A Guide: What AI Data You Need And How to Source It?

Since its widespread adoption, AI is transforming industries like healthcare, retail, entertainment, and digital marketing. 

From enhanced data analysis to automation, AI makes it possible to personalize solutions, save time, and optimize resource use within these industries. However, there’s a catch!

Without quality training data, most AI models are ineffective. 

Moreover, most AI models require a great deal of unbiased data, yet acquiring vast amounts of data is costly and challenging. That’s why we’ve prepared this guide, breaking down what data you need apart from training data and how to source AI data.

What AI Data You Need

Each AI model has unique data requirements and serves a specific purpose. Nonetheless, in AI model development and advancement,  most AI datasets fall under these three AI data categories:

(a. Training Data

Training data consists of real-world examples or scenarios pre-processed or prepared to help an AI model adjust its settings or parameters, helping it make accurate decisions or predictions.

While some AI models rely on labeled data to make accurate predictions, some are programmed to identify hidden relationships, structures, or patterns from unlabeled data. 

Overall, training data makes up around 60% to 80% of the total data you need to develop an AI model. And, its quality directly impacts the efficiency and accuracy of your model. 

(b.Validation Data

While training the AI model, you need validation data to assess and monitor the performance of the model. You don’t want to train the AI model only to realize that it has been memorizing the training data you feed it. 

So, during the training phase, test and measure the AI’s performance on the validation data. If the model performs well, this suggests it is learning correctly. Else, optimize the model’s learning settings. 

(c.Test Data

Finally, you need test data to evaluate the general ability of your model after it’s gone through the training and validation phase. Unlike training or validation data, test data is usually kept separate and secret throughout the AI development process.

Like validation data, test data is usually 10% – 20% of the total data used to develop a model. The data should help you know the AI model’s real-world performance metrics and the presence of common machine learning pitfalls like overfitting. 

How to Source AI Data

Before getting data for AI, you must define the purpose and requirements of your AI model. This step is critical because AI models employ a range of learning methods, requiring varied data types and formats.

By referencing the requirements of your AI project, you should determine a suitable source among these ones. We are going to focus on popular and cost-effective sources of AI data.

1. Utilize readily available datasets

Due to the costly nature of AI data, established organizations provide free datasets to accelerate AI advancement, foster collaboration, and promote transparency.

Companies like Google have the resources to collect and process AI data for various purposes. Use the Google Dataset Search platform to browse AI research, academic, and government datasets.

Other platforms to explore include UCI Machine Learning Repository, Kaggle, and other government data portals. 

Moreover, you can explore existing data within your company, like sales data, operational logs, or customer records. 

1. Scape websites or use APIs to access web data

Need niche datasets or real time data? Use automated scripts to extract data from select websites. Whether you are a beginner or techy, there are both code and no-code web data extraction solutions.

Some websites or platforms, such as social media sites, avail APIs (Application Programming Interfaces) to ease data extraction. 

With the help of APIs, you can extract both structured and real-time data with little to minimal hiccups. However, ensure to assess a site’s robots.txt file or terms of services to know what data you are not allowed to access.

1. Purchase commercial datasets

Besides open-source or publicly available datasets, there are commercial datasets. Even though they are usually ready for use, you must pay or partner with the owners to gain access. 

Besides getting a pre-built domain-specific dataset from a reputable provider, you can also have the provider curate the data from scratch. For this, you are to provide documentation specifying your AI project’s requirements. 

Then, the commercial provider would collect, clean, structure, and validate the dataset, reducing the need for preprocessing and saving you time.

If your model requires real-time data, you can also let the provider handle regular access and updates of data streams, keeping your models up-to-date and relevant. 

1. Generate synthetic data

At times, specific AI data may be sensitive, rare, or expensive. This is where synthetic data comes in. 

Using simulation models or AI-based data generators, you can mimic the traits and distribution of real-world data. This enables you to train robust AI models even when the data is so sensitive, especially in the medical space. 

For instance, simulation models are capable of generating virtual medical records, making it possible to create AI models capable of detecting chronic diseases or helping with finding a cure.

Like the rest of the data sources, synthetic data generation techniques are scalable and cost-effective. Moreover, they are privacy-friendly and enhance model diversity. Even so, you must always prepare AI data despite your select source. 

Why Data Preparation Matters in AI

1. Adhere to specific objectives and ensure data quality

Despite the data source, the dataset in use must match or fit the specific needs of your project, including data quality and volume. Remember, data quality directly affects the performance of a model. 

1. Improve data compatibility

AI data comes in multiple formats. It is up to you to standardize, clean, and validate the data to resolve discrepancies and ensure it matches the data requirements of your model. 

1. Address bias and ethical concerns

To have a fair and ethical AI model, you must conduct a data diversity audit and apply fairness constraints and algorithms. 

Working with biased data is likely to reinforce stereotypes, amplify social inequalities, or perpetuate discrimination, leading to ethical issues, and loss of public trust or business opportunities. 

Closing Words

Unlike curating data for scientific research, education, or finance auditing, you need to curate three primary data categories for AI development and advancement — training, validation, and test data. 

With this guide, you now understand why we need these data categories and when to use them. You are also in a position to acquire relevant AI data based on your objectives. 

As you proceed to define your AI project needs and retrieve the needed data, remember to always prepare and audit your data before proceeding to train, validate, and test the AI model.