Data quality frequently makes the list of top concerns for data teams. Now, data quality is even more critical with the rise of AI and AI-powered applications. Forty-six percent of CDOs surveyed1 named data quality as the biggest challenge for realizing generative AI's potential.

Improving data quality isn’t a quick fix. To measure data quality, data teams often use data quality and data observability solutions like Snowflake’s upcoming Native Data Quality Monitoring. But that’s only half the battle. The other half is getting data quality information to the people who need it, when and where they need it. From data engineers to data consumers, teams only benefit from high-quality data when they can access and understand it. 

That’s why has prioritized pioneering a first-of-its-kind integration with Snowflake’s Data Quality Monitoring currently in Private Preview. helps data teams access important data quality information and share it across teams. In doing so, they make data more trustworthy, which leads to teams making better data-driven decisions with confidence.

Extend data quality metrics to everyday workflows

What’s the easiest way to share data quality metrics with teams that are already context-switching among different tools and apps? Make data quality metrics available where they work, in tools like, Tableau, Power BI, and more. By using’s embeddable trust signal, Hoots, you can ensure that data consumers have up-to-date information about their data. When issues arise, data teams can automatically update the status on Hoots for all impacted dashboards and reports, so that data consumers immediately see a status change on their dashboard with relevant data quality metrics.

Snowflake customers can surface data quality metrics to users in the catalog or on dashboards with Hoots.’s enhanced Snowflake collector harvests metadata for data metric functions (DMFs). Their observations provide context about freshness, accuracy, and more.

If you have a standalone data observability solution like Monte Carlo, Bigeye, or others, integrations can surface data quality metrics in the catalog or broadcast information to dashboards and reports via Hoots.

Define data quality for the organization

Make data quality rules available and explainable to everyone in the organization. When you catalog Snowflake’s DMFs, you can define your data quality checks in business-friendly terms. Centralize data quality definitions across one or multiple tools so that everyone is using the same measures of quality across the board. Governance teams can even take data quality measurements one step further and use Eureka Bot governance automations to update statuses for completeness, freshness, and more.

Reduce time to resolution for data quality issues

When data quality issues arise in Snowflake or other data observability tools, data engineers don’t always have end-to-end visibility into their data supply chain. Manual troubleshooting takes up a lot of time. By looking up the affected resource in, Explorer Lineage will identify upstream sources and downstream transformations and dashboards that need an update. Data engineers can resolve issues faster with added visibility, and then communicate changes back to data consumers on their dashboards by changing the Hoot's status.

Increase engagement in data quality & governance initiatives

People who use data in their day-to-day are often the first to notice when something appears off.  But, these people don’t have access to Snowflake or data observability solutions to get information about their data's quality and report issues. They end up connecting with their data teams via emails and direct messages, making it difficult for the data teams to track reported issues. 

To streamline this process, data consumers can go from a Hoot on their dashboard directly into the data catalog to ask a question or suggest a business term update. By connecting data consumers to data teams in the catalog, you can encourage people to say something if they see something, and give them easy workflows to ask questions and suggest changes.

Streamline giving a hoot about data quality 

No one said tackling data quality was easy. Lighten the load for your data teams by storing, communicating, and sharing information about data quality. 

By checking for data quality with capabilities like Snowflake’s DMFs, your data teams get more context. By making this information available in the catalog and via Hoots, you extend these insights to everyone in the organization. Whether it’s resolving pipeline issues faster, using the right data for critical reports, or automating governance tasks, sharing data quality information can help everyone take action on data in their day-to-day work.

Are you interested in getting more value from your Snowflake investment and extending your data quality initiatives with Learn more about our Snowflake-exclusive offering that provides the benefits of the data catalog including the enhanced integration for data quality, data lineage, generative AI, and more.

1AWS for Data’s 2024 CDO Agenda