To keep pace with data’s clock speed of innovation, data engineers need to invest not only in the latest modeling and analytics tools, but also technologies that can increase data accuracy and prevent broken pipelines. The solution? Data observability, the next frontier of data engineering and a pillar of the emerging Data Reliability category.

As companies become increasingly data driven, the technologies underlying these rich insights have grown more and more nuanced and complex. While our ability to collect, store, aggregate, and visualize this data has largely kept up with the needs of modern data teams (think: domain-oriented data meshes, cloud warehouses, data visualization tools, and data modeling solutions), the mechanics behind data quality and integrity has lagged.

No matter how advanced your analytics dashboard is or how heavily you invest in the cloud, your best laid plans are all for naught if the data it ingests, transforms, and pushes to downstream isn’t reliable. In other words, “garbage in” is “garbage out.”

Before we address what Data Reliability looks like, let’s address how unreliable, “garbage” data is created in the first place.

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