GigaOm Key Criteria for Evaluating Data Observability Solutionsv2.0

1. Executive Summary

Data observability solutions provide insights into different facets of the overall health of an organization’s data, maintaining high levels of quality, availability, and usefulness to serve enterprise objectives. They track a variety of metrics and logs about data assets, the infrastructure through which data journeys, and the various transformations and modifications made to data. They also provide incident resolution measures with real-time responses to unforeseen circumstances. By delivering end-to-end data lineage revealing what happened to data, in which component of its pipelines or target systems, these solutions issue real-time alerts to incidents, root cause analysis, and impact analysis to mitigate data downtime and adhere to service-level agreements (SLAs).

Data observability is rapidly becoming a data management mainstay for ensuring data is in an optimal state to fuel applications, base decisions upon, train or tune artificial intelligence (AI) models, and direct meaningful action. It counters data drift, modernizes data quality to incorporate data in motion in real-time updates, and ensures data reliability—without which an organization’s data-centric investments have meager ROIs.

As organizations progress ever further in their journeys to become data-driven, the stakeholders in data observability broaden accordingly. Foremost among this target audience is the data consumers who rely on data to do their jobs, which includes everyone from sales personnel to customer service and business end users. Networking professionals, IT teams, and administrators also rely on data observability to ensure that they’re servicing end users with accurate, reliable, and trustworthy data, in time for them to do their jobs. Upper-level executives and managers who base strategic projections upon relevant, fresh data and historic data also benefit from data observability platforms.

Business Imperative
The business imperative for data observability involves drastically decreasing the number of data incidents, heightening responsiveness to those that occur, and democratizing data culture and the tools required to foster it. The rash of vendors in this space attests to its maturity, though this is tempered by the reality that data observability emerged at the end of the previous decade. The urgency in adopting these solutions is acute for any organization working with a bevy of external sources, data pipelines, and decentralized architectures like data mesh and data fabric. The impact of data observability solutions in each of these areas is formidable, allowing organizations to achieve efficiency and efficacy, while the degree of automation present in these offerings minimizes the effort required to implement them.

The data observability marketplace is reflecting the rapid change characterizing the data ecosystem as a whole since the emergence of generative AI capabilities. Ever-increasing amounts of data are now needed from a diversity of sources. The scalability of data observability platforms, their scope, and the level of automation they include help keep pace with these needs. Consequently, vendors’ capabilities are expanding and solidifying with progressions for edge applications, greater levels of sophistication for staples like data profiling and anomaly detection, and automated remediation measures.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of a data observability solution deployment to the business, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of a data observability solution, we provide an overall Sector Adoption Score (Figure 1) of 4.8 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a data observability solution is a very credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for data observability are explained in more detail in the Sector Brief section that follows.

Figure 1. Sector Adoption Score for Data Observability

This is the second year that GigaOm has reported on 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 Key Criteria report highlights the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) for selecting an effective data observability solution. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. 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.