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Quest Data Intelligence vs SAP Data Hub [EOL] comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Jul 16, 2026

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

Quest Data Intelligence
Average Rating
8.4
Reviews Sentiment
7.1
Number of Reviews
22
Ranking in other categories
Data Governance (17th), Metadata Management (7th), AI Governance (3rd)
SAP Data Hub [EOL]
Average Rating
7.6
Reviews Sentiment
6.8
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Featured Reviews

Jog Raj - PeerSpot reviewer
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.
VM
GTM Lead at Capgemini
The solution is seamless, but the database sometimes leads to confusion
We used to have multiple different kinds of databases, which internally, had different compliance levels. Retention management is very different now. If the policy is live and the claim has been completed, I couldn't archive the claim. I needed to keep a reference integrity of that claim and understand which policy paid out the claim. With this solution, the policy came in six months ago and qualified for archiving. The claim had been paid and in every environment, the claim had been closed, including the reporting system, the claims system, etc. With the payment set gateway, I can just go and archive. But, we had a hard time during this process. I rate the overall solution a seven out of ten.

Quotes from Members

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

Pros

"We always know where our data is, and anybody can look that up, whether they're a business person who doesn't know anything about Informatica, or a developer who knows everything about creating data movement jobs in Informatica, but who does not understand the business terminology or the data that is being used in the tool."
"Erwin DI checked all the boxes for us."
"By using the data catalog, we have definitely improved in terms of maturity as a data-driven decision-maker organization, and we are now getting to a level where everybody understands the data, understands how it is organized, and how they can use this data for different business decisions."
"The automated data lineage and impact analysis being driven from the mapping documents are astounding in reducing the time to research impact analysis from six to 16 weeks down to minutes, because it's a couple of clicks with a mouse."
"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 erwin Data Intelligence provides to me include better decision-making, saving money and time by applying governance policies and procedures on the actual technical data, ensuring decisions are based on KPIs that are governed from source to end, and providing functionality for data classification to prevent unauthorized access through integration with the infrastructure team using DLP methodology."
"The client is thrilled with higher quality, lower-cost products, and the services."
"The data mapping manager is the most valuable feature."
"Having this solution enables us to approach our clients to upgrade their databases, and we upgrade them according to their business requirements."
"SAP is one of the most seamless ERPs that have integrated SAP archiving within Excel. I have not seen this with any other database."
"They lead in terms of business functions, and no other solution has business functions already implemented to perform business analysis, with a lot of prebuilt business functions for machine learning and orchestration that we can use directly to get an analysis out from the existing enterprise data."
"The most valuable feature is the S/4HANA 1909 On-Premise"
"Its connection to on-premise products is the most valuable. We mostly use the on-premise connection, which is seamless. This is what we prefer in this solution over other solutions. We are using it the most for the orchestration where the data is coming from different categories. Its other features are very much similar to what they are giving us in open source. Their push-down approach is the most advantageous, where they push most of the processing on to the same data source. This means that they have a serverless kind of thing, and they don't process the data inside a product such as Data Hub. They process the data from where the data is coming out. If it is coming from HANA, to capture the data or process it for analytics, orchestration, or management, they go to the HANA database and give it out. They don't process it on Data Hub. This push-down approach increases the processing speed a little bit because the data is processed where it is sitting. That's the best part and an advantage. I have used another product where they used to capture the data first and then they used to process it and give it. In Data Hub, it is in reverse. They process it first and give it, and then they put their own manipulations. They lead in terms of business functions. No other solution has business functions already implemented to perform business analysis. They have a lot of prebuilt business functions for machine learning and orchestration, which we can use directly to get an analysis out from the existing data. Most of the data is sitting as enterprise data there. That's a major advantage that they have."
 

Cons

"The versioning can sometimes be confusing because we use the publishing feature for the mapping. Technical analysts sometimes have two versions, and they should know that the public version is the correct one."
"Another area where it can improve is by having BB-Graph-type databases where relationship discovery and relationship identification are much easier."
"The SDK behind this entire product needs improvement. The company really should focus more on this because we were finding some inconsistencies on the LDK level. Everything worked fine from the UI perspective, but when we started doing some deep automation scripts going through multiple API calls inside the tool, then only some pieces of it work or it would not return the exact data it was supposed to do."
"There is room for improvement in automation, no question."
"One big improvement we would like to see would be the workflow integration of codeset mapping with the erwin source to target mapping. That's a bit clunky for us. The two often seem to be in conflict with one another. Codeset mappings that are used within the source to target mappings are difficult to manage because they get locked."
"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."
"There is room for improvement with the data cataloging capability. Right now, there is a list of a lot of sources that they can catalog, or they can create metadata upon, but if they can add more then that would be a good plus for this tool."
"Improvement is required for the AIMatch feature, which is supposed to help automatically discover relationships in data."
"Nowadays there are some inconsistencies in data bases, however, they upgrade and release the versions to market."
"Its performance needs improvement. It is a little slow. It is not the best in the market, and there are other products that are much better than this."
"The company has everything offshore."
"In 2018, connecting it to outside sources, such as IoT products or IoT-enabled big data Hadoop, was a little complex. It was not smooth at the beginning. It was unstable. It took a lot of time for the initial data load. Sometimes, the connection broke, and we had to restart the process, which was a major issue, but they might have improved it now. It is very smooth with SAP HANA on-premise system, SAP Cloud Platform, and SAP Analytics Cloud. It could be because these are their own products, and they know how to integrate them. With Hadoop, they might have used open-source technologies, and that's why it was breaking at that time. They are providing less embedded integration because they want us to use their other products. For example, they don't want to go and remove SAP Analytics Cloud and put everything in Data Hub. They want us to use SAP Analytics Cloud somewhere else and not inside the Data Hub. On the integration part, it lacks real-time analytics, and it is slow. They should embed the SAP Analytics Cloud inside Data Hub or support some kind of analysis. They do provide some analysis, but it is not extensive. They are moreover open source. So, we need a lot of developers or data scientists to go in and implement Python algorithms. It would be better if they can provide their own existing algorithms and give some connections and drop-down menus to go and just configure those. It will make things really quick by increasing the embedded integrations. It will also improve the process efficiency and processing power. Its performance needs improvement. It is a little slow. It is not the best in the market, and there are other products that are much better than this. In terms of technology and performance, it is a little slow as compared to Microsoft and other data orchestration products. I haven't used other products, but I have read about those products, their settings, and the milliseconds that they do. In Azure Purview, they say that they can copy, manage, or transform the data within milliseconds. They say that they can transform 100 gigabytes of data within three to five seconds, which is something SAP cannot do. It generally takes a lot of time to process that much amount of data. However, I have never tested out Azure."
 

Pricing and Cost Advice

"The solution is aggressively priced."
"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."
"The licensing cost was very affordable at the time of purchase. It has since been taken over by erwin, then Quest. The tool has gotten a bit more costly, but they are adding more features very quickly."
"erwin was at a good price. The federal government wouldn't buy something if the pricing wasn't good."
"The whole suite, not just the DI but the modeling software, the harvester, Mapping Manager — everything we have — is about $100,000 a year for our renewals. That works out to each module being something like $8,000 to $10,000."
"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."
"The price is reasonable, and a subscription is required."
"There is an additional fee for the server maintenance."
"The Cloud is very expensive, but SAP HANA previous service is okay."
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Outsourcing Company
10%
Government
9%
Construction Company
8%
Outsourcing Company
14%
Manufacturing Company
13%
Construction Company
10%
Financial Services Firm
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise4
Large Enterprise16
No data available
 

Questions from the Community

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...
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.
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...
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Also Known As

erwin DG, erwin Data Governance, erwin Data Catalog
No data available
 

Overview

 

Sample Customers

Oracle, Infosys, GSK, Toyota Motor Sales, HSBC
Kaeser Kompressoren, HARTMANN
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