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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.

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.

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.

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.

Explore the evolving world of data products (or data as a product), their impact on decision-making, how they're integrated into businesses, and what the implications of this new phrase is.

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.

Discover the significance of data contracts as a bridge between business and data in our blog post. Uncover their definition, key elements, benefits, and effective implementation tips.

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.

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.

How much data do we need to reconstruct a useful picture of our data? That’s the question we attempt to answer in this article. The groupings of questions we need to answer in order to describe our data are the “pillars” that underlie data observability in 2023.

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.

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.

Modern data teams have increasingly complex and powerful solutions for making sure their data is exactly where it needs to be, when it needs to be there, and in the correct format. ELT tools like Fivetran hold a goldmine of useful information, including hints about the potential root causes for data issues that might have started upstream.Metaplane now integrates with Fivetran, giving our joint users a richer view into the makeup and health of their whole data ecosystem.

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.

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.

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.

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