

Find out in this report how the two Data Governance solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
We have seen a return on investment, having saved close to 10.5% of our costs, with expected ROI growing yearly by about 17%.
Instead of going into Google or consulting a colleague, I can just go to Alation Data Catalog, search there, and understand everything by myself.
If three engineers save ten hours each per month using erwin Data Modeler versus manual modeling, that equals three hundred sixty hours saved per year.
It replaces manual charting in Visio with a structured tool, providing significant return on investment.
If the modeling is compromised, then the entire structure will be compromised.
Some of them have dragged on for six months, which is much longer than we would ever want some support issues to go on.
we have defined several SLAs that have resolved critical issues and incidents with excellent support.
we have been able to contact them, and they have been able to help us whenever we have had issues
The quality and speed of their support are excellent; everyone is very helpful, and they can solve problems quickly.
This rating reflects my ability to effectively utilize the tool and get support for licensing issues, installation errors, or corrupted repositories end-to-end.
Quest is committed to keeping the product robust.
As data sizes grow, we can also scale categorizations, calibrations, and the types of data we ingest and catalog through this tool.
Alation Data Catalog's scalability is really good since everything is cloud and everything gets pulled into the system via Snowflake and DBT automatically.
Alation Data Catalog's scalability has met our needs as our data and user base has grown.
I would rate it probably a nine, making it a leader in data modeling.
erwin Data Modeler had a very good standardization infrastructure and supported a controlled multi-user environment with check-ins and check-outs.
Performance can degrade during larger collaborations and requires tuning for optimal performance.
It has never gone down when we wanted to access it.
Alation Data Catalog has been stable for me, as I haven't seen much downtime.
From my experience, Alation Data Catalog is stable.
This lack of an auto-save methodology can be improved so that if a system crash occurs, work can be saved and rework can be avoided.
New versions often introduce enhanced features but may cause model crashes due to memory exhaustion.
Sometimes when I want to open the attribute editor, it stops working and the whole application freezes.
The platform needs improvement in integrating with BI tools, reducing manual efforts, and facilitating a smooth connection.
Additionally, bringing in AI capabilities to automate tracking and labeling can optimize how we handle data information levels.
Alation Data Catalog can be improved by enhancing their lineage capabilities to make it easier for us as users to search the lineage without having to use the Alation Data Catalog user interface.
The previous version of erwin Data Modeler used to crash unaccountably, but this one hasn't ever crashed on me, so it's been a lot more stable than the previous version that we had.
There are many features, and I would expect good documentation detailing each feature, including when and how to use it, to be very useful because data modeling is not very popular in the data area and there aren't many educational videos regarding erwin Data Modeler.
Erwin Data Modeler could improve in areas such as the interface, as there are features like copy and paste, creating duplicates, and the visualization elements and toolbars which feel quite old.
Collibra is a little bit expensive than Alation, but I presented the benefits of having that platform, so that little bit of money was not a big deal.
Discussions with the vendor partners provided a detailed breakdown of costs and how they can be reduced over time.
For a cloud or SaaS standard edition, it typically runs around two hundred to two hundred ninety-nine US dollars per month.
It is more targeted toward an enterprise level since organizations looking to store business information and relationship values may consider the pricing.
It was the only data catalog tool where I could save and even create SQL queries.
Importantly, we establish a single source of truth, centralizing metadata and standardizing definitions, which minimizes confusion and helps with consistent data use across the organization.
My stakeholders can find relevant data assets quickly using a Google search without needing technical jargon, reducing the time spent searching for data.
One of the key aspects of data governance is defining the data dictionary and clearly identifying which data is accessible by whom and what is not accessible, particularly regarding PII-related data.
The way the data is organized and you have a visual of that organization helps a great deal in terms of trying to remember what you did and trying to retrieve the information.
One of the best aspects of the tool is its reverse engineering capability.
| Product | Market Share (%) |
|---|---|
| Alation Data Catalog | 4.0% |
| erwin Data Modeler | 0.3% |
| Other | 95.7% |

| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 2 |
| Large Enterprise | 15 |
| Company Size | Count |
|---|---|
| Small Business | 15 |
| Midsize Enterprise | 3 |
| Large Enterprise | 34 |
Alation pioneered the data catalog market and is now leading its evolution into a platform for a broad range of data intelligence solutions including data search & discovery, data governance, lineage, stewardship, analytics, and digital transformation.
Thanks to its powerful Behavioral Analysis Engine, inbuilt collaboration capabilities, and open interfaces, Alation combines machine learning with human insight to successfully tackle even the most demanding challenges in data and metadata management. With Alation, analysts are empowered to search, query and collaborate on their data to achieve faster, more accurate insights.
Trusted by 400+ enterprises worldwide like Salesforce, Nasdaq, LinkedIn, Finnair, and Cisco.
Erwin Data Modeler provides an effective approach to visualizing and managing data models. It assists in creating, reversing, and synchronizing data models with ease, supporting logical and physical transitions while enhancing understanding across teams.
Erwin Data Modeler is a comprehensive tool designed for professional database management. It offers capabilities to organize and enforce standards, automating script generation with robust reverse engineering and DDL output. Users can manage complex data environments, capitalize on integration with data intelligence, and maintain large-scale databases smoothly. Despite its strengths, improvements in multi-language support, database integration, and reporting features are needed. Users benefit from extensive support for conceptual, logical, and physical database modeling, enhancing architectural design and data governance for platforms like SQL Server, Oracle, and Teradata.
What are the key features of Erwin Data Modeler?Erwin Data Modeler finds application in industries focused on robust data management, implementing it for enterprise data warehouses, business domain models, and operational systems. It supports architectural design and governance, aligning with business applications demanding precise data representation and visualization.
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