BigQuery now features a suite of augmented analytics Table-Valued Functions (TVFs) designed to automate complex data analysis at scale. Augmented analytics combines AI, ML and statistical methods to automate insight discovery and pattern explanation. These functions allow you to diagnose why metrics changed, uncover underlying trends and relationships across the data, and even isolate the true impact of business decisions.
These TVFs run directly where your data lives, which helps speed up analysis and reduces the need to export data into external tools. In addition, since these functions are compact and yield structured SQL outputs, they can easily be integrated as skills for AI agents, which easily enables automated, conversational data investigation workflows.
We are introducing six new augmented analytics functions in BigQuery, each created to address a specific analytical challenge:
|
TVF Function |
What It Helps You Find |
Real World Question It Answers |
|
AI.KEY_DRIVERS |
Identifies the top drivers behind an increase or drop in a metric between two time periods or groups. |
Why did revenue spike this quarter compared to last quarter? |
|
AI.CAUSAL_EFFECT |
Quantifies the impact of an action or event by comparing the observed results to an expected baseline. |
How much of the revenue lift came from our pricing update rather than organic growth? |
|
ML.CORRELATION |
Evaluates the direction and strength of the relationship between pairs of numeric metrics. |
Does increased user session duration correlate with higher lifetime customer value? |
|
ML.DETECT_CHANGE_POINTS |
Identifies specific dates or intervals where a metric experiences a shift compared to surrounding patterns. |
During which time periods did our platform latency experience persistent, structural shifts? |
|
ML.TREND |
Separates the underlying growth or decline from short-term fluctuations or noise. |
What are the underlying trends of my revenue over the past year, abstracting away the outlying spikes and drops? |
|
ML.SEASONALITY |
Discovers predicable repeated cycles across hours, days, weeks, months or quarters. |
Which days of the week consistently experience the highest server load? |
As we show in the next section, these functions can be easily chained together. The output of one function, such as a detected time window, can directly parameterize the next analytical step.
A step-by-step example of chaining insights
Consider a case where there is a shift in a metric, and you need to diagnose the underlying cause and measure the business lift.
To diagnose, we can chain ML.DETECT_CHANGE_POINTS, AI.KEY_DRIVERS and AI.CAUSAL_EFFECT using the Austin Bikeshare sample dataset (bigquery-public-data.austin_bikeshare.bikeshare_trips). This dataset contains historical trip volume and demographic data for the city’s bikesharing program.
Step 1: Detect change points
ML.DETECT_CHANGE_POINTS automatically identifies statistically significant structural shifts or level changes in your time-series data. While this example demonstrates the analysis in a single aggregate metric, this function is highly scalable and is capable of running across millions of individual time series.
To find these shifts, we run the following query across the daily baseline:
