7 AI Data Collection Best Practices in 2024

The performance of any AI model is determined by the quality of its training dataset. With the growing interest in AI data collection (Figure 1), it is also important to know the best practices of the process to avoid data collection challenges.

This article explores 7 best data collection practices that organizations should incorporate in their process of obtaining relevant AI training data. Whether you are working with AI data collection services or gathering data by yourself, these best practices can be helpful.

Figure 1. Rising interest in AI data collection in the past few years

1. Understand the objectives of the project

It is important to articulate the requirements of the AI/ML model. Understanding what your model will do can help ensure you access data relevant to the scope of the project. You can consider the following points to understand what data will be required for which tasks:

• Whether the model will perform simple classification tasks such as yes/no, black/white, good/bad, cat/dog questions, or multi-classification tasks with multiple objects such as cats, dogs, birds, etc. 
• Whether the model will require numeric data for tasks such as product pricing.
• Whether the model will perform ranking tasks such as product ranking based on specifications and purchase history of the customer.

These considerations can give you a clear picture of what is required from the AI/ML model and which data needs to be collected.

2. Establish data pipelines and leverage DataOps

Almost every business activity generates data. How a company gathers, manages, and leverages that data makes a difference. A data pipeline is can help automate the flow of data from collection to processing. The helps with data collection in the following ways:

• It eradicates the need for manual data collection
• Ensures data is transformed to suit AI models
• Integrates data from various sources into a comprehensive dataset
• Offer scalability to accommodate ever-growing data volumes.

Sometimes when the data architecture is complicated, it can make the data pipeline more time-consuming. In this situation, DataOps can be established to enable employees to work with data in real-time and collaborate on data management.

Implementing Dataops involves a cultural shift. It emphasizes enhancing collaboration among data teams, ensuring reproducibility through consistent data environments, accelerating the flow of data with Continuous Integration and Delivery (CI/CD), and meticulously upholding data integrity and quality, ensuring AI models receive the best possible input.

3. Establish storage mechanisms

The following mechanisms of data storage can be used:

• Companies can use a data warehouse and store their data through the extract, transform and load (ETL) method. In this method, you know which data you will use, so you extract it, transform it and load it. However, with this method, sometimes it’s hard to know in advance which data will be useful in the future. This method works best when data security is the priority and only unstructured data is being managed.
• Data lakes can also be used in which both structured and unstructured data can be stored. This can be coupled with the ELT method, in which the transformation stage is done after the data is loaded. This enables the engineer to transform the data on-demand in the future. This method is better when real-time decision-making is critical, scalability is required, and the project involves big data.

4. Determine a collection method

Determining the right data collection method is also one of the most important steps of the complete process. The following methods can be used:

4.1. Custom crowdsourcing

Public crowdsourcing is a participatory method of data collection that involves working with a large group of participants. For example, to train a computer vision system for reading road signs, the system requires road sign image data to be trained. Through public crowdsourcing, the company can obtain these images from the public by providing some instructions and creating a sharing platform.

Click here to learn more about crowdsourcing.

If you need to compare the top crowdsourcing platforms on the market, you can checkout this guide.

4.2. Private sourcing

Private data sourcing or in-chouse method is used to collect data through an internal team. An example of private data sourcing can be surveying. This method is better for projects which require small datasets and do not have complicated models. This method of collecting data is also good for projects with higher privacy and security levels.

4.3. Customer data

If collecting data externally is not an option, businesses can use internal data that is generated by their customer base. This can be useful data for the business and is available for free. However, this can be challenging for SMEs or startups since they might not generate sufficient data. While gathering customer data or other data of sensitive nature, it is also important to follow legal regulations and look at ethical considerations.

4.4. Prepackaged data

Prepackaged data is a cheaper option to collect data and is easy to implement. However, this sometimes can create more complications for companies due to the lack of customization such data sets offer offers.

4.5. Automated data collection

If the required data is available online, automated tools can be an effective way of collecting data. The data collection process can be automated in the following ways:

• Web scraping
• Web crawling
• APIs

5. Evaluate collected data

Data quality is paramount to a successful AI/ML model. Therefore, an organization should consider the following points to ensure that the data quality is sufficient and that the data can be trusted.

• If humans are collecting data, evaluate how tangible it is. This can be done by analyzing a subset of data and identifying how often errors are made.
• Evaluate the data transfer process for any technical issues and the impact of those issues. Search for data duplications, server errors, storage crashes, cyberattacks, etc.
• Analyze to see if any data is left out and how critical is the number of the data omitted.
• Ensure that the data is balanced. Collected data should cover all required outcomes of the model. For example, while collecting data for a supplier evaluation system, the dataset should include a balanced amount of good supplier and bad supplier data.

You can also implement data proprocessing practices improve the collected data and make it ready to be fed into the machine learning model for training.

6. Collect concise data

While gathering data, It can be tempting to collect all the data that is available. However, this can cause unnecessary complexity in your AI/ML model. It is important to reduce the horizon of data collection to specific and concise data that is aligned with the goals of the AI/ML model. You can follow the following practices make your dataset more accurate.  

6.1. Attribute sampling

For instance, for a forecasting model which predicts which customers make more purchases, the bounce rate and the age of the customer can be relevant; however, credit card details can be irrelevant. This approach is called attribute sampling, where the data is sampled based on its attributes.

6.2. Record sampling

This is another approach to making the data more concise and accurate. In record sampling, the data with missing, erroneous, or doubtful values is removed from the collected dataset to improve the accuracy of the trained model.

7. Documentation and metadata

When gathering data to train AI models, the quality, characteristics, and nuances of your data play a massive role in the final performance and applicability of the model. Properly documenting data and maintaining metadata is like keeping a detailed lab notebook in scientific experiments. You can consider recording the following information regarding the data:

7.1. Source information

• Why it matters: Knowing where your data came from helps assess its credibility, reliability, and possible biases. For instance, data from a reputable research institution may be viewed differently than data scraped from random websites.
• What to document: Original sources, collection/generation methods (e.g., web scraping, surveys, human-generated), any third-party vendors used, and date of acquisition.

7.2. Preprocessing steps

• Why it matters: Preprocessing can significantly alter data. Without knowing what was done, it’s challenging to reproduce results or diagnose issues.
• What to document: Any filtering, cleaning, normalization, transformation, feature extraction, or augmentation. The exact methods, parameters, and tools/libraries used should be noted.

7.3. Data structure and description

• Why it matters: This helps anyone using the data understand its content and format quickly.
• What to document: Describe each feature (column) in the dataset, data types, units (if applicable), and any encoding or normalization applied.

7.4. Known biases or limitations

• Why it matters: No dataset is perfect. Documenting known biases allows modelers to be aware of potential pitfalls or areas where the model might perform poorly.
• What to document: Any over-or under-represented groups, areas with sparse data, known errors, or any other limitations.

7.5. Sample data

7.6. Data Collection procedures

• Why it matters: The method of collection can introduce biases or errors.
• What to document: Were the data points collected randomly? Was there a specific sampling strategy? Was there any incentive given to participants in a survey? All these details matter.

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

For more on data collection, feel free to download our whitepaper:

Further reading

• Quick Guide to Data Collection
• Data Extraction: What it is, Why it matters & Key Features

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