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A Guide to the Six Types of Data Quality Dashboards

DataKitchen

A Guide to the Six Types of Data Quality Dashboards Poor-quality data can derail operations, misguide strategies, and erode the trust of both customers and stakeholders. Data quality dashboards have emerged as indispensable tools, offering a clear window into the health of their data and enabling targeted actionable improvements.

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Webinar: A Guide to the Six Types of Data Quality Dashboards

DataKitchen

In this exciting webinar , Christopher Bergh discussed various types of data quality dashboards, emphasizing that effective dashboards make data health visible and drive targeted improvements by relying on concrete, actionable tests. Each type serves a unique role in driving changes in data quality.

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Scaling Data Reliability: The Definitive Guide to Test Coverage for Data Engineers

DataKitchen

Scaling Data Reliability: The Definitive Guide to Test Coverage for Data Engineers The parallels between software development and data analytics have never been more apparent. And how you can create 1000s of tests in a minute using open source tools. And how you can create 1000s of tests in a minute using open source tools.

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What’s Killing Data Innovation At Your Company? The Hidden Crisis in Data Usability

DataKitchen

Confusing dashboards, mismatched numbers, and endless Slack threads of “Wait… is this right?” Fix The Fear: Why Data Engineers and Quality Teams Love TestGen We test software code with care and consistency—so why don’t we apply the same discipline to our data? Just connect it to your data and start testing.

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

But today, dashboards and visualizations have become table stakes. Think your customers will pay more for data visualizations in your application? Five years ago they may have. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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Bigeye Enable Monitoring, Quality and Lineage of Data

David Menninger's Analyst Perspectives

I previously explained that data observability software has become a critical component of data-driven decision-making. This has increased the focus on data observability software providers such as Bigeye and the role they play in ensuring that data meets quality and reliability requirements. 

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Announcing Open Source DataOps Data Quality TestGen 3.0

DataKitchen

Open-Source, Generative Data Quality Software. Now With Actionable, Automatic, Data Quality Dashboards Imagine a tool that can point at any dataset, learn from your data, screen for typical data quality issues, and then automatically generate and perform powerful tests, analyzing and scoring your data to pinpoint issues before they snowball.