Phase 03 of the Data Science Lifecycle

Transforming Raw Data
into Model-Ready Foundations

We transform your raw data into clean, reproducible datasets as the basis for modelling.

Back to Overview Phase 02 Phase 04

Data preparation

The Art of Data Preparation

Data preparation takes up the largest portion of project work. We automate it with robust processes.

Data Cleaning

Systematic handling of missing values, duplicates, and inconsistencies.

Feature Creation

Deriving meaningful features based on domain knowledge.

Automation

Reproducible pipelines with error handling and quality checks.

RAW DATA

NULL 38%

?? 25%

dup. 18%

mixed 12%

Clean

Impute · Dedupe

Transform

Normalize · Encode

Analyze

Aggregate · Derive

FEATURES

quality 98%

coverage 95%

features 42

tests 100%

Data preparationreproducible · tested · version-controlled

Data science project approach

Our Approach

Cleaning & Harmonisation

Systematically addressing data quality issues across different sources.

Feature Engineering

Creating features that best support model learning.

Pipeline Development

Automated, versioned data processing workflows.

Quality Assurance

Integrated check mechanisms for consistently assured data quality.

Data science deliverables

Typical Deliverables

Prepared, model-ready datasets

Automated processing pipelines

Documentation of all transformations

Integrated quality checks