GigaOm Radar for Data Observabilityv2.0

1. Executive Summary

Data observability platforms provide continual insight into the current health of an organization’s data to ensure it’s of high quality, fully up-to-date, and dependable for enterprise-grade applications. They supply this advantage across a variety of tools, architectures, infrastructure, and data pipelines, in both on-premises and cloud environments within an organization’s data ecosystem. Data observability represents the latest evolution of data quality, and it’s as applicable to data-at-rest as it is to data-in-motion. Data observability is quickly becoming essential for credible data management.

The importance of data observability is demonstrated by what it enables and what it prevents. This discipline facilitates data reliability, which is the natural outcome of credible data observability constructs and practices, to underpin data-driven investments in general. It’s equally indispensable for minimizing or mitigating data downtime, data drift, and poor data quality so that applications, analytics, and operational systems can achieve peak performance and reliability.

Data observability solutions are becoming increasingly relevant to an ever-expanding base of user constituencies and personas. Data engineers and data scientists rely on data observability mechanisms in their work to pipeline, transform, and load data for a variety of downstream purposes. IT personnel employ data observability capabilities to reinforce the effectiveness of the data governance and data management platforms they use while gaining a degree of insight into data’s current state at various stages of its enterprise journey. Most importantly, perhaps, data consumers and business users depend on the byproduct of data observability—reliable, trustworthy data delivered in a timely manner—to achieve business objectives by leveraging its strategic value as an enterprise asset.

Data observability is becoming indispensable because it can help enterprises anticipate, identify, and remediate unknown or recurring issues impacting data—which traditional data quality and monitoring tooling can’t do. Strong solutions in this space wield machine learning (ML), AI, and active metadata analysis to pinpoint the root causes of data issues and all relevant system components and stakeholders impacted by them. They include capabilities for stopping pipelines when needed, quarantining data of substandard quality while allowing healthy data to be processed, monitoring and reducing infrastructure expenditure, and a host of other attributes. By combining all of these mechanisms in a single platform contiguous with data governance, data management, data engineering, data science, analytics, and other data-centric endeavors, data observability vendors provide critical platforms that can pay for themselves many times over.

This is our second year evaluating the data observability space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines 16 of the top data observability solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading data observability offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.