What is AI, Machine Learning, and Data Science?

Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS) turn high-quality data products – often built from event streams – into insights, predictions, and decisions.

When your data comes from Event Sourcing, you start with a unique advantage: a complete, ordered history of every meaningful change in your system. Instead of working with static snapshots, you work with time-ordered facts – perfect for building models, detecting patterns, and performing reliable analysis.

Why Event Sourcing Is a Perfect Fit

Event Sourcing provides:

• Rich context: Events are written in the language of the business, so your features carry real-world meaning.
• Immutable history: Nothing is lost – you can always reproduce the exact dataset used for training or analysis.
• Complete timelines: Every change is timestamped, making it ideal for time-series analysis and temporal data mining.
• Statistical power: Seasonality, trends, and correlations become visible because no data is overwritten.
• Explainable features: Even if a neural network remains a black box, the origin of each feature is clear and traceable.

From Events to Features

In traditional systems, analytics often start with pre-aggregated tables. With Event Sourcing, you derive features directly from the raw events, ensuring accuracy and flexibility.

For example, in a library domain:

• Borrowing patterns – detect seasonal trends in popular genres.
• Member history – calculate punctuality rates or overdue probabilities.
• Title popularity – forecast future demand based on past loans.
• Fee patterns – analyze correlations between book type, due date, and late returns.

Because you always have the full sequence of events, you can try new feature engineering approaches at any time – even months or years after the data was first recorded.

Beyond Big Data Hype

You don't need a massive, complex "big data" stack to get started. Event Sourcing works with simple pipelines: events feed projections, projections feed models.

You can start small – for example, training a basic classifier to predict overdue books – and evolve to more advanced models as your needs grow.

Explainable AI and Domain Understanding

A major challenge in AI is understanding why a model makes a prediction. While Event Sourcing doesn't magically make deep models transparent, it ensures that:

• Every feature can be traced back to a specific sequence of events.
• Domain experts can validate whether the features make sense in business terms.
• Models can be audited and reproduced using exactly the same historical data.

This tight link between data and domain language leads to more trustworthy, interpretable results.

Next up: From Events to Insights – how Event Sourcing, Data Mesh, and AI connect into a continuous, end-to-end path.