Lead Analytics Engineer at a tech vendor with 10,001+ employees
Real User
Top 20
Aug 1, 2026
I would probably give Monte Carlo an eight out of 10. I think it is very usable and beneficial overall for data monitoring. I really appreciate things such as the time since last update, which is easy to find, row count changes, and even some of the orchestration, such as execution time, which has been really cool. There are some really cool monitors that come out of the box. Monte Carlo has some other cool things such as field lineage and table lineage that have been pretty cool to use. I can use it right within that tool and do not have to go to Atlan to look at that. Setting up personal dashboards has been probably a little harder to do compared to Metaplane or Elementary, but at least the functionality is there. That is kind of the reason I would give Monte Carlo an eight out of 10. I think Monte Carlo's AI capabilities are fine overall. Monte Carlo seems to be doing what it needs to be doing as far as keeping the data secure in its product, even when using AI. I do not have much to say about that. I personally have not used the AI tool within Monte Carlo much, so I probably cannot say I have a definitive opinion on that. I believe I might have used it once in the past and it helped me figure out what I needed to know as far as using some of the tooling in it, but I do not remember what I was trying to use at the time. The first thing I would suggest to somebody brand new to Monte Carlo is to spend the time understanding monitors in general. That is for me the bread and butter of what the tool can do. I would suggest spending time in the developer documentation, understanding what each monitor does, some of the out of the box monitors such as volume testing, row count changes, and similar things. I think utilizing some of the custom fields or custom testing is really powerful if you know what you are doing and know how to implement that in a smart way. While the capability is there, I would suggest people really understand those two things specifically. I would recommend Monte Carlo overall. I gave it an eight out of 10 because I think it does its job in helping us monitor our data products and making sure that nothing is breaking that we are not aware of, as well as being actively aware of any anomalies that might exist.
Enterprise Network Architect at Concordia University-Wisconsin
Real User
Top 20
Jun 22, 2026
I would like to leave a review for the tool called Monte Carlo, which is a data quality analysis tool. I think they just changed the layout a little bit, and during my work, I didn't see any changes in the platform except for the AI feature, which I don't use anymore. Our final goal is to automate every data quality manual check into Monte Carlo so we don't have to do a lot of checks by ourselves. If I find something bizarre, I go to Monte Carlo and see if it's happening there as well, so it gives me confirmation that the issue is occurring and we can contact our supplier or the country to verify what it is. Monte Carlo is a really good tool; whenever I do data verification checks for the dashboards, and if I find something bizarre, I go to Monte Carlo and see if it's happening there as well, so it gives me confirmation that the issue is occurring and we can contact our supplier or the country to verify what it is. I would rate this product a 7 overall.
Data Engineer & Management & Governance Senior Analyst at a tech vendor with 10,001+ employees
Real User
Top 10
Jun 2, 2026
My advice for others looking to use Monte Carlo is to definitely go for it because it is quite useful, accurate, and saves a significant number of hours. Regarding Monte Carlo's AI capabilities, I am not sure about governance and security, but I find it very helpful for data observability. When linked with Collibra and Immuta, it indirectly contributes to data governance and security. Monte Carlo is deployed in my organization on the public cloud. Regarding Monte Carlo, people are not very aware of it compared to other capabilities, so I think they can work on improving their advertising efforts. I rate this review nine out of ten.
Data quality monitoring throughout the data lifecycle is very important, especially in this artificial intelligence era. If you feed garbage into artificial intelligence, it will hallucinate more and will not give you accurate results. It might divert into deploying many more agents and utilizing many more tokens rather than confining to a particular set of tokens. It is not only important from your data perspective, but also very important from your revenue perspective. The lost tokens are directly impacting an increase in costs or a decrease in revenue. Regarding the pricing, it is a bit expensive compared to traditional monitoring systems provided by other vendors. However, the extra features and the trust come with some cost, so I think it should be fine. I have worked with many customers who do not have any complaints. In fact, they migrated many other systems from traditional monitoring systems to Monte Carlo. The customers are accepting of this pricing model. Monte Carlo has many advantages compared to other solutions. As I mentioned, it has a lot of machine learning functionality and excellent user friendliness. The interface is quite crisp and the appearance is quite good. Traditional tools require some prior knowledge, but with Monte Carlo, you can onboard any user at any time. They can easily understand how to use that tool. The solution requires maintenance because new features get rolled out and you need to upgrade those features. During that time there is a little bit of a pain point, but that is acceptable because you will experience new functionality. If others are looking to implement this product, my advice is to robustly monitor their system with very little human intervention. Monte Carlo has an option where it will directly allow you to dig deep into the root cause and you just need to do a few clicks and it will get you to that data issue where it is happening. Very little human intervention is required for this. I give this solution an overall rating of eight out of ten.
AI Machine Learning Engineer at a tech vendor with 10,001+ employees
Real User
Top 10
May 28, 2026
Regarding Monte Carlo's security features, it has pretty good security, and they are doing a good job on the security side of things. Regarding Monte Carlo's AI capabilities, I would say its accuracy is around eight or nine out of ten. My advice to others looking into using Monte Carlo is to learn everything first before using it, rather than testing everything as you go. I would rate this review seven out of ten.
Data Governance Systems Specialist at a energy/utilities company with 1,001-5,000 employees
Real User
Top 5
Jan 12, 2026
For those looking into using Monte Carlo, I advise identifying the most critical data products first. Check data sets feeding regulatory reports, operational dashboards, and forecasting systems. Next, establish your SLAs and data quality expectations upfront. Whatever tool you deploy, do so iteratively, tune alerts to fit your domain patterns, and utilize lineage to build trust across teams. By doing so, instead of reactive data firefighting, you will enable proactive data reliability, essential for any data-driven energy business. I would rate this solution a 4 out of 5.
Associate Sr. Manager at Financial Insight Technology, Inc.
Real User
Aug 31, 2023
The product has centralized nodes and is a pioneer in the data observability domain. It has helped a lot in investigating system issues. It also saves a lot of time in identifying issues by improving data traffic. I rate Monte Carlo a nine out of ten.
Monte Carlo offers a comprehensive data observability platform that ensures reliable data pipelines and prevents data downtime by providing real-time monitoring and alerting, making it a crucial tool for data-driven organizations.Monte Carlo provides end-to-end visibility into data infrastructure, helping teams quickly identify, troubleshoot, and resolve data issues. This prevents costly data incidents and improves data trust. As data systems become more complex, maintaining accurate and...
I would probably give Monte Carlo an eight out of 10. I think it is very usable and beneficial overall for data monitoring. I really appreciate things such as the time since last update, which is easy to find, row count changes, and even some of the orchestration, such as execution time, which has been really cool. There are some really cool monitors that come out of the box. Monte Carlo has some other cool things such as field lineage and table lineage that have been pretty cool to use. I can use it right within that tool and do not have to go to Atlan to look at that. Setting up personal dashboards has been probably a little harder to do compared to Metaplane or Elementary, but at least the functionality is there. That is kind of the reason I would give Monte Carlo an eight out of 10. I think Monte Carlo's AI capabilities are fine overall. Monte Carlo seems to be doing what it needs to be doing as far as keeping the data secure in its product, even when using AI. I do not have much to say about that. I personally have not used the AI tool within Monte Carlo much, so I probably cannot say I have a definitive opinion on that. I believe I might have used it once in the past and it helped me figure out what I needed to know as far as using some of the tooling in it, but I do not remember what I was trying to use at the time. The first thing I would suggest to somebody brand new to Monte Carlo is to spend the time understanding monitors in general. That is for me the bread and butter of what the tool can do. I would suggest spending time in the developer documentation, understanding what each monitor does, some of the out of the box monitors such as volume testing, row count changes, and similar things. I think utilizing some of the custom fields or custom testing is really powerful if you know what you are doing and know how to implement that in a smart way. While the capability is there, I would suggest people really understand those two things specifically. I would recommend Monte Carlo overall. I gave it an eight out of 10 because I think it does its job in helping us monitor our data products and making sure that nothing is breaking that we are not aware of, as well as being actively aware of any anomalies that might exist.
I would like to leave a review for the tool called Monte Carlo, which is a data quality analysis tool. I think they just changed the layout a little bit, and during my work, I didn't see any changes in the platform except for the AI feature, which I don't use anymore. Our final goal is to automate every data quality manual check into Monte Carlo so we don't have to do a lot of checks by ourselves. If I find something bizarre, I go to Monte Carlo and see if it's happening there as well, so it gives me confirmation that the issue is occurring and we can contact our supplier or the country to verify what it is. Monte Carlo is a really good tool; whenever I do data verification checks for the dashboards, and if I find something bizarre, I go to Monte Carlo and see if it's happening there as well, so it gives me confirmation that the issue is occurring and we can contact our supplier or the country to verify what it is. I would rate this product a 7 overall.
My advice for others looking to use Monte Carlo is to definitely go for it because it is quite useful, accurate, and saves a significant number of hours. Regarding Monte Carlo's AI capabilities, I am not sure about governance and security, but I find it very helpful for data observability. When linked with Collibra and Immuta, it indirectly contributes to data governance and security. Monte Carlo is deployed in my organization on the public cloud. Regarding Monte Carlo, people are not very aware of it compared to other capabilities, so I think they can work on improving their advertising efforts. I rate this review nine out of ten.
Data quality monitoring throughout the data lifecycle is very important, especially in this artificial intelligence era. If you feed garbage into artificial intelligence, it will hallucinate more and will not give you accurate results. It might divert into deploying many more agents and utilizing many more tokens rather than confining to a particular set of tokens. It is not only important from your data perspective, but also very important from your revenue perspective. The lost tokens are directly impacting an increase in costs or a decrease in revenue. Regarding the pricing, it is a bit expensive compared to traditional monitoring systems provided by other vendors. However, the extra features and the trust come with some cost, so I think it should be fine. I have worked with many customers who do not have any complaints. In fact, they migrated many other systems from traditional monitoring systems to Monte Carlo. The customers are accepting of this pricing model. Monte Carlo has many advantages compared to other solutions. As I mentioned, it has a lot of machine learning functionality and excellent user friendliness. The interface is quite crisp and the appearance is quite good. Traditional tools require some prior knowledge, but with Monte Carlo, you can onboard any user at any time. They can easily understand how to use that tool. The solution requires maintenance because new features get rolled out and you need to upgrade those features. During that time there is a little bit of a pain point, but that is acceptable because you will experience new functionality. If others are looking to implement this product, my advice is to robustly monitor their system with very little human intervention. Monte Carlo has an option where it will directly allow you to dig deep into the root cause and you just need to do a few clicks and it will get you to that data issue where it is happening. Very little human intervention is required for this. I give this solution an overall rating of eight out of ten.
Regarding Monte Carlo's security features, it has pretty good security, and they are doing a good job on the security side of things. Regarding Monte Carlo's AI capabilities, I would say its accuracy is around eight or nine out of ten. My advice to others looking into using Monte Carlo is to learn everything first before using it, rather than testing everything as you go. I would rate this review seven out of ten.
For those looking into using Monte Carlo, I advise identifying the most critical data products first. Check data sets feeding regulatory reports, operational dashboards, and forecasting systems. Next, establish your SLAs and data quality expectations upfront. Whatever tool you deploy, do so iteratively, tune alerts to fit your domain patterns, and utilize lineage to build trust across teams. By doing so, instead of reactive data firefighting, you will enable proactive data reliability, essential for any data-driven energy business. I would rate this solution a 4 out of 5.
The product has centralized nodes and is a pioneer in the data observability domain. It has helped a lot in investigating system issues. It also saves a lot of time in identifying issues by improving data traffic. I rate Monte Carlo a nine out of ten.