In 2025, we made data quality management proactive. In 2026, we're making it intelligent.

Throughout 2025, our product releases shared a common goal: to make data quality management proactive rather than reactive. Instead of discovering data quality issues after they've impacted analytics or models, teams can now catch problems early, route alerts intelligently, and trust their behavioral data for high-stakes applications.

As we look ahead to 2026, let's revisit the data quality innovations that defined 2025 and what's next.
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Looking Back: From Design to Analysis

Faster, Better Tracking Design

Snowplow MCP Server for Tracking Design

AI-assisted tracking design arrived. Data engineers and product managers can now collaborate with Claude, Cursor, or any preferred agent, to design Snowplow tracking that follows best practices automatically. The MCP Server uses Snowplow CLI and API to provide tools that understand Snowplow's event-entity model.

The impact: radically faster tracking design, easier migration from competitors, and better-quality tracking for teams new to Snowplow's approach. AI assistance that understands your data model prevents common mistakes before they reach production.

Unified Tracking Design in Event Studio

The most requested workflow improvement from our users was to create data structures directly within event specifications. Previously, teams built tracking bottom-up, first creating data structures, then attaching them to event specifications. This context-switching slowed iteration and introduced errors.

Now teams start with their use case and design top-down, creating entities inline within event specifications. Seamless workflow, fewer mistakes, faster time-to-value.

Source App Code Generation

From specification to deployed tracking in seconds. You can now generate JavaScript initialization code and out-of-the-box tracking directly from Console. Tracker configuration, plugins, global entities all configured correctly. Copy, paste, start firing events. Eliminates documentation hunting and configuration errors.
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Quality at Development Time

Snowtype

Eliminate runtime errors with type-safe tracking code. Snowtype is a command-line tool that transforms your tracking plan into type-safe code. With this workflow, you define your event specifications and data structures in Event Studio, generate type-safe tracking code with Snowtype, and get instant warnings for type mismatches or missing required fields directly in your IDE.

Instead of manually crafting JSON objects that might fail validation at collection, developers use functions that correspond directly to their tracking definitions. Auto-generated documentation, including screenshots, eliminates ambiguity about what needs to be tracked and when.

Teams using Snowtype see far fewer invalid events in their tracking. Problems caught at compile time, not runtime. Reduces time spent writing custom tracking code.
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Real-Time Quality Visibility

Data Quality Dashboard

Real-time visibility into failed events with near 5-second latency. The Data Quality Dashboard deploys within your infrastructure, maintaining security boundaries while providing centralized data monitoring through Console.

Schema enforcement at collection means invalid events are tagged immediately, preventing contamination of downstream datasets. Failed events are isolated in 'bad rows' streams for faster troubleshooting.

Organizations lose $13-15 million annually to poor data quality. The Data Quality Dashboard provides the visibility teams need to maintain clean, reliable data, particularly critical for AI/ML applications where training data quality directly impacts model performance.

Integration with Snowtype means errors caught at compile time never reach the pipeline.
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Failed Event Alerts

Visibility becomes action. Failed Event Alerts make data quality monitoring proactive, routing notifications directly into Slack or email workflows without manual dashboard checking.

Filter alerts by App ID, issue type, or data structure to reduce noise and strengthen data ownership. Teams responsible for specific data streams receive targeted, context-rich notifications with investigation details.

The result: faster resolution times, reduced alert fatigue, clearer accountability.
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Volume-Based Alert Thresholds 

Failed Event Alerts support volume-based thresholds, distinguishing between minor issues (a few scattered failures) and critical problems (thousands of events failing within minutes), giving you context-aware alerting that understands severity.
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Complete, Meaningful Data

Element Tracking Plugin

Data quality management isn't just about validation. It's about capturing complete user behavior. The Element Tracking plugin enables tracking of web page component presence and visibility, going beyond clicks to capture what users actually see.

You can monitor page content for changes, automatically firing events when elements appear, scroll into/out of view, or are removed. Configurable rules (minimum time in view, percentage visible) ensure only meaningful exposures are captured.

Use cases: impression tracking, recommendation monitoring, per-element scroll depth, component-centric funnels, modal tracking. Richer context for personalization engines and recommendation models, with less noise in training data.
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From Quality Events to Quality Analysis

Automatically Generated Data Models

High-quality events are only valuable if analysis is fast. Automatically generated data models eliminate the manual SQL work that sits between validated event tracking and analysis-ready tables.

From any tracking plan in Console, you can now generate optimized models through a guided workflow. Select event specifications, entities, and properties, then deploy views or incremental dbt models to your warehouse. Automatic flattening transforms nested event structures into analysis-ready columns.

Teams using Event Specification filtering with Snowtype get end-to-end quality assurance: validation at tracking design, enforcement at collection, filtering at modeling. This layered approach to data quality management ensures accuracy, completeness, and consistency from source to warehouse. 

The impact: from defining a tracking plan to querying analysis-ready tables in minutes instead of days.
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The Common Thread: Proactive Data Quality Management

Every 2025 release made data quality more proactive:

• At design time: MCP Server and unified workflows prevent modeling mistakes
• At development time: Snowtype catches errors before deployment
• At collection time: Schema validation quarantines invalid events immediately
• At monitoring time: Real-time dashboards and alerts catch issues within seconds
• At modeling time: Specification-based filtering ensures only validated data reaches analytics

Organizations can now trust their behavioral data for high-stakes applications like ML model training, real-time personalization, and agentic decision-making, where data quality directly impacts business outcomes.
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Looking Ahead: 2026 and Beyond

The foundation established in 2025 enables more ambitious capabilities in 2026.
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Data Completeness Without Compromise

Today, data structures check if events match their schemas. Soon, Event Specifications will validate business logic and ensure complete behavioral context. Data quality metrics that go beyond "did the event arrive" to "does this event capture the complete business context we need for personalization, recommendations, and agentic decision-making."

With this new approach, you’ll have validation that understands not just data structure but data meaning. Configurable handling will give teams control over how validation issues are managed, from strict enforcement to flexible monitoring based on business criticality.

For Signals users, Event Specifications will also simplify attribute creation. Create attributes directly from Event Specifications, with the Signals API supporting Event Specs alongside schemas. Less manual mapping, fewer attribute updates, faster time-to-insight.
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AI Assistance Across the Lifecycle

The MCP Server proved AI can accelerate tracking design. In 2026, AI assistance will expand across the entire data lifecycle.

Tracking Design Agent will bring AI-powered recommendations directly into the design process. Automated schema and event specification generation encourages reuse while reducing manual effort. Organizational context means recommendations align with your specific business goals and governance practices, not generic best practices.

For new customers, guided onboarding powered by AI assistance will reduce time-to-value. Redesigned workflows with in-product guidance will enable self-service. And one-line tracking integration will get you from signup to collecting data in minutes.
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AI-Ready, Agentic-Ready

These capabilities point toward data quality management’s role in the agentic era: systems that detect problems, understand context, recommend solutions, and fix issues automatically.

As AI agents increasingly rely on behavioral data to understand customers and make decisions, data quality becomes non-negotiable. Snowplow's approach (quality enforced at every stage from design to analysis) provides the reliable, trustworthy data that powers agentic applications.
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Get Started Today

All 2025 capabilities are available now for Snowplow customers. Subscribe to our newsletter (via the purple form on the top right hand corner of this page) for monthly updates on data quality features and behavioral data best practices. New to Snowplow? Request a demo to speak with the team.