---
title: Quick start guide for Administrators
slug: analytics/docs/7n_X-quick-start-guide-for-administrators
docTags: 
createdAt: 2026-07-20T15:44:45.060Z
---

This quick start guide walks you through the end-to-end setup—from creating a service account and accessing the platform to connecting your data warehouse, defining datasets, and building your first exploration. See [Terms and Concepts](docId:_qYUzPHaq-0idJcVCKWLP) for explanations of these terms.

## Log in to Optimizely Analytics

1. Access the Optimizely Analytics web application [URL](https://analytics.optimizely.com/) on any browser, enter your email, and click **Next**.

   ![log in-1.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/hsG85IlP3DkFocIrD3Hz7-20260720-144954.png)
2. Enter the password, verify your account, and sign in.

   ![log in-2.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/WF7NXuuJWGnwcqkfavH9y-20260720-144954.png)

:::hint{type="info"}
**Note**

If you encounter difficulties logging into Optimizely Analytics, write an email to [Optimizely Support](#).
:::

### Create a service account with read/write schema

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.

1. Create a service account for Optimizely Analytics in your data warehouse of choice ([Snowflake](https://docs.snowflake.com/en/sql-reference/sql/create-account?), [BigQuery](https://docs.cloud.google.com/iam/docs/service-account-overview), [Databricks](https://docs.databricks.com/aws/en/admin/users-groups/service-principals), or [Amazon Redshift](https://docs.aws.amazon.com/redshift/latest/mgmt/using-service-linked-roles.html?utm_source=chatgpt.com)) to query the warehouse.
2. Add a schema with read/write access granted.
   - [Create a schema in Snowflake](https://docs.snowflake.com/en/sql-reference/sql/create-schema?utm_source=chatgpt.com)
   - [Create a schema in BigQuery](https://docs.cloud.google.com/bigquery/docs/schemas?utm_source=chatgpt.com)
   - [Create a schema in Databricks](https://docs.databricks.com/aws/en/sql/language-manual/sql-ref-syntax-ddl-create-schema)
   - [Create a schema in Redshift](https://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_SCHEMA.html?utm_source=chatgpt.com)
3. Note the database and schema name for [enabling materialization](https://app.archbee.com/docs/QF9Vm8TZ7tcCbAlJZKmrf/V1bCdnHVWF0iA32vvysMA#Hqh8I) in Optimizely Analytics later.

### Configure your data warehouse for Optimizely Analytics

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:

- [Configure your Snowflake warehouse](docId\:jPv3uImEXIOTMVMwDULki)
- [Configure your BigQuery warehouse](docId\:qDEt1Hu7bvl9VTNgMFPKE)
- [Configure your Amazon Redshift warehouse](docId\:VLmPrPguUuEctczz-pYvh)
- [Configure your Databricks warehouse](docId\:NIz-GR2cb2g6sBBZGbQy_)

### Send Optimizely data to your warehouse

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.

- [Send data to your Snowflake warehouse](docId\:FnL0XJcnpU1JvDr-n2s8z)
- [Send data to your BigQuery warehouse](docId\:QdXkVBwnKEe7WzB8ItsJW)
- [Send data to your Databricks warehouse](docId\:wvrcvLQQS6DYaPLwdQfD2)
- [Send data to your Amazon Redshift warehouse](docId\:i_lcvX7Lp-KSQNO7wFsO8)

### Create an application and connect your data warehouse

1. Create an application within your organization for storing data and generated analyses.
2. Create a connection to your data warehouse. You can also [connect to multiple warehouses ](docId\:F1Zq12lgmBGj1QZ46nQIX)and select one of them to be the primary connection. The following warehouse options are available:
   - [Connect your Snowflake warehouse](docId\:mKe0v_HO9CGzosTnMlIb7)
   - [Connect your BigQuery warehouse](docId\:tP6Tsv7p3GLwfgYXTpzfd)
   - [Connect your Amazon Redshift warehouse](docId\:Afpi9xOIDc1xpNwWwIMYv)
   - [Connect your Databricks warehouse](docId\:rZs3X3dB0gipGOxzD81jQ)

### Enable Materialization

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

1. Go to **Settings** > **General Settings** > **Materialization** and enable the feature.

![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/hrjhAYGVzL-lGxNR3tfo8_opti-qsg-1.png)

2. Configure the following fields:
   - **Database** – The database name in the data warehouse where the materialized tables are created.
   - **Schema** – The schema's name in the database where the materialized tables are created.
   - **Refresh Cron Schedule** – The refresh periodicity of the materialized tables using the cron syntax. The recommended periodicity is **daily,** so the schedule is 0 0 \* \* \*.

## Create datasets and establish relationships between them

You must create user and event datasets and a relationship between them before creating explorations in Optimizely Analytics.

:::hint{type="warning"}
**Important**

To keep your initial configuration focused and manageable, avoid importing every available table into Optimizely Analytics at the start. Instead, bring in only the essential datasets—users and events—and use them to create a Daily Active Users (DAU) chart. This streamlined approach ensures clarity and sets a strong foundation for further analysis.
:::

### Create event datasets

Before you create a dataset, you must understand whether your events are represented by one or many tables in your warehouse. These categories are two common possibilities, but it is also possible that you may not fall under any of these categories.

Create a [source dataset](docId\:M4N30MMf6yfYkjN1oV0Um) if the data you want to evaluate your experiments against lives in one table. If multiple tables represent your event, you can create a [union dataset](docId:0IZUnYEzdKOEnGasY8e0A).

:::hint{type="info"}
**Note**

If you use **Snowplow**, your events are stored in a single table. If you use **Rudderstack** or **Segment**, your events are stored in multiple tables. If you do not use any of these, click Help Get Help to contact Analytics. Analytics supports a variety of setups, and this document covers common setups, but others are supported.
:::

### Create actor datasets

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 ](https://app.archbee.com/docs/0j9UV-86jfmNAUBPH1dpK/bgCoYYaXYFRFO0c4zHAbZ#Xe_X1)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](../../analytics/work-with-data/datasets/column-actor-dataset.mdx), such as the **visitor\_id** column in an event table within the existing decisions dataset.

### Create a relationship between the event and actor datasets

Creating a relationship between datasets tells Analytics how to join these tables together.

1. Go to **Related Datasets > + Add Related Dataset**.
2. Select the event dataset – **ProductEvents**.
3. Select **one to many** for **Cardinality** because one user will have multiple events.
4. Select the columns. In the **Users** table, select the **id** column — this is usually the primary key. In the Events table, choose the column representing the ID of the user who performed that event – here, **user\_id**.
5. Click **Save**.

## Build an exploration

When your datasets are ready, you can build an exploration using them. You can manually create an exploration or ask Mark to [generate an exploration](docId\:C2Yn91DAeLD5PuVY69gA9) for you.

1. Go to **Settings** **>** **Defaults** and select the **Users** dataset for the **Actors dataset** field, and for the **Event Stream** field, choose **Product Events**. When you set these defaults, users are populated by default as the selected actor for this measure. Now, you are all set to build your exploration. This should be done before you create an exploration.

   ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/rT0qDXElPyOcgukGqe2X8-20260720-144954.png)
2. Click **+ >** **Event Segmentation**.
3. Select the **Count of unique actors that performed event** measure.
4. Click **Select Events** and choose **Play Content**.
5. Click **Run** to display a chart in the visualization window.
6. Name the exploration and click **Save**.

[Learn more about Explorations in Optimizely Analytics](docId\:u2tlXpjocdzP6-_-Odi10).

## Performance guidance

The following techniques are various approaches you can take to ensure that Optimizely Analytics runs at an optimal cost and performance profile:

1. Ensure your events/conversions table is clustered by event date (not time), and event type (the same column selected in the **Semantics** tab of the dataset in Optimizely Analytics), in that order.
2. (Experimentation Analytics only) Check if the warehouse is clustered by the **experiment ID** column and decision date (not time) if it has a separate decisions table.
3. Ensure that you have created a new schema in your warehouse, give Optimizely Analytics read and write access to it, and then enter the name of that schema in the Optimizely Analytics app settings, in the **Materialization** section. This enables the materialization of repeated queries, which is a large cost/performance boost.
4. Ensure your warehouse instance size is appropriate.
5. Consider creating a new warehouse for Optimizely Analytics, if this same warehouse instance is also used by other workloads, so that Optimizely Analytics queries are isolated from other workloads.