This approach was suggested by Marco Tognon, and it deserves a round of applause! Combining the power of BigQuery, Dataform, and automated models for anomaly detection in GA4 data is a brilliant example of data analytics in action.
How Does It Work?
1. Gathering Data. Start by creating a table to store events (e.g., page_view). This ensures you're working with clean and structured data.
2. Analyzing Trends. Aggregate the data by day or week to calculate key metrics like event counts, unique users, and averages. This step helps reveal overall trends.
3. Training a Model. Use BigQuery ML to predict "normal" behavior. Simple models like linear regression or ARIMA are excellent choices for time series data.
4. Spotting Anomalies. Compare actual values with predictions. Significant discrepancies indicate potential anomalies.
5. Automating the Process. Leverage Dataform to schedule regular updates and checks. For instance, a script can automatically analyze data daily and send alerts if anomalies are detected.
This method saves time, maintains data quality, and ensures you’re on top of critical metrics. A fantastic approach that's bound to inspire others!
If you work with GA4 to BigQuery exports, be sure to check out my SQL cheat sheet.
