DataMasque vs Quest Data Intelligence comparison

Why PeerSpot?

DataMasque and Quest Software are both solutions in the Data Governance category. DataMasque is ranked #29 with an average rating of 10.0, while Quest Software is ranked #17 with an average rating of 8.0. DataMasque holds a 0.4% mindshare in DG, compared to Quest Software’s 2.8% mindshare. Additionally, 100% of DataMasque users are willing to recommend the solution, compared to 100% of Quest Software users who would recommend it.

DataMasque

Read 4 DataMasque reviews

1,904 Views
514 Comparison Views

100% willing to recommend

Quest Data Intelligence

Read 22 Quest Data Intelligence reviews

6,485 Views
5,188 Comparison Views

100% willing to recommend

DataMasque

Quest Data Intelligence

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Executive SummaryUpdated on Jul 16, 2026

erwin Data Intelligence and DataMasque compete in data management and protection. erwin Data Intelligence leads in pricing and support, while DataMasque has a strong feature set aligned with perceived value.

Features: erwin Data Intelligence offers comprehensive data cataloging, automated data lineage, and impact analysis, ideal for data governance. DataMasque emphasizes data anonymization, privacy compliance, and real-time data protection, suitable for privacy-focused operations.

Room for Improvement: erwin Data Intelligence could enhance user-friendliness and streamline deployment. Improvements in cloud integration can broaden its appeal. DataMasque might benefit from expanding its integration capabilities and enhancing automation features. Further development in scalability can support larger deployments.

Ease of Deployment and Customer Service: DataMasque provides easy cloud-based deployment with efficient customer service. erwin Data Intelligence has more complex on-premise and hybrid options resulting in longer deployment times, balanced by dedicated support.

Pricing and ROI: erwin Data Intelligence has higher upfront costs, offering long-term benefits through its governance solutions. DataMasque's flexible pricing ensures quicker ROI, appealing to those focused on data privacy and compliance.

To learn more, read our detailed DataMasque vs. Quest Data Intelligence Report (Updated: September 2026).

Buyer's Guide

DataMasque vs. Quest Data Intelligence

September 2026

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

DataMasque

Ranking in Data Governance

29th

Average Rating

10.0

Reviews Sentiment

3.4

Number of Reviews

Ranking in other categories

Data Masking (8th)

Quest Data Intelligence

Ranking in Data Governance

17th

Average Rating

8.4

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Metadata Management (7th), AI Governance (3rd)

Mindshare comparison

As of October 2026, in the Data Governance category, the mindshare of DataMasque is 0.4%. The mindshare of Quest Data Intelligence is 2.8%, up from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Governance Mindshare Distribution
Product	Mindshare (%)
Quest Data Intelligence	2.8%
DataMasque	0.4%
Other	96.8%

Data Governance

Featured Reviews

Nierojan Pushparajan

Director of Software Engineering at TelliHealth

Masking sensitive test data has improved compliance and now enables safe, realistic debugging

The best features DataMasque offers that stand out to me are the flexibility to create rule sets and mask UUIDs, which have been really beneficial. Additionally, their customer service and technical support have been very helpful in getting this set up. The flexibility in creating rule sets has helped my team specifically because we have lots of dependencies on different databases and different database types. We have an RDS instance and an OpenSearch instance. Having those rule sets really allowed us to connect data between the two because the same data can be across multiple datasets. Creating these rules enabled us to not think too much about which ones need to be unique or which ones need to be the same across different tables. Whenever we run into issues or questions, we are able to talk to the DataMasque team, and they give us a reasonable technical response within a good timeframe that allows us to solve our issues pretty quickly. We have not had huge delays or blockers because of the communication between us and DataMasque. I appreciate the consistency to continue to improve the application, and there are new features coming out this year that will be tools we use. DataMasque is doing a great job of adding new features, and we will definitely be using them in the future. DataMasque has positively impacted our organization by already showing visible benefits in security and reduced risks for compliance, even though we are still in the process of setting up production and using it to the full extent. It will allow our developers to troubleshoot issues easily without the risk of viewing PHI or anything similar. We are seeing the benefits already of that, but we still need to complete the full integration and incorporate it into our workflows to see the full benefits, which we are currently in the process of. So far, it has been really good. By just masking production data, we can go into this masked database set to understand edge cases and issues as they come up from support without the developers having to look at any production data. They can go into this masked dataset and see the problem firsthand, something we would not have caught with our dev dataset because it is such a smaller amount of data, whereas production has a vast amount of data that we can look through.

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Jog Raj

Senior Consultant at a computer software company with 11-50 employees

Automated lineage and business glossaries have improved data understanding and collaboration

I am open to answering a few questions about erwin Data Intelligence and sharing my opinion about the product. Regarding the analytic part of the product, I find it very interesting that erwin Data Intelligence has its own inbuilt reporting toolset, with a new version coming out in January that will be AI-powered. This means you will be able to write analytical reports and questions based on the metadata and data lineage, making it more powerful than the current version, as AI will assist in creating those reports and performing analysis on the metadata. I find that the time taken to realize value from erwin Data Intelligence can be quite long. Creating a data catalog and developing data lineage takes significant time, and I expect that AI will help accelerate the process of creating data products by streamlining the steps involved. I can recommend erwin Data Intelligence to other users. I would rate this review as an eight out of ten overall.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Being able to generate the masking rules directly from the tool based on what it finds during the data discovery helps ensure that all sensitive fields are identified and masked."

"DataMasque has positively impacted our organization by already showing visible benefits in security and reduced risks for compliance, even though we are still in the process of setting up production and using it to the full extent."

"We were able to profile all 400+ databases in our environment, identify compliance data that was not known or identified previously and allow the teams a self-service option to restore to lower environments."

"We can now have a staging environment very much like a production environment, which improves our testing and product quality."

"It is a central place for everybody to start any ETL data pipeline builds. This tool is being heavily used, plus it's heavily integrated with all the ETL data pipeline design and build processes. Nobody can bypass these processes and do something without going through this tool."

"The data management is, obviously, key in understanding where the data is and what the data is. And the governance can be done at multiple levels. You have the governance of the code sets versus the governance of the business terms and the definitions of those business terms. You have the governance of the business data models and how those business data models are driving the physical implementation of the actual databases. And, of course, you have the governance of the mapping to make sure that source-to-target mapping is done and is being shared across the company."

"I can estimate that we lowered our time to market by 70 percent right now using these automation scripts, which is a really big thing."

"The solution saves time in data discovery and understanding our entire organization's data."

"Overall, DI's data cataloging, data literacy, and automation have helped our decision-makers because when a source wants to change something, we immediately know what the impact is going to be downstream."

"The main benefits end users receive from erwin Data Intelligence include saving time and money while streamlining processes."

"We use the codeset mapping quite a bit to match value pairs to use within the conversion as well. Those value pair mappings come in quite handy and are utilized quite extensively. They then feed into the automation of the source data extraction, like the source data mapping of the source data extraction, the code development, forward engineering using the ODI connector for the forward automation."

"This type of solution was key to moving our entire company in the right direction by getting everyone to think about data governance."

More Quest Data Intelligence pros

Cons

"UUID generation could be improved."

"If we are talking about the business side of the product, maybe the Data Literacy could be made a bit simpler. You have to put your hands on it, so there is room for improvement."

"There is room for improvement with respect to the connector and how to connect to the structured and unstructured database."

"There may be some opportunities for improvement in terms of the user interface to make it a little bit more intuitive. They have made some good progress. Originally, when we started, we were on version 9 or 10. Over the last couple of releases, I've seen some improvements that they have made, but there might be a few other additional areas in UI where they can make some enhancements."

"We still need another layer of data quality assessments on the source to see if it is sending us the wrong data or if there are some issues with the source data. For those things, we need a rule-based data quality assessment or scoring where we can assess tools or other technology stacks. We need to be able to leverage where the business comes in, defining some business rules and have the ability to execute those rules, then score the data quality of all those attributes. Data quality is definitely not what we are leveraging from this tool, as of today."

"There are always ways to improve things. For example, we can use AI to be able to find out something. When we are typing something, if we don't know the exact term, Artificial Intelligence would be useful to find terms that are phonetically or syntactically similar. Instead of having to type in the exact name, they can provide those in the list. So, they can provide AI support for the search because when you have thousands and thousands of terms, it is hard to remember all the names."

"The metadata ingestion is very nice because of the ability to automate it. It would be nice to be able to do this ingestion, or set it up, from one place, instead of having to set it up separately for every data asset that is ingested."

"The technical support could be improved."

"There were some issues when drawing the data models. If you have more than 500 or 600 tables, it takes a long time to display those in the right position on the screen."

More Quest Data Intelligence cons

Pricing and Cost Advice

Information not available

"Smart Data Connectors have some costs, and then there are user-based licenses. We spend roughly $150,000 per year on the solution. It is a yearly subscription license that basically includes the cost for Smart Data Connectors and user-based licenses. We have around 30 data stewards who maintain definitions, and then we have five IT users who basically maintain the overall solution. It is not a SaaS kind of operation, and there is an infrastructure cost to host this solution, which is our regular AWS hosting cost."

"We operate on a yearly subscription and because it is an enterprise license we only have one. It is not dependent on the number of users."

"Erwin Data Catalog is very expensive."

"There is an additional fee for the server maintenance."

"The licensing cost is around $7,000 for user. This is an estimation."

"erwin's pricing was cheaper than its competitors."

"You buy a seat license for your portal. We have 100 seats for the portal, then you buy just the development licenses for the people who are going to put the data in."

"I am not very familiar with its pricing. I know it is not cheap, but it is also not super expensive. It depends on the company size. For a company making $1 million, it is very expensive. For a company making 10 million and above, it might be okay."

More Quest Data Intelligence pricing and cost advice

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Top Industries

By visitors reading reviews

Energy/Utilities Company

24%

Construction Company

18%

Outsourcing Company

14%

Comms Service Provider

11%

Financial Services Firm

14%

Outsourcing Company

10%

Government

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	4
Large Enterprise	16

Questions from the Community

What is your experience regarding pricing and costs for DataMasque?

As we are still in the early stages, I can't say anything definitive about pricing yet, but I believe it will be usage-based, which should work well.

See all answers

What needs improvement with DataMasque?

UUID generation could be improved. Although UUIDs were masked correctly, some validation rules were not supported, requiring a switch from validators to pattern matching. Expanding native UUID vali...

See all answers

What is your primary use case for DataMasque?

I created a staging environment with production-like data.

See all answers

What needs improvement with erwin Data Intelligence by Quest?

In my opinion, the analytics part of erwin Data Intelligence is not satisfactory. The name 'Intelligence' is not related specifically to analytics; it is focused on data governance with no advanced...

See all answers

What is your primary use case for erwin Data Intelligence by Quest?

My main use case for erwin Data Intelligence is applying it and the DQ Labs in a financial institution.

See all answers

What advice do you have for others considering erwin Data Intelligence by Quest?

The integration of business glossaries has significantly helped improve collaboration in our organization. We define the business glossaries first during meetings with the business, and then we bul...

See all answers

Comparisons

Informatica Intelligent Data Management Cloud (IDMC) vs DataMasque

Compared 58% of the time

Perforce Delphix vs DataMasque

Compared 42% of the time

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Collibra Platform vs Quest Data Intelligence

Compared 17% of the time

Alation Data Catalog vs Quest Data Intelligence

Compared 9% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Quest Data Intelligence

Compared 6% of the time

Global Data Excellence DEMS vs Quest Data Intelligence

Compared 5% of the time

Microsoft Purview Data Governance vs Quest Data Intelligence

Compared 4% of the time

More Quest Data Intelligence Competitors

Product Reports

Buyer's Guide

Data Masking

September 2026

Download DataMasque product report

Buyer's Guide

Quest Data Intelligence

September 2026

Download Quest Data Intelligence product report

Also Known As

No data available

erwin DG, erwin Data Governance, erwin Data Catalog

Overview

DataMasque offers robust data masking, maintaining referential integrity while ensuring security and compliance. Its features support realistic dataset testing without exposing sensitive data, integrating seamlessly across databases.

DataMasque is designed to protect PII and PHI by masking sensitive information across interconnected databases and cloud platforms. This ensures applications, reporting, and integrations are not impacted by structural inconsistencies. Its adaptable rule set and auto-generation of masking rules aid in swift deployment, enhancing data security frameworks and accelerating release cycles. DataMasque also provides strong technical support and continues to enhance its capabilities with ongoing improvements. Users find value in its existing functionality while looking forward to further usability enhancements, including expanded capabilities for unstructured data and improved post-masking validation.

What Are DataMasque's Main Features?

Identical Data Masking: Ensures masked data retains the appearance and formatting of original data, enhancing usability.
Referential Integrity Preservation: Maintains data relationships across systems, crucial for accurate testing and reporting.
Automation: Facilitates seamless integration and efficient masking operations without manual intervention.
Scalability: Supports growth and handles large datasets across distributed environments.
Compliance and Security Alignment: Helps meet regulatory requirements like GDPR while maintaining robust security.
Speed and Accuracy: Guarantees rapid data processing with precise masking rule application.
Rule Set Flexibility: Offers custom rules to adapt to specific data structures and needs.
Auto-Generation of Masking Rules: Automatically creates rules, speeding up deployment and reducing configuration errors.

What Benefits and ROI Should Users Consider?

Data Security Enhancement: Significantly reduces the risk of data breaches by masking PII and PHI.
Improved Compliance: Aligns with regulatory standards, helping avoid legal and financial penalties.
Faster Release Cycles: Reduces time to market by enabling secure, realistic testing environments.
Risk Reduction: Minimizes hazards associated with data exposure in non-production environments.
Integration Ease: Smoothly integrates across databases, maintaining consistency and accuracy.

DataMasque is implemented across technology, healthcare, finance, and retail industries to safeguard sensitive data without disrupting operations. It assures that development, testing, and analytics use environments that mirror live systems closely, which is critical for sectors handling large volumes of personal data.

DataMasque

erwin Data Intelligence is a comprehensive platform for metadata management, data cataloging, and governance. It enables organizations to gain insights, improve traceability, and streamline compliance through its advanced features.

Focusing on data lineage, metadata repositories, and seamless integrations, erwin Data Intelligence provides a unified perspective of enterprise data. Its robust capabilities include Smart Data Connectors for automation, efficient data visualization with mind maps, and adaptable metadata properties. While the platform integrates well into existing systems, areas for improvement include automation, SDK inconsistencies, and the need for better data quality assessments. Use cases highlight its importance in enhancing business data models and regulatory compliance.

What are the key features of erwin Data Intelligence?

Single Source of Truth: Centralizes data for consistent information across the organization.
Smart Data Connectors: Automate connections to data sources, enhancing efficiency.
Data Lineage: Tracks data pathways for better traceability and understanding.
Business Glossaries: Enables clear definitions and shared vocabulary for all users.
Metadata Management: Streamlines metadata organization and retrieval.
Impact Analysis: Assesses implications of data changes rapidly and accurately.

What benefits should users look for in reviews?

Enhanced Efficiency: Automation scripts reduce manual processes and increase productivity.
Improved Data Visualization: Mind maps facilitate an intuitive understanding of complex data.
Customizable Properties: Adapt metadata attributes to fit specific organizational requirements.
Unified View: Offers a comprehensive perspective of data governance practices.

Industries implementing erwin Data Intelligence often focus on mapping data sources and integrating governance with ETL tools. This supports comprehensive data management strategies, enabling business teams to better locate, understand, and utilize data effectively. Its application in metadata management and automated reporting is particularly valuable in sectors requiring stringent regulatory compliance.

Quest Software

Sample Customers

Information Not Available

Oracle, Infosys, GSK, Toyota Motor Sales, HSBC

Buyer's Guide

DataMasque vs. Quest Data Intelligence

September 2026

Free Report: DataMasque vs. Quest Data Intelligence

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