Data Quality

Without a solid data management strategy, you're crossing your fingers and hoping that nothing goes wrong. Here's your first step toward a sound data governance strategy: using tags properly in Snowflake.

Schema drift can result in missing or inconsistent data, undermining the accuracy of your Snowflake queries and reports if unaddressed. Here are 3 ways to track schema drift in Snowflake.

Until recently, ML model creation focused more on the architecture of the model itself, rather than the data feeding it. Here's why that's a problem.

Best practices for maintaining a few aspects of data integrity.

Learn about common data quality mishaps when implementing new tools for your data stack.

Learn more about data relevance and how it can improve your data quality.

Learn more about how data security plays a role in your data quality practices.

Learn more about how data reliability plays a role in your data quality practices.

Learn what data usability is and what role it has to play in improving data quality across your data stack.

Learn more about how valid data can play a part in your data quality strategy.

Data completeness is one of the ten dimensions of data quality. In this post, you'll learn what it means, why it matters, and how it's measured.

Data accuracy is one of the ten dimensions of data quality. In this post, you'll learn what it means, why it matters, and how it's measured.

Data freshness is one of the ten dimensions of data quality. In this post, you'll learn what it means, why it matters, and how it's measured.

Data consistency is one of the ten dimensions of data quality. In this post, you'll learn what it means, why it matters, and how it's measured.

How does data quality impact business performance? In the best case, poor data quality creates more work for your data team. In the worst case, it costs you time, frustrates stakeholders, jeopardizes revenue, and damages customer sentiment.

Learn more about how data quality tests, data monitoring, and data observability differ, while still improving data quality in their own ways.

There are a multitude of potential data quality issues, and equally many ways to improve. This post describes two guidelines, three concepts, and four best practices to preserve trust in data.

Data catalogs are being adopted to improve data quality and help analytics and data engineering teams better understand what's available to use. Read on if you're currently evaluating data catalogs!

Learn how you can use data lineage, provided by data observability tools, to improve data quality across your modern data stack.

A framework of data quality metrics, a shortlist of metrics, and a process for identifying which metrics your team should use.

Learn how machine learning can help make your data quality checks more robust, empowering data and analytics engineers to make reliable and accurate data-driven decisions

Learn how to extract and use data lineage in Snowflake to optimize your data pipelines and improve data engineering workflows.

Discover how to ensure your data is fresh and up-to-date in Snowflake with two essential tools: the MAX function and the LAST_ALTERED column.

Master Data Freshness in Google BigQuery: Keep your insights up-to-date and your decisions data-driven with our guide to monitoring table and view updates in BigQuery.

Understand your data better in Amazon Redshift with accurate row counts. Discover how using COUNT functions and system statistics can optimize your data management.

Explore various methods to obtain row counts in Snowflake, from simple SQL queries to leveraging table statistics, for performance optimization and data analysis.

A comprehensive guide on retrieving row counts in tables and views for Google BigQuery. We detail practical methods, discuss their trade-offs, and provide useful code snippets.

Data engineers use unit testing for data quality today, but quality assurance needs more. In this post, we offer best practices for robust data quality standards.

Understand 5 common sources of data quality issues: input errors, infrastructure failures, incorrect transformations, invalid assumptions, and ontological misalignment. Learn to combat these with smart strategies to turn data into your strongest asset.

Data quality has a massive impact on the success of an organization. In this blog post, we highlight what it is, why it matters, what challenges it presents, and key practices for maintaining high data quality standards.

Need to streamline your data quality issue management process? In this blog post, we take inspiration from site reliability engineering to craft a six-step process for handling your data incidents.

Product-led companies are fundamentally data-driven. Data about the product, customers, and users facilitates every part of the PLG business and the product behind it. Given the central role of data, problems with data quality can have massive impacts on revenue, customer sentiment, team efficiency, and strategy and forecasting.

From getting to know hundreds of data teams, we've discovered that the most common data quality challenges result from both human and machine errors. Here are the top five problems we've encountered.

The people traditionally most concerned with data quality are naturally the people debugging data issues themselves, being analytics teams. Conversations around data quality have been focused around data pipeline tests and anomaly detection. What about ensuring data quality both upstream and downstream of analytics workflows?