Get started with Warehouse-Native Experimentation Analytics

Optimizely Warehouse-Native Experimentation Analytics enhances your experimentation results by evaluating experiments against data that lives in your data warehouse. This ensures your data warehouse remains the single source of truth, keeping your data secure and centralized. With Analytics’s warehouse-native architecture, you can seamlessly use existing data structures, letting you efficiently and effectively model your data to support your experimentation analytics needs.

This article contains the steps you must perform before you dive into Warehouse-Native Experimentation Analytics: connect your data warehouse, create necessary datasets, and have experiments in Optimizely Feature Experimentation or Web Experimentation. 

Optimizely Analytics supports Snowflake, BigQuery, Databricks, and Amazon Redshift. 

Optimizely Analytics uses the service account to query the warehouse. The schema is used to improve performance by caching common computations, such as the drop-down list of unique values in a column. This substantially improves the warehouse cost and performance.  

Setting up your data warehouse for analytics is a crucial step in ensuring seamless data integration and analysis. This process involves configuring your warehouse to efficiently handle data operations and connect with Optimizely Analytics. Learn how to configure the following warehouses:

Using real-time or batch processing methods, you can send decision and conversion events to your data warehouse. Integrating experimentation decision events into your data warehouse is essential for centralizing your data, enabling more robust analytics, and data-driven decision-making. 

Create a connection to your data warehouse. The following warehouse options are available:

Analytics creates materialized tables in the data warehouse, which contain intermediate results for improving performance.

Analytics uses three datasets to generate insights into experimentation: a decision dataset, an actor dataset, and an event dataset. You must create these three datasets and link them together.

A decision dataset effectively populates the experiments list in Optimizely Analytics. It serves as a foundation for analyzing experimentation decisions and outcomes.

Learn how to create datasets in Optimizely Analytics.

You can also analyze any experiments run outside of Optimizely by connecting the data from your own data warehouse. Whether your experiments are conducted through in-house systems or other feature flagging tools, you can evaluate the results with Optimizely’s Stats Engine by uploading or connecting third-party datasets to Optimizely Analytics.

Optimizely Analytics must be able to distinguish between Optimizely-sourced decision datasets and external decision datasets, and treat any non-Optimizely labeled decision datasets as originating from your data warehouse.

Once your third-party decision dataset is connected and configured, you can use it in a similar fashion to Optimizely-sourced datasets to create an Experiment Scorecard, view experiments, and explore results.

To label Optimizely-sourced decision datasets, 

You must create an event dataset to calculate experiment success metrics in Optimizely Analytics. 

Events represent actions taken by users, such as clicks, page views, or purchases. By tracking these events, you can gain insight into what users are doing within your product. By examining sequences of events, you can analyze user journeys and identify patterns, bottlenecks, or drop-off points.

If the data you want to evaluate your experiments against lives in one table, create a source dataset. If you want to create a logical combination of multiple warehouse tables exposed as a single dataset, often used to unify event tables by type, create a union dataset.

Actors represent the users themselves, and datasets about them can include attributes like demographics, preferences, or account details. This helps in segmenting users for targeted analysis.

If you have a users table, you can use it to create an actor dataset to track unique visitors to experiments and link your Users table to your decision dataset using the user's unique identifier. 

If you do not have a users table, you can use an identifier column to create a column actor dataset, such as the visitor_id column in an event table within the existing decisions dataset.

When your datasets are ready, link them by creating the following relationships: 

After configuring datasets and metrics, you can gain insights and analyze experiments.