TOFU

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.

Poor data quality creates extra work and frustrates stakeholders. At worst, it costs time, revenue, and damages customer sentiment.

How to choose the right data architecture: Weighing the pros and cons of Data Mesh and Data Fabric.

Your business is evolving, and so is business tech. Data modernization makes sure the tools you use to collect and analyze data keep up.

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

Address data quality issues with guidelines, concepts, and practices to preserve trust and turn data into your greatest asset.

Explore data products' role in decision-making, their business impact, and the evolving implications of this concept in data strategy.

Understanding Microsoft Fabric: From OneLake's unique data storage to flexible compute setups.

A detailed guide comparing Data Mesh and Data Lake architectures, and factors to consider when choosing.

Business intelligence is evolving, and it's happening right now. Discover the 5 transformative shifts set to redefine the future of BI.

Using stale data for decision-making can have dire consequences. In this blog post, we explore the risks and emphasize the significance of accurate data.

Data contracts bridge business and data workflows. Learn their key elements, benefits, and implementation strategies in our blog.

Learn more about Data Product Managers, their origin, impact on businesses, and traits to develop to make it a career.

Get an in-depth understanding of the Data Mesh architecture, its real-world applications, and the keys to its successful adoption.

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

Five important reasons for why you should consider making data quality a priority now, instead of waiting until it becomes an issue.

How to make the right choice in a market bursting with options

Data observability is an emerging technology that solves an age-old problem: understanding the state of data systems to increase awareness and trust.

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.

Keep insights fresh and decisions data-driven with our guide to monitoring table and view updates for data freshness in BigQuery.

Optimize Redshift with accurate row counts using COUNT functions and system stats for smarter data management and insights.

Discover methods to obtain row counts in Snowflake, from SQL queries to leveraging table stats for performance optimization.

How much data is enough for observability? Learn the key pillars that describe your data and underpin data observability practices in 2024.

Learn about five data quality challenges from errors to assumptions and strategies to ensure reliable, trusted organizational data.

Predicting the future of data observability: 10 years from now, automation and generic metadata will be key in incident management and data management.

Data observability is vital for ensuring high-quality data and informed decision-making. We cover its benefits, challenges, and how it impacts businesses.

ELT tools hold insights for upstream data issues. Metaplane's Fivetran integration offers a comprehensive view of your data ecosystem.

Data quality is essential for success. Learn its importance, challenges, and practices to maintain high standards in your organization.

Streamline data issue management with a six-step process inspired by site reliability engineering to handle data incidents effectively.

As the new kid on the block, data observability often goes misunderstood. In this blog post, we strive to correct the misconceptions we hear most frequently.

Data is central to product-led growth. Learn how poor data quality impacts revenue, team efficiency, and strategic forecasting.

Human and machine errors create data quality challenges. Discover the top five problems data teams face and strategies to address them.

From 'Tech Bros talk Data Scams' to 'Thoughtful Data Modeling', here are 11 data leaders making waves in 2023.