IBM Data Observability Alternatives

Considering alternatives to IBM Data Observability? See what this market IBM Data Observability users also considered in their purchasing decision. When evaluating different solutions, potential buyers compare competencies in categories such as evaluation and contracting, integration and deployment, service and support, and specific product capabilities.

Check out real reviews verified by Gartner to see how IBM Data Observability compares to its competitors and find the best software or service for your organization.

Reviewed in Last 12 Months

How Alternatives Are Selected

1. Monte Carlo

4.7

My overall experience with MC is very good. It is super nice that many aspects are automated and it helps my team to easily get to the root cause of a quality issue.Read all insights and reviews for Monte Carlo

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2. Bigeye AI Trust Platform

4.6

Easy to use and setup within my department for data quality monitoring and testing Read all insights and reviews for Bigeye AI Trust Platform

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3. Synq

4.7

The Platform makes it simple to connect systems and sync data. and keep the information consistent without a lot of manual effort.It is re;iable, intuitive and has helped streamline several of our internal workflows.Read all insights and reviews for Synq

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4. Elementary Cloud

4.6

Overall, Elementary has been a strong addition to our modern data stack and has delivered significant value as a data observability platform. It integrates naturally into our dbt-first workflow and builds on the metadata and logs we already generate, which makes adoption friction extremely low. With health dashboards, test visibility, and alerting; it has made data quality tangible not just for engineers, but also for analysts and business stakeholders. It has improved our ability to trust, monitor, and act on data issues in production, and we would make the same decision again.Read all insights and reviews for Elementary Cloud

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5. Pantomath

4.8

Working with the Pantomath team has been a wonderful experience. It's been very satisfying to see all levels of management actively engaged in the Pantomath product. The product and implementation teams have been very easy to work with and are there to answer questions that come up during any part of the process. The product itself is very easy to use. I look forward to seeing how this product evolves in the next year!Read all insights and reviews for Pantomath

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6. Soda

4.4

Soda is a complete product for executing Data Quality checks, as it offers both an interactive UI but also the means to run them programmatically. Their Customer Support has also been reliably on time and helpful and it fostered collaboration in order to develop a better tool.Read all insights and reviews for Soda

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7. Telmai

5

Telmai was very easy to deploy and maintain compared to other tools I have used in this space. The UI is intuitive and easy to use, onboarding data to the tool is very simple, and using out of the box ML based data observability (ex., data drift, freshness, schema drift, etc.) is as easy as flipping a toggle. It works with all of the cloud based technologies we use in our stack, and is forward looking with a focus on the ability to work with open data formats (i.e., iceberg). I have been using Telmai to scan large data assets (hundreds of columns, some with nest formats / json, billions of rows), and have had no issues beyond initial cluster sizing setup one time during initial deployment, which the Telmai team was able to help with and resolve quickly. I have been very happy with this tool and look forward to continuing to use it.Read all insights and reviews for Telmai

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8. Sifflet

4.3

Sifflet has been useful in getting better visibility into the health of our data pipelines. It helped me quickly notice when datasets were not updating as expected, which saved time troubleshooting issues in dashboard.Read all insights and reviews for Sifflet

Where IBM Data Observability Scored Higher

Better evaluation and contracting

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