Data Quality

Ensure Accurate, Reliable Behavioral Data Across Your Entire Data Pipeline

Name: Snowplow Data Quality Management
Brand: Snowplow

Validate, monitor, and manage data quality with comprehensive controls from collection to activation.

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Improve Data Quality at Source

Prevent data quality issues before they occur with built-in validation, real-time monitoring, and automated testing capabilities.

Maintain Data Integrity

Ensure consistent, accurate data collection across all platforms with version-controlled schemas and automated validation.

Resolve Issues Quickly

Identify and debug data quality problems in real time with specialized developer tools and comprehensive monitoring.

Prevent Data Quality Issues at Collection

Generate type-safe tracking code with built-in validation using Snowtype. Eliminate common data quality issues upfront with inline documentation and automated checks, ensuring data consistency across your pipeline.

Enforce data quality standards through strict, automated validation

Prevent schema violations with type-safe code generation

Maintain consistent data structure across nine platforms including web, mobile, and server environments

Screen showing instructions to implement tracking with Snowtype, including a command to generate the latest code and a copy button.

Dashboard displaying Snowplow Micro with total events sent as 12, good events 8, bad events 4, and a bar graph showing event counts from 10:20 to 10:35.

Validate Data Before Production

Test and validate data quality in development environments with Snowplow Micro. Enable developers to catch and fix data quality issues before they reach production, ensuring clean, reliable data from the start.

Catch data quality issues early in the development cycle

Validate events against schemas before deployment

Automate quality assurance testing to maintain data integrity

Monitor and Resolve Failed Events

Track and manage failed events in real time through a Data Quality Dashboard in console. Quickly identify, diagnose, and resolve data quality issues with detailed error reporting and recovery tools.

Identify root causes through detailed error messages and debugging tools

Recover valid events and fix systematic issues at their source

Set up custom alerts for specific failure patterns or thresholds

Dashboard showing issue details with validation error for $.classes property, including error message, app ID, tracker version, timestamps, and sample data table of app IDs, browser info, languages, view heights and widths.

Screenshot of an analytics dashboard showing event details including a Pageview event with timestamps, application ID, event ID, platform, and tracker information, along with JSON schema validation status.

Monitor Data Quality in Real Time

Ensure continuous data quality with real-time validation using Snowplow Inspector. Quickly identify and diagnose data quality issues as they occur, maintaining the integrity of your data pipeline.

Monitor event validation in real time

Debug data quality issues instantly

Track data quality metrics with detailed visibility into HTTP requests

Version Control Your Data Structures

Manage data schemas and tracking definitions using Git-backed Data Structures. Enable collaborative development and maintain data consistency with automated validation and approval deployment workflows.

Track and manage schema changes with full version control

Automate schema validation and deployment through CI/CD pipelines

Enable collaborative schema development with pull request workflows

Terminal screen showing a git diff of login.yaml file updating version from 1-0-0 to 1-0-1 and changing enum values in result from success, failure to 200, 403.

Sequence diagram showing interactions between ID Service Endpoint, Snowplow Tracking Code, and Snowplow Collector with steps for requesting an ID, setting cookies, and event tracking.

Ensure Data Continuity and Accuracy

Maintain reliable, consistent user tracking with first-party persistent cookie management, Cookie Extension Service. Ensure data quality even in restrictive browser environments like Intelligent Tracking Prevention (ITP), delivering accurate insights for critical business decisions.

Maintain data consistency with reliable user identification

Ensure accurate data collection across all browser environments

Enable precise analysis with high-quality, continuous user tracking

Data Foundation

Modeling & Analytics

ML & Agentic AI

Data Quality

Ensure Accurate, Reliable Behavioral Data Across Your Entire Data Pipeline

Improve Data Quality at Source

Maintain Data Integrity

Resolve Issues Quickly

Prevent Data Quality Issues at Collection

Validate Data Before Production

Monitor and Resolve Failed Events

Monitor Data Quality in Real Time

Version Control Your Data Structures

Ensure Data Continuity and Accuracy

Resources For Your Data Journey

Garbage In, Garbage Out: How to Ensure High-Quality Data for AI-Ready Pipelines

Unwrapping Snowplow's Data Quality Roundup for 2024

Shift-Left Data Quality: Building Better Data from the Start

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