Most AI/ML projects fail because the data isn’t ready.
When your data lives in Postgres (operational), Snowflake (warehouse), BigQuery (warehouse), and Databricks (processing), you can prepare AI-ready data that drives business value.
The 4 AI-ready data requirements that matter
1. Data Quality
AI-ready data starts with clean data that has no missing values or outliers that could skew model results. It requires consistent data where the same definitions apply across all sources, ensuring models learn from reliable patterns. Most importantly, it needs complete data where all features are present, avoiding gaps that reduce model accuracy.
2. Data Preparation
Effective data preparation includes feature engineering to create useful features that capture meaningful patterns. It requires data transformation through normalization and scaling so features are on comparable scales. Finally, it needs data sampling to create balanced datasets that prevent models from overfitting to dominant classes.
3. Data Access
AI-ready data must be accessible quickly with query performance that doesn’t slow down model training. It needs scalable access that can handle the volume of data required for effective machine learning. Most critically, it requires secure access with proper governance to ensure sensitive data is protected.
4. Data Lineage
Data lineage tracks the complete data flow from source to model, ensuring you understand how data was transformed. Version control for data enables reproducibility so you can recreate model results exactly. Documentation makes data understandable so team members can use it effectively without your intervention.
What to build first (week 1)
Start with a simple AI-ready data pipeline that extracts data from Postgres, Snowflake, and BigQuery. Clean the data by handling missing values and outliers that could skew results. Engineer features that capture meaningful patterns for your use case. Transform data through normalization and scaling so features are comparable. Finally, train models in Databricks using your prepared data.
Once you have the basics working, add data versioning to track different versions of your datasets, model versioning to track model iterations, monitoring to track model performance over time, and retraining processes to update models regularly as new data arrives.
Why most AI/ML projects fail
Most AI/ML projects fail because data quality is poor with missing values and outliers that corrupt model training. Features are weak and not predictive, meaning models can’t learn meaningful patterns. Data is inaccessible due to slow queries that make experimentation impractical. Most critically, lineage is missing so you can’t reproduce results or understand how data was transformed.
When you prepare AI-ready data, you can build better models because quality data enables accurate learning. You can create better features that are actually predictive of your target outcomes. You can access data fast with performance that supports rapid experimentation. Most importantly, you can reproduce results because lineage tracks exactly how data was prepared.
The hidden cost of unprepared data
When data isn’t AI-ready, models are inaccurate because poor data quality leads to unreliable predictions. Features are weak and not predictive, meaning models can’t learn useful patterns. Access is slow with poor performance that makes experimentation frustrating. Most critically, results aren’t reproducible because there’s no lineage tracking how data was prepared.
AI-ready data means models are accurate because good data quality enables reliable predictions. Features are strong and predictive, allowing models to learn meaningful patterns. Access is fast with good performance that supports rapid iteration. Most importantly, results are reproducible because lineage tracks exactly how data was transformed.