Data Preparation Guide: Getting Your Data AI-Ready

The Data Preparation Reality

Data preparation consumes 60-80% of AI project time. It's not glamorous, but it's essential. Quality data preparation is often the difference between project success and failure.

The Data Preparation Pipeline

1. Data Discovery

Understand what data exists.

• Inventory data sources
• Assess data access
• Evaluate quality
• Identify gaps

2. Data Collection

Gather data for your use case.

• Extract from sources
• Handle different formats
• Manage access and security
• Document lineage

3. Data Cleaning

Fix data quality issues.

• Handle missing values
• Remove duplicates
• Fix inconsistencies
• Correct errors

4. Data Transformation

Shape data for modeling.

• Normalize/standardize
• Encode categories
• Handle outliers
• Create derived fields

5. Feature Engineering

Create predictive features.

• Domain-driven features
• Aggregations
• Time-based features
• Interaction features

6. Data Validation

Ensure quality and appropriateness.

• Quality checks
• Distribution analysis
• Bias assessment
• Documentation

Common Data Issues

Missing Values

• Understand why data is missing
• Choose appropriate handling (impute, remove, flag)
• Document decisions

Duplicates

• Define what constitutes a duplicate
• Decide which records to keep
• Track deduplication logic

Inconsistencies

• Standardize formats
• Resolve conflicting values
• Create mapping rules

Outliers

• Investigate before removing
• Domain knowledge matters
• Document handling decisions

Feature Engineering Tips

Time-Based Features

• Day of week, month, quarter
• Time since event
• Rolling windows
• Lag features

Aggregations

• Counts and sums
• Averages and percentiles
• Min/max values
• Distinct counts

Text Features

• Word counts
• Sentiment scores
• TF-IDF
• Embeddings

Categorical Encoding

• One-hot encoding
• Label encoding
• Target encoding
• Embeddings

Best Practices

1. Understand the data: Explore before transforming
2. Document everything: Future you will thank you
3. Automate pipelines: Manual prep doesn't scale
4. Validate continuously: Catch issues early
5. Preserve raw data: Keep the original
6. Version your work: Track changes

Data preparation is foundational. Invest the time to get it right.

Next Steps

For data preparation tools, see Pandas documentation and dbt transformation documentation.

Ready to prepare your data for AI?

• Explore our Data Analytics services for data preparation support
• Contact us to discuss your data readiness needs