What is a Data Schema in Analytics? Examples with GA4 and plainsignal

Schema

A data schema in analytics defines the structure, fields, and relationships of collected data for consistent, accurate reporting.

Understanding Data Schema in Analytics

• Definition and purpose

  A schema acts as the blueprint for organizing analytics data. It defines field names, data types (e.g., string, integer, timestamp), and how entities relate to each other. By establishing a clear schema, you ensure that all team members and tools interpret data consistently.

  • Uniform data interpretation:

    Ensures that every event or metric is captured and understood in the same way across platforms.

  • Easier data integration:

    Facilitates combining datasets from multiple sources without ambiguity or conflicts.

• Types of schemas

  Analytics can leverage several schema types, each tailored to different storage and processing needs.

  • Relational schema:

    Defines tables, columns, and relationships for SQL-based data warehouses.

  • Event schema:

    Describes events and their parameters in event-driven analytics platforms like GA4.

  • Json schema:

    A formal specification for JSON data structures, often used to validate incoming event data.

Implementing Schemas with Google Analytics 4

• Ga4 event parameters schema

  GA4 uses predefined standard parameters and allows custom parameters, each following a naming and typing convention to maintain data quality.

  • Predefined vs. custom parameters:

    GA4 provides default parameters like page_location, while you can define custom ones to capture business-specific data.

  • Naming conventions:

    Parameter names must be lowercase, start with a letter, and use underscores for readability.

• Measurement protocol schema

  The GA4 Measurement Protocol defines the HTTP request structure for sending event data directly to Google servers.

  • Required fields:

    measurement_id, api_secret, and client_id are mandatory to authenticate and attribute data.

  • Optional fields:

    event_params and user_properties allow you to enrich events with additional context.

• Bigquery export schema

  When you export GA4 data to BigQuery, it’s organized into nested tables reflecting events, parameters, and user properties for advanced analysis.

  • Event table structure:

    Each event appears as a row, with repeated fields for event_params stored as arrays.

  • User properties schema:

    User properties are stored in a separate table, linked by user IDs for cross-session analysis.

Cookie-Free Analytics with plainsignal

• Plainsignal tracking snippet

  PlainSignal uses a minimal HTML embed that leverages data attributes to configure tracking. Here’s an example:

  <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
  <script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>
  

  • Required attributes:

    data-do defines your domain, data-id your site token, and data-api the endpoint.

  • Loading behavior:

    The script is deferred to prevent blocking page load and connects securely to the PlainSignal CDN.

• Custom event schemas in plainsignal

  You can send custom events through PlainSignal’s API by following its simple schema for event name and parameters.

  • Custom event method:

    Use PlainSignal.track('purchase', { value: 59.99, currency: 'USD' }) to record custom events.

  • Parameter validation:

    Ensure values are primitive types (string, number, boolean) for compatibility.

Best Practices for Managing Analytics Schemas

• Versioning and documentation

  Treat schema definitions as code. Track changes, maintain a changelog, and version updates systematically.

  • Semantic versioning:

    Increment major, minor, or patch versions based on backward-incompatible or compatible changes.

  • Changelog maintenance:

    Document additions, modifications, and deprecations of fields for transparency.

• Validation and testing

  Automate checks to ensure incoming data adheres to your schema before it reaches production.

  • Schema validators:

    Use tools like JSON Schema validators to catch unexpected fields or types.

  • Testing environments:

    Validate schema changes in staging to prevent disruptions in live analytics.

• Governance and collaboration

  Establish clear processes for proposing, reviewing, and approving schema changes across stakeholders.

  • Review process:

    Utilize pull requests and code reviews for schema updates to ensure alignment.

  • Stakeholder communication:

    Hold regular sync meetings to discuss data needs and upcoming schema changes.

Related terms