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Have confidence in your data,
no matter what
Get everything you need to trust your data with GX Cloud: an end-to-end solution for your data quality process and a unique Expectation-based approach to testing, backed by the world’s most popular data quality framework.
Take control of your data quality process
An end-to-end solution
GX Cloud —powered by the proven capabilities of GX OSS— has you covered from start to finish in your data quality process, from your first exploratory data profiles to your complete, continuous process with historical results. With everything you need in one place, GX Cloud frees you to focus on the what of your data quality, rather than the how.
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What you need, when you need it
Data quality touches practically every corner of your organization. GX Cloud seamlessly integrates with your existing stack, and deploys throughout your pipeline to pinpoint problems. And since it’s easy to scale, you can bring your data quality process to the places it’s most needed—fast.
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Bring your business together
Getting your technical teams and business stakeholders on the same page—and keeping them there—is one of the top challenges to maintaining a data quality process. GX Cloud’s SaaS interface and plain-language approach make it super-useable, so you can get critical input from even nontechnical stakeholders.
Expectations: your key to data quality
An Expectation is a verifiable assertion about your data. Simple and readable, an Expectation is understandable and accessible to everyone who sees it. And your combined Expectations aren’t just a comprehensive set of data quality tests—they’re a repository of institutional knowledge about your data that can otherwise go unrecorded.
This intuitive approach is accessible to technical and nontechnical teams alike. It offers the precision your technical team needs with plain-language readability that empowers stakeholders to stay engaged.
Expectations offer deeper insights than schema-focused validation and are more resilient to changing business and technical requirements than low-configuration options are. They cover a core set of data quality dimensions, including schema, freshness, volume, missingness, basic semantic type, cardinality, and data type.
