Data Processing

Data Processing

However, modern data processing goes far beyond that. It involves many additional steps such as transformation, extraction, conversion, and enrichment.

As a data practitioner, you may not always imagine the full scope of what data processing entails. Most people initially think of simple tasks such as removing duplicate values or deleting null entries.

In reality, modern data processing is a comprehensive and systematic workflow. It is not limited to “cleaning” but also includes several crucial steps, such as:

• Data transformation: standardizing formats, converting data types, and unifying measurement units.
• Splitting & merging data: extracting subfields from raw data or combining multiple sources together.
• Conversion & encoding: transforming data between systems and encoding categorical values into numbers for analysis.
• Data enrichment: adding external information to increase analytical value.
• Advanced processing: detecting anomalies, normalizing data according to statistical standards, and creating new features for machine learning.

In other words, data processing is the vital bridge between raw data and actionable insights. It ensures that data is clean, consistent, and ready for analysis, reporting, or model training.