Table of Contents
- Data Observability Primer
- Report Methodology
- Decision Criteria Analysis
- Evaluation Metrics
- Key Criteria: Impact Analysis
- Analyst’s Take
- About Andrew Brust
Data observability provides details about the health of data throughout an organization. Although it’s a more recent development, this discipline is quickly becoming a vital part of modern data management. Data observability provides a nuanced understanding of the health of data at rest and in motion.
Optimal data health means that data is discoverable, available, usable, governable, and of high quality. Knowledge about these facets of data allows organizations to quickly troubleshoot, triage, and rectify data issues before applications, analytics, and user experiences are compromised. Results include a reduction or elimination of data downtime, heightened data reliability, and increased ability to transform superior data health into a commanding ability to accomplish business goals.
Data observability overlaps and shares some similarities with related technology areas, including application performance monitoring (APM), general observability, and monitoring. While data observability comprises elements of each of these, or their data management equivalents, it is its own distinct function. Data observability is an indispensable requisite for DataOps, which standardizes change management and the delivery of data for consistent, fast results. Data observability is to DataOps as observability is to DevOps and IT operations.
Data observability and data quality are similar but distinct functions. Traditional data quality tooling applies to data at rest. Data observability, on the other hand, applies to data in motion and at rest.
The salient characteristics of data observability are just as applicable to data in data pipelines as to data in repositories. Thus, this discipline is primed to assist with determining the traits of contemporary data and analytics environments, including distributed ecosystems and edge deployments, as well as on-premises, hybrid, multicloud, and polycloud use cases.
Another cornerstone of data observability is its considerable reliance on automation. There are several aspects of AI, AIOps, predictive analytics, and even knowledge representation techniques that provide some of data observability’s core functionality. AI is particularly useful in rectifying issues related to data health and/or supplying recommendations on how to do so most successfully. With such automated approaches to the ongoing monitoring, alerting, and rectifying of data health issues, properly implemented data observability solutions solidify the value of data pipelines and data itself to an enterprise.
The GigaOm Key Criteria and Radar reports provide an overview of the data observability market, identify capabilities (table stakes, key criteria, and emerging technologies) and non-functional requirements (evaluation metrics) for selecting a data observability solution, and detail vendors and products that excel. These reports will give prospective buyers an overview of the top vendors in this sector and will help decision-makers evaluate solutions and decide where to invest.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding, consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.