Downtime incidents can be financially catastrophic for companies, leading to lost productivity, misused man-hours, and eroding customer trust. With data analytics becoming increasingly important, companies are turning towards data observability solutions to detect, resolve and prevent instances of data downtime. Data downtime refers to periods of time where data is missing, inaccurate or otherwise “bad”, and can cost companies millions of dollars per year. Data observability tools monitor factors like data freshness, distribution, volume, schema, and lineage to provide rich context that enables rapid triage, troubleshooting, and effective communication with stakeholders impacted by data reliability issues.
While there are some commonalities between application observability and data observability, there are key differences as well. Application observability use cases focus on detection, alerting, incident management, root cause analysis, impact analysis, and resolution of application downtime. The key personas leveraging and building application observability solutions include software engineer, infrastructure administrator, observability engineer, site reliability engineer, and DevOps engineer. The core functionality of high-quality application observability includes end-to-end coverage across applications, fully automated integration with existing tech stacks, and traceability/lineage to highlight relationships between dependencies and where issues occur for quick resolution.
In contrast, data observability tackles a different type of system reliability – analytical data. Tools use automated monitoring, automated root cause analysis, data lineage, and data health insights to detect, resolve, and prevent data anomalies. Use cases for data observability include detection, alerting, incident management, root cause analysis, impact analysis, and resolution of data downtime. Key personas responsible for data observability include data engineers, data designers, data product managers, analytics engineers, and data reliability engineers. High-quality data observability solutions possess core functionalities like quick and seamless connection to existing stacks, machine learning (ML) models to automatically learn an environment and its data, and minimal configuration and practically no threshold-setting.
Companies are learning the business impact that analytical data downtime incidents can have, not only on their public image but also on their bottom line. For instance, a May 2022 data downtime incident involving gaming software company Unity Technologies sank its stock by 36% when bad data caused its advertising monetization tool to lose the company upwards of $110 million in lost revenue. As a result, organizations are turning towards data observability solutions to prevent such incidents from occurring.
