I still remember the days in the early 2010s, when the term „big data“ was widely discussed in the tech industry. Companies were encouraged to collect as much data as possible, seeing it as a key resource (or the new oil as we called it those days) for the digital economy. However, focusing only on quantity led to problems. Many organizations now have large amounts of data but struggle to use it effectively because they do not know where it comes from, how it is structured, or how reliable it is. These issues become even more serious when training AI and ML models since bad data can lead to incorrect predictions and errors that are hard to fix.
This is where data products come into play. I know, there are a lot of definitions around and there is not one single truth. With my learnings from the past decade, I like to go with this approach:
Unlike simple datasets or data pipelines, data products combine different elements to create structured, reliable, and easy-to-use data. A well-defined data product has at least those three key aspects:
- Data Sets: These can be tables, views, machine learning models, or real-time data streams. The data may be raw or processed from different sources. A data product should always publish its data model so that users understand what it contains.
- Domain Model: This layer simplifies the technical details and presents data in business-friendly terms. It also includes important calculations, metrics, and transformation rules to make the data easier to use.
- Access and Governance: A good data product provides controlled access through APIs and visualization tools while enforcing security rules. This ensures that data can be used safely and efficiently.
In addition, a Data Products Catalog helps users find and understand available data products. It documents key attributes and metadata, allowing organizations to adopt a self-service approach to data access.
Why Data Products Matter for AI and ML
AI and ML models need high-quality, well-structured, and meaningful data. While models are trained using historical data, real-world AI applications depend on accurate inference—using models to make predictions on new data. Providing the right context to these models is a major challenge.
For AI/ML to be effective, it needs up-to-date information, not just historical data. This is why data products based on real-time data streaming are so important. By structuring and enriching data properly, organizations can ensure that AI/ML models get high-quality, trustworthy data for better decision-making.
In the next part of this series, we will discuss the challenges of building and maintaining data products for AI and ML, especially when working with real-time data. We will look at issues like data governance, integration difficulties, and the role of data engineering in AI-driven businesses.g with real-time data. We will examine issues like data governance, integration complexities, and the evolving role of data engineering in AI-driven enterprises.