Leveraging Snowflake’s data metrics functions in DataGalaxy

Maintaining high data quality is crucial for making informed business decisions. However, assessing and ensuring data quality across large datasets can be challenging without the right tools.

DataGalaxy now supports Snowflake’s data metrics functions to help measure and manage data quality effectively for all Snowflake users.

Keep reading to learn more about leveraging Snowflake’s data metrics functions directly into DataGalaxy to keep your data catalog up-to-date with all your predefined and customized Snowflake DMFs.

The need for robust data quality measurement

As organizations increasingly rely on data to drive decisions, the need to improve data quality will only grow over time. Data quality measurement allows organizations to ensure they have quality data and they can improve the management of this data over time to enable teams to have confidence in its use.

Because data quality is the foundation upon which all data-driven decisions are made, working with data of poor quality can have significant impacts on business operations, including:

• Inaccurate decision-making: Poor data quality can lead to incorrect insights and decisions, negatively affecting business outcomes.
• Compliance risks: Inconsistent or erroneous data can result in compliance failures, leading to legal and financial repercussions.
• Operational inefficiencies: Data quality problems can slow processes and reduce overall efficiency, costing time and resources.

The benefits of using Snowflake’s data metrics functions

Snowflake’s data metrics functions offer a comprehensive toolbox for measuring and managing data quality by providing robust tools for assessing the quality of your data. These tools allow you to set benchmarks and metrics to continuously monitor and improve data quality.

Data metrics functions provide many benefits for improving organizational data governance after you set data quality rules, including:

The benefits of using DataGalaxy with Snowflake’s data metric functions

Using a data catalog with Snowflake offers several benefits that enhance data management, governance, and analytics capabilities. Some of the benefits of using DataGalaxy and Snowflake together for data quality measurement include:

• Seamless integration: DataGalaxy’s Snowflake integration allows all users in your organization to benefit from data quality checks. Reading the results of these checks is also easily accessible, enabling quick identification and resolution of data issues.
• Quick reaction to quality issues: Quickly address any quality issues identified by Snowflake’s data metrics functions to ensure your data remains reliable and trustworthy. Timely interventions prevent small issues from becoming significant problems, maintaining the integrity of your data.
• No additional tools needed: Snowflake and DataGalaxy work together to eliminate the need for extra data quality tools, streamlining your data management processes. This integration offers an end-to-end solution for monitoring data quality and speeding up issue remediation. Snowflake DMF processes the metrics, while DataGalaxy leverages them to generate actionable insights for users who govern the data assets.

Conclusion

Ensuring data quality is critical for making informed business decisions. Leveraging Snowflake's data metrics functions directly into DataGalaxy offers a powerful solution for maintaining high data quality across large datasets.

By leveraging Snowflake’s robust data metrics functions within DataGalaxy’s comprehensive data catalog, organizations can swiftly identify and address data quality issues, enhancing data governance and operational efficiency. This streamlined approach not only eliminates the need for additional tools but also fosters confidence in data use, ultimately driving better business outcomes.