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What is AI-Ready Data?


Before we define AI-ready data, it’s important to understand the key requirements it needs to meet:

1. Be easy for a data scientist (or an LLM) to interpret and reason about.

2. Be easy to query and feature engineer with. (Data should not require lots of prep)

3. To be accurate (so that predictions on the data are accurate)

These requirements form the foundation of what makes data truly AI-ready. Now, let’s explore how these requirements translate into specific properties of AI-ready data.

What is AI-Ready Data Exactly? 

Simply put, AI-ready data is structured, high-quality information that can be easily used to train machine learning models and run AI applications with minimal engineering effort.

It’s characterized by its compatibility with the best data modeling tools, a consistent format for historical and real-time data streams, and comprehensive metadata that ensures clarity and reliability for data scientists.

To fulfill the requirements of AI-ready data, it must possess the following properties: 

  1. Comprehensive metadata and documentation: This should cover, at minimum, the data schema and semantics. It’s essential for both human beings and LLMs to understand and start working with the data effectively. 
  2. Clean and well-structured data: This makes it easy to query and feature engineer. Schemas and dbt models are critical here, ensuring that data scientists and data science agents can compute on the data quickly and efficiently. The dbt models, in particular, aggregate the data up to different altitudes so data scientists can simply pick it up at the right level, rather than having to do any complicated aggregation themselves. 
  3. Clear lineage and validation: These are critical for ensuring data accuracy. It’s increasingly important that the full lineage is auditable, allowing companies to explain to their customers and auditors what decisions have been made by their AIs based on what data. 

Diving deeper into the concept, Snowplow’s Yali Sassoon recently described that AI-ready data has several important characteristics that make it particularly valuable to organizations implementing AI solutions:

  1. Ease of feature modeling: The data is structured in such a way that little effort is required to generate features for machine learning algorithms. This saves you time and resources in the data preparation phase.
  2. Consistency across platforms: The same data can be delivered to multiple data warehouses for historical analysis and real-time streams for immediate use. This consistency eliminates the challenge of transitioning from model training on historical data to using current data.
  3. Built-in data quality: AI-ready data is validated for both structure and semantics, giving you a high level of assurance about its quality. This is key to building reliable AI models.
  4. Comprehensive metadata and lineage: With this type of data, your data scientists have access to detailed information about the origin, transformation and meaning of the data. This transparency contributes to a better understanding and facilitates the development of accurate models.
  5. Compatibility with dbt models: AI-ready data works seamlessly with dbt models like those offered by Snowplow, so you can use the output directly for machine learning algorithms.

Together, these characteristics make AI-ready data more accessible and usable for data scientists and AI practitioners.

Why is AI-Ready Data Important?

Well, without it, it is unlikely that your company will ever be successful with AI.

Companies need to prioritize the creation and maintenance of AI-ready data. There are several reasons for this:

  1. Accelerated AI development: As mentioned earlier, AI-ready data helps your data scientists spend less time preparing data and more time developing and refining models. This acceleration is important, especially at a time when the race is on to deliver AI-powered solutions.
  2. Improved model accuracy: It’s simple – high-quality, well-structured data leads to more accurate AI models. Only when you use AI-ready data can your organization create more reliable predictive models and make more informed decisions.
  3. Streamlined MLOps: Consistency between historical and real-time data streams allows you to simplify the process of machine learning operations (MLOps). This seamless transition from model training to deployment can help you to deliver more efficient and effective AI implementations.
  4. Cost reduction: By minimizing the need for data preparation for your engineers, you can reduce the cost of your AI projects.
  5. Improved data governance: AI-ready data has comprehensive metadata and lineage information to help you improve your data governance. This also enhances auditability and transparency, which is crucial for explaining AI decisions to customers and auditors. 
  6. Future-proofing: Companies like Snowplow are already thinking about how to make their data Gen-AI-ready so that our customers are in the best position to adopt new AI technologies.

Currently, data scientists spend around 39% of their time preparing and cleaning data. What’s clear is that AI-ready data has the potential to reduce the time data scientists spend preparing data. 

Make your Data AI-Ready!

To summarize, AI-ready data is not just a buzzword. It’s a fundamental advantage for any business that wants to fully utilize the potential of AI.

By ensuring your data is structured, consistent, and rich in metadata, you can accelerate the adoption of AI in your organization, improve model accuracy, and streamline MLOps processes.

The field of AI will continue to evolve. It is the time to invest in AI-ready data so your organization is prepared for the new technologies of the future.

Whether you’re just getting started with AI or looking to enhance your existing capabilities, AI-ready data is a strategic move that will pay dividends in terms of efficiency, innovation, and, most importantly, competitive advantage.

Want to get started with AI-ready customer data? Get in touch with us today and we’ll show you how Snowplow’s next-gen Customer Data Infrastructure allows you to create and maintain the highest quality AI-ready data. 

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Adam Roche
Adam Roche

Content Lead

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