Why Data Transformation Matters in AI and ALCHIMIA

Jun 17, 2025 | blog

When it comes to AI and machine learning, often the focus is on the models and algorithms. But the models and algorithms only work well if the data they use is clean and consistent. This is why data transformation is crucial for ensuring models are trained and perform well.

In the ALCHIMIA Project, which uses AI and Federated Learning to improve the metallurgy industry in Europe, data transformation is especially important because the data comes from many different factories, machines, and systems in different formats and structures.

What is Data Transformation?

Data transformation means changing raw or messy data into a clean, structured format that AI systems can use. It usually happens after data is collected (extracted) and before it is stored or used (loaded) as part of the larger ETL process.

This step can include:

  • Cleaning: Removing duplicate entries, fixing mistakes, and filling in missing values.
  • Normalization: Making sure all the data follows the same format or units.
  • Enrichment: Adding more useful information to the data.
  • Structuring: Organizing unstructured data into a usable layout (like turning plain logs into tables).

Why AI Needs Transformed Data

AI models need data that is:

  • Consistent: Same units, labels, and structure.
  • Complete: No important information missing.
  • Clean: No errors or irrelevant details.

If raw data is used without fixing it, AI models might give wrong results or not work at all. Data transformation makes sure the data is good enough to use.

The Challenge in ALCHIMIA: Data from Many Sources

ALCHIMIA uses federated learning with data coming from many different factories. Each one has its own setup:

  • Different types of sensors and machines
  • Various ways of recording data
  • Local naming styles
  • Unique workflows

This means that the same type of data (like temperature or speed) might be recorded in different ways. Some might use Celsius, others Fahrenheit. Some might record every second, others every minute. This inconsistency has to be fixed so that AI can learn from all the data together.

How Data Transformation Helps ALCHIMIA

  1. Making Data the Same Across Factories
    • It standardizes data so AI models can understand it, no matter where it came from.
  2. Preparing Data for AI Models
    • Clean and structured data is needed to train models that improve production and reduce waste.
  3. Reducing Errors
    • When the data is clean, AI outputs are more reliable, which is important in industries where mistakes can be costly.
  4. Using Tools to Save Time
    • Open-Source tools like Python, Pandas, and PySpark help automate the transformation process.

Data transformation is a necessary process in ALCHIMIA as it helps bring together different types of factory data so that the AI models and overall Federated Learning platform can effectively use the data in a standardized format, regardless of which factory/plant it comes from.