, accurate, and useful for building robust machine learning models. You can consider these steps:

3.1. Identification of data quality issues

Before and during the process of gathering data, potential data quality issues should be identified. Knowing these challenges beforehand can help in tailoring your data collection approach to mitigate them.

3.2. QA during data gathering

Quality Assurance starts with the data-gathering process itself. The goal here is to prevent data quality issues from occurring in the first place. This involves rigorous planning and scrutiny of the data collection approach to ensure it aligns with the overall objectives and produces high-quality data.

This process is also called data preprocessing where the data is processes during the collection process. You need to:

• Clean raw data
• Ensure data integrity
• Remove or fix inconsistent data
• Add missing data

3.3. QC checks

Once the data is gathered, quality checks are performed to identify any errors or inconsistencies that might have crept in during the data-gathering phase. Practices such as data validation, removing inaccurate data, statistical checks, or even manual review could be employed.

3.4. Continuous monitoring

Data quality is not a one-time check but a continuous process. As more data is gathered, periodic audits should be conducted to ensure the quality is maintained and the data collection approach is still effective.

3.5. Feedback loop

Any data quality issues identified should be fed back into the QA process to refine the data collection approach, thus forming a feedback loop aimed at continuously improving data quality.

Why perform QA and QC?

Ensuring the quality of the data collected enables:

• Reduction of AI bias
• Reduced chance of overfitting/underfitting of the model
• A smooth training process
• Higher level of accuracy and performance 
• Fewer false positives and erroneous results

To learn more about how to improve the quality of your data collection process, check out this quick read.

4. Storing the data

Regardless of whether you choose in-house data collection or opt for the crowdsourcing approach, a well-thought-out storage plan is essential for safely housing the data you’ve gathered. This data serves as the foundation for training your machine learning model, and its security and accessibility are of utmost importance.

The following considerations can enhance your data storage strategy:

4.1. Assess your storage needs

Understanding your storage needs is crucial. If you’re dealing with sensitive or private data, you may require private servers fortified with high-security measures. Additionally, it’s wise to consider scalable storage solutions, as the size of your dataset may grow over time, necessitating more storage space.

4.2. Assess your storage provider

If you’re relying on third-party storage providers, it’s imperative to scrutinize their security protocols and data handling practices. Ensure that they meet your project’s specific requirements for scalability and security. Review their track record, compliance certifications, and customer reviews to make an informed decision.

4.3. Ensure multi-format backups

A robust backup strategy is essential for data security and protection. Multiple backups in various formats and locations can protect against data loss from hardware failure, data corruption, or other unforeseen events. Options for backups could include local server backups, external hard drives, and off-site or cloud-based backups.

5. Annotating the data

Data annotation is also a crucial step in preparing data for training. It involves labeling or tagging the data to make it machine-readable. For instance, for a facial recognition system, face images will be annotated by creating tags on different parts of the face in the image. 

Without the high-quality annotation, the data collected will be unreadable or useless for the model. Some data collection vendors offer this additional service. The different types of data annotations include:

• Text annotation
• Image annotation
• Document annotation
• Video annotation
• Audio annotation

To learn more about data annotation and what challenges you might face with it, check out this quick read.

6. Process documentation

In this stage, the project team should record the entire process of gathering or generating data to faciliate potential improvements.

6.1. Metadata and documentation

It’s crucial to meticulously document how the data was collected, the data sources utilized, any transformations applied to the data, and any other relevant metadata. 

This documentation serves as a roadmap for data provenance, ensuring that future researchers or data scientists can understand the dataset’s origins, characteristics, and any potential limitations. Good documentation enhances the data’s trustworthiness and reproducibility, thereby contributing to more robust and reliable machine learning models.

6.2. Review and feedback loop

Establish a system for periodically reviewing the data collection process, particularly if it’s an ongoing initiative. Make note of any inconsistencies, data quality issues, or bottlenecks that arise. A scheduled review enables you to make timely adjustments to your data collection methods, tools, or protocols, ensuring the ongoing relevance and quality of the data.

This feedback loop is essential for iterative improvement, helping you adapt to changing requirements or new insights that may emerge as the project progresses.

You can also check our data-driven list of data collection/harvesting companies to find the option that best suits your project needs.

To learn more about data collection, feel free to download our whitepaper:

Further reading

• Top 6 Data Collection Best Practices
• Top 5 Data Collection Trends for Data-Driven Businesses
• Top 3 Amazon Mechanical Turk Alternatives & Evaluation

If you need help finding a vendor or have any questions, feel free to contact us:

External Links

• 1. Sumanto. (2021). Model Naïve Bayes Classifiers For Detection Apple Diseases. researchgate. Accessed: 28/August/2023.