How to Connect Snowflake Data Quality to Downstream Dashboards: A Complete Guide for 2026

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 23rd, 2026 | 12 min read

Quick answer: How do you connect Snowflake data quality to downstream dashboards?

Connecting Snowflake data quality to downstream dashboards requires surfacing trust signals, i.e., real-time quality badges, where business users work. You can natively define and schedule data quality checks using Data Metric Functions (DMFs), log the results to a centralized table, and propagate those signals through metadata platforms (Atlan) to BI tools (Tableau and Power BI).

Core connection methods:

  • Native Snowsight dashboards: Build native visualizations directly within Snowflake to monitor quality trends, record counts, and execution health alongside your warehouse performance.
  • Metadata control planes (Atlan): Use a metadata control and context plane like Atlan for lineage-integrated visibility. Atlan crawls Snowflake DMF metadata and automatically propagates health status across your entire stack.
  • External BI tools: Use standard SQL connectors to pull DMF results into Tableau, Power BI, or Sigma to build custom "Data Health" headers or overlay quality scores directly onto executive-level dashboards.

Below: establishing quality checks in Snowflake, mapping quality to dashboards through lineage, surfacing trust signals in BI tools, automating quality alerts and workflows, and implementation approach.

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Why do teams struggle to connect data quality to dashboards?

While data quality checks run in Snowflake, business decisions happen in Tableau, Looker, or Power BI. Many organizations fail to bridge the gap between technical quality checks and business-facing dashboards, creating significant blind spots.

According to Gartner research, poor data quality costs organizations $12.9 million annually. Most of that cost stems from decisions made on untrustworthy data flowing into dashboards without visible quality signals.

Three specific challenges emerge:

1. Quality checks remain invisible


Snowflake DMFs generate metrics stored in a dedicated event table for data metric functions. Because these results sit isolated in a system-level event table, dashboard creators have no immediate indication that upstream quality issues exist.

The disconnect means analysts discover data problems through user complaints rather than proactive monitoring.

2. Manual validation wastes time


Without automated quality-to-dashboard connections, teams resort to manual checks. Data engineers run ad-hoc queries to verify freshness, while analysts spot-check row counts before publishing reports. This reactive approach consumes hours of productive time across modern data teams.

3. Impact analysis requires guesswork


When a quality check fails on a source table, determining which dashboards are affected requires tracing dependencies manually. Teams often search through outdated documentation or wait for users to report broken visuals. The lack of automated lineage from quality checks to dashboard consumption creates massive resolution delays.

How do you establish quality checks in Snowflake?

To connect quality to your dashboards, you must first establish the foundation within Snowflake using Data Metric Functions (DMFs). DMFs are SQL functions that measure specific quality dimensions, such as completeness, accuracy, freshness, statistics, uniqueness, and volume.

Step 1. Choose between system and custom DMFs


Snowflake provides two ways to define DMFs:

  • System DMFs: Pre-built functions provided by Snowflake for common checks. Examples include SNOWFLAKE.CORE.NULL_COUNT, SNOWFLAKE.CORE.DUPLICATE_COUNT, and SNOWFLAKE.CORE.FRESHNESS.

  • Custom DMFs: User-defined functions created using SQL or Python for domain-specific logic (e.g., verifying that a postal_code column matches a specific regex or that order_amount is never negative).

Quality checks run on schedules (cron), on-demand, or triggered when fresh data arrives. Results flow into Snowflake event tables for downstream consumption.

Step 2: Define and attach expectations


An expectation lets you define criteria for data quality checks. When the DMF returns a value, it is compared to this expectation to gauge if the quality check has been passed or failed.

In the example below, if the count of nulls is 10 or more, it is flagged as a violation.

ALTER TABLE orders
ADD DATA METRIC FUNCTION snowflake.core.null_count ON (customer_id)
EXPECTATION customer_nulls (VALUE < 10);

Step 3: Configure the schedule


To ensure the data is continuously monitored, you must set a DATA_METRIC_SCHEDULE at the table level. You can trigger checks on a specific interval, using Cron expressions, or via triggers.

For instance, you can use this code to set the data metric function schedule to run three times daily at 0600, 1200, and 1800 UTC.

ALTER TABLE hr.tables.empl_info SET
  DATA_METRIC_SCHEDULE = 'USING CRON 0 6,12,18 * * * UTC';

You can use the SHOW PARAMETERS command to view the DMF schedule.

Step 4: Monitor the event table


Snowflake logs data quality results in a dedicated event table: SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS_RAW. You can use a Snowsight page to monitor data quality in a table or view.

How do you map quality metrics to dashboards through lineage?

To bridge the gap between Snowflake’s back-end telemetry and front-end business decisions, you must map quality metrics to the specific assets users consume. Data lineage is the essential “map” that allows a technical failure in a raw table to be translated into a business warning on a dashboard.

Lineage provides three critical functions for dashboard connectivity:

  • Automated propagation: When a Data Metric Function (DMF) fails on a source table, a metadata platform like Atlan uses lineage to propagate that failure downstream. Even if the dashboard is five joins away from the source, the user sees a warning.

  • Root cause analysis (RCA): When a business user sees an anomaly in a Tableau dashboard, lineage allows them to trace back up the graph. They can immediately see that a specific expectation violation occurred in a Snowflake staging table three hours ago.

  • Proactive suppression: Lineage allows teams to be proactive. If a critical DMF fails upstream, you can use the lineage map to automatically add a “Maintenance” overlay to downstream reports, preventing users from making decisions on corrupted data before they even open the app.

Lineage mapping approaches


Establishing this map requires a combination of automated parsing and cross-system integration.

1. Automated SQL parsing

Modern metadata platforms parse SQL queries, dbt models, and stored procedures to build lineage graphs automatically. This captures the actual data flow without manual documentation.

Atlan’s automated lineage maps Snowflake tables to Tableau dashboards by analyzing query patterns and join relationships. When quality metrics indicate issues in a source table, the platform surfaces every dependent dashboard.

2. BI tool integration

Deep integrations with visualization platforms extend lineage beyond the warehouse. Tableau workbooks, Looker explores, and Power BI reports connect directly to their upstream Snowflake sources.

This creates an unbroken chain from quality check → source table → transformation → dashboard field, making impact analysis immediate rather than investigative.

3. Cross-system tracking

Quality issues rarely respect system boundaries. Data might originate in Salesforce, land in Snowflake, transform through dbt, and ultimately feed Tableau dashboards. Complete lineage spans these tools.

A unified context layer like Atlan bridges these gaps by maintaining metadata across the full data stack, propagating Snowflake DMF signals to every connected tool.

How do you surface trust signals in BI tools?

Surfacing trust signals is the final “last-mile” step. It moves data quality out of technical logs and into the visual field of the business user. There are two primary ways to achieve this, ranging from native BI features to automated metadata overlays.

1. Metadata-driven health badges


The most effective way to surface trust signals is through a metadata control plane like Atlan. Instead of building custom charts, you inject status indicators directly into the BI tool’s interface.

For instance, users can hover over a specific metric (e.g., “Total Revenue”) to see its freshness and quality score, inherited directly from the upstream Snowflake source.

Atlan’s trust signals integrate with Slack, email, and in-app notifications. Messages include:

  • Which quality check failed
  • Upstream table and column affected
  • Downstream dashboards impacted
  • Owner contact information
  • Severity and recommended actions

As a result, whenever an upstream expectation is violated, these notifications alert dashboard owners, allowing them to verify the data before executive meetings.

2. Native BI “Certification” features


Most modern BI tools have built-in “Certified” or “Trusted” markers. You can automate these using Snowflake metadata in your BI tool of choice, like Tableau, Power BI, or Looker. For instance, use the Tableau API to toggle the “Certified” badge on a data source based on whether its corresponding Snowflake DMFs passed.

How do you automate quality alerts and workflows?

To prevent “broken dashboards,” you must automate the transition from detection to notification and remediation. Snowflake can natively push alerts to external systems (Slack, email, etc.) when a DMF identifies a quality issue.

For more advanced monitoring, AI-powered anomaly detection, and end-to-end data lineage, many organizations integrate with third-party data observability platforms like Monte Carlo, or a unified metadata control and context plane like Atlan.

The best way to automate is by making incident management collaborative with a modern data quality platform like Atlan, which embeds quality monitoring into existing workflows.

For instance, when a Snowflake DMF fails, Atlan can automatically:

  • Notify dashboard owners via their preferred channel.
  • Create a tracked issue with full context.
  • Pause scheduled report refreshes.
  • Trigger investigation playbooks.

See Atlan + Snowflake quality metrics in your dashboards

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How do modern platforms streamline quality-to-dashboard connections?

Modern metadata platforms automate the connection between Snowflake quality checks and downstream consumption. These platforms act as the connective tissue between Snowflake’s technical logs and the business-facing dashboard, orchestrating quality across your data ecosystem.

Platform capabilities that matter


  • Native execution: Quality checks run inside Snowflake without moving data or adding infrastructure.

  • Universal trust signals: Quality indicators appear in Tableau, Looker, Power BI, and data catalogs simultaneously.

  • Automated lineage: Column-to-dashboard tracking updates automatically as pipelines evolve.

  • Business-friendly rules: Domain owners define quality expectations in natural language rather than SQL.

  • 360° quality view: Aggregate signals from multiple tools (Monte Carlo, Soda, Snowflake DMFs) into unified data health dashboards.

  • Contextual alerts: Notifications include ownership, lineage impact, and resolution guidance, moving beyond simple error messages.

Atlan’s Data Quality Studio exemplifies this approach. Its no-code interface runs natively on Snowflake DMFs, and propagates the results to every connected tool. Dashboard users see trust signals without leaving their BI platform.

The integration maintains Snowflake as the computation engine while adding orchestration and visibility. Data stays in the warehouse. Quality context travels everywhere.

Book a demo to see how Atlan surfaces Snowflake quality metrics in your dashboards.

Real stories from real customers: Quality-to-dashboard success

General Motors: Data Quality as a System of Trust


“By treating every dataset like an agreement between producers and consumers, GM is embedding trust and accountability into the fabric of its operations. Engineering and governance teams now work side by side to ensure meaning, quality, and lineage travel with every dataset — from the factory floor to the AI models shaping the future of mobility.” - Sherri Adame, Enterprise Data Governance Leader, General Motors

GM builds trust with quality data

Watch Now →

Workday: Data Quality for AI-Readiness


“Our beautiful governed data, while great for humans, isn’t particularly digestible for an AI. In the future, our job will not just be to govern data. It will be to teach AI how to interact with it.” - Joe DosSantos, VP of Enterprise Data and Analytics, Workday

Workday's data AI-ready

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Moving forward with quality-to-dashboard connections to close the trust gap

Connecting Snowflake data quality to downstream dashboards transforms your data stack from a collection of “black boxes” into a transparent data and AI ecosystem.

When you bridge the gap between technical event tables and business-facing reports, you fundamentally change how the organization interacts with data. You no longer wait for users to discover errors or ask if a dashboard is “safe” to use. Instead, the architecture itself proactively communicates health through automated lineage and trust badges.

Building reliable connections between Snowflake quality checks and downstream dashboards requires automation, lineage, and embedded trust signals. Start by implementing native DMFs for critical tables, then extend quality visibility into BI tools where business users actually work.

The platforms and processes you choose determine whether quality becomes a strategic advantage or remains a technical afterthought. Atlan connects Snowflake quality to your dashboards automatically.

Let’s Help You Build It → Book a Demo

FAQs about connecting Snowflake data quality to dashboards

1. Can Snowflake DMFs directly display in Tableau or Power BI?


No, DMFs run in Snowflake and store results in event tables. You need a metadata platform or custom integration to surface these results in BI tools. Modern data catalogs like Atlan read DMF results and propagate trust signals to Tableau, Power BI, and Looker automatically.

2. How do quality signals update in real-time for dashboards?


Quality checks run on schedules (hourly, daily, or on data refresh). When checks complete, platforms push updated trust signals to connected BI tools. The refresh frequency depends on your DMF schedule and integration architecture. Streaming quality updates require real-time quality monitoring tools.

3. What happens when a quality check fails mid-day?


Failed checks trigger alerts to data owners and dashboard consumers. Most platforms pause affected dashboard refreshes or display warning badges to prevent decisions on bad data. Teams investigate root causes using lineage analysis while users receive status updates.

4. Do I need separate quality tools for each BI platform?


No, unified metadata platforms aggregate quality signals from multiple sources and distribute them across all connected BI tools. You define checks once in Snowflake, and trust signals appear everywhere without tool-specific configuration.

5. How does this work with dbt transformations?


dbt tests and Snowflake DMFs complement each other. dbt validates transformation logic. DMFs monitor warehouse table quality. Lineage platforms connect both layers, showing how dbt test failures affect downstream dashboards through the same quality signal framework.

6. Can business users define their own quality rules?


Yes, modern platforms offer no-code interfaces where domain owners specify quality expectations in business terms. The platform translates these into technical DMFs that run in Snowflake. This democratizes quality definition beyond data engineering teams.

7. How do you measure data quality impact on business reporting?


The most critical metric for reporting is Data Downtime: the duration when a dashboard is inaccurate or unusable. Track Mean Time to Detection (MTTD), Mean Time to Resolution (MTTR), and use lineage to map Snowflake DMF failures to downstream reports. This helps quantify the “impact radius” in dollars and monitor SLA compliance.

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