‘Observability data’ vs ‘Data Observability’

Over the past year, Grand Ventures has been closely tracking the Observability and Data Observability spaces, and as part of that effort we’ve talked to large numbers of both new startups and existing players within the Observability and Data Observability spaces.

In my <next> blog, I’m going to go deeper into the trends within those two segments and why I think they are important; however, with this blog my goal is simply to provide some clarity on the differences between the ‘Observability’ vs ‘Data Observability’ market segments.

It’s natural to assume that ‘Observability’ is a subset of ‘Data Observability’ or vice versa. Turns out it’s a faulty assumption. When you expand the definition a bit you might even end up comparing “Data Observability” and “Observability data”. They share the same two words (in a different order) which clearly is the genesis for much of the confusion I see/hear discuss companies in these spaces inside of Grand Ventures, when I read about companies in these segments in the press or blogs, and when I see the two segments discussed in social channels. I suspect the confusion is rooted in the way the human mind interprets the English language… we optimize first for the words themselves and then to a lesser degree in the ordering of the words.

Observability Data

Observability data refers to the telemetry data that is collected and analyzed to gain insight into the behavior of a system. This data includes metrics, logs, traces, and other types of data that are used to monitor and troubleshoot the health, performance, and availability of things like Kubernetes containers, virtual machines, cloud infrastructure, storage, and custom applications.

Observability data (also known as telemetry) is typically collected in real-time and is used to identify issues and trends in the system. When you think of Observability data, think of the data collected by vendors/tools like Datadog, New Relic, Dynatrace, Honeycomb, and others — increasingly using open source data collection tools like Open Telemetry (OTEL), Fluentbit, and Prometheus. The audience for Observability Data is Site Reliability Engineers (SREs), ITOps teams, and increasingly SecOps.

Data Observability

Data observability, on the other hand, is concerned with the quality and reliability of data itself. Data Observability is focused on understanding, measuring, and improving the quality of data as it flows through a system. Data observability involves monitoring and analyzing data to ensure that it is accurate, complete, and consistent. Data engineers, data warehouse administrators, and ETL developers are the primary audience for Data Observability tools. Monte Carlo, AccelData, Metaplane, Databand, and Datafold are leading vendors in the Data Observability space.

Data Observability has gained prominence in recent years to solve challenges that have cropped up from feeding emerging next-gen data warehouse solutions, like Snowflake, 10’s and sometimes 100’s of distinct data sources/streams.

Differences between Observability Data and Data Observability Data in a nutshell

The key difference between Observability Data and Data Observability is where they focus. Observability Data is all about the behavior of a system: it is up or down, is it performing slowly, how many people logged in over the last minute, how long did it take to load a page, etc.), while Data Observability is used by Data Engineering teams and is concerned with the quality, lineage, and reliability of data itself — where the data came from, how are elements of data related to one-another, etc.

Hopefully that provides some level of clarity of Observability Data vs Data Observability.

Are you a founder that love talking about data observability or observability data? Reach out to us at Grand Ventures.”