Oracle Enterprise Data Quality (EDQ) vs SAS Data Management comparison

Oracle and SAS are both solutions in the Data Quality category. Oracle is ranked #13, while SAS is ranked #7 with an average rating of 9.0. Oracle holds a 3.6% mindshare in DQ, compared to SAS’s 3.4% mindshare. Additionally, 88% of Oracle users are willing to recommend the solution, compared to 88% of SAS users who would recommend it.

Oracle Enterprise Data Qual...

Read 8 Oracle Enterprise Data Quality (EDQ) reviews

1,188 Views
1,176 Comparison Views

88% willing to recommend

SAS Data Management

Read 18 SAS Data Management reviews

3,799 Views
874 Comparison Views

88% willing to recommend

Oracle Enterprise Data Qual...

SAS Data Management

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Executive SummaryUpdated on Mar 15, 2026

Oracle Enterprise Data Quality and SAS Data Management are competing products in the data quality and management sector. SAS Data Management appears to have the upper hand with its comprehensive features, despite higher costs.

Features: Oracle EDQ offers data profiling, data cleansing, and integration capabilities, ensuring data accuracy and consistency. Its entity matching is effective, providing a unified data view. It is versatile with strong integration with third-party applications. SAS Data Management provides advanced analytics, data integration, and reliable scalability. It features powerful data standardization, transformation, and verification capabilities, alongside robust data governance and integration tools, accommodating complex data environments.

Room for Improvement: Oracle EDQ could enhance its scalability and analytics capabilities to compete with larger data environments. Its integration with non-Oracle systems might require improvements for seamless cross-platform utility. Additionally, network performance optimization would enhance data processing speed. SAS Data Management may benefit from a more intuitive user interface to attract non-technical users. Simplifying metadata management processes and reducing setup complexity could improve user experience. Its advanced features might require streamlining for ease of use for new users.

Ease of Deployment and Customer Service: Oracle EDQ provides a smooth deployment process with effective customer support for quick setup and issue resolution. SAS Data Management also facilitates easy deployment, complemented by robust support infrastructure, assisting in complex configurations and maintenance.

Pricing and ROI: Oracle EDQ is competitively priced, offering strong ROI with lower initial costs, appealing to budget-conscious organizations. SAS Data Management, despite higher initial expenditure, delivers significant long-term benefits and superior ROI due to its extensive capabilities and scalability, attracting businesses prioritizing comprehensive features.

To learn more, read our detailed Oracle Enterprise Data Quality (EDQ) vs. SAS Data Management Report (Updated: April 2026).

Buyer's Guide

Oracle Enterprise Data Quality (EDQ) vs. SAS Data Management

April 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

Oracle Enterprise Data Qual...

Ranking in Data Quality

13th

Average Rating

8.4

Reviews Sentiment

7.9

Number of Reviews

Ranking in other categories

No ranking in other categories

SAS Data Management

Ranking in Data Quality

7th

Average Rating

8.6

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Integration (28th), Data Governance (23rd)

Mindshare comparison

As of May 2026, in the Data Quality category, the mindshare of Oracle Enterprise Data Quality (EDQ) is 3.6%, up from 1.7% compared to the previous year. The mindshare of SAS Data Management is 3.4%, up from 2.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Mindshare Distribution
Product	Mindshare (%)
SAS Data Management	3.4%
Oracle Enterprise Data Quality (EDQ)	3.6%
Other	93.0%

Data Quality

Featured Reviews

Venkatraman Bhat

Deliver Head - Database and Infrastructure Cloud Services at Tech Mahindra Limited

Fast, has good extraction, validation, and transformation features, and provides good support

Though validation is good and fast enough in Oracle Data Quality, an area for improvement is the accuracy of the validation. Though the solution offers multidimensional validation, it needs a bit more improvement in the accuracy aspect because smaller products can offer better accuracy in terms of validation compared to Oracle Data Quality. What I'd like to see from the solution in its next release, is an increase in compliances and regulations that would allow it to cover all industries because multiple verticals demand data quality nowadays, and this improvement will be helpful as Oracle Data Quality is an in-built delivered solution.

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Fernanda Knopki

Data Scientist & Scrum Master at Volvo Group

Has supported centralized data access and improved governance through unified administration

The best features I appreciate about SAS Data Management tool are that it's easy to create the flows and schedule data, and the tables are not too big, making it easy to control the ETL process, including user access which is also easy to manage in SAS. The data integration tools with SAS Data Management were quite good because they allowed me to do all lineage, to manage and see all the variables, find the libraries where the data is, and understand the data in just one tool. Offering another development environment combining code and predefined objects, where I could just drag and drop to achieve what I needed, although some objects were not too good, but some were effective. The metadata management feature of SAS Data Management helps a lot; creating your data marts or data lake with good naming conventions, library conventions, and so on is very important because it allows easy queries to find the whole structure, though I think metadata governance also depends on first definitions, not only on the tool. SAS Data Management is very good for data governance; for me, it was the best tool I worked with in data management, although I don't have too much experience with others, and it truly helped a lot.

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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

"I have found the most valuable features to be data cleansing and deduplication."

"OEDQ helped us to define Data Quality issues from a business perspective by business users and ability to manage those issues within our ETL tool (Oracle Data Integrator - ODI)."

"The quality of data has vastly improved and our business intelligence reports are accurate in real time."

"The features I like most about Oracle Data Quality include extraction, transformation, and validation, which makes it a multipurpose product such as Oracle GoldenGate and Oracle Data Integrator. I also like that Oracle Data Quality is very fast, so you can use it for a large volume of data within a short period. You have to do the validation very quickly, so the solution helps in that area of data quality. Another feature of Oracle Data Quality that I like is the MDM (Master Data Management) where you'll have a single source of protection, and this makes the solution perfect and helpful to my company."

"Oracle Data Quality gives you value for money and better ROI, especially when it's deployed on the cloud, because it's fast, ready to use, and you can just subscribe and provision it, then start using it without great effort for setup, installation, or configuration."

"Oracle EDQ is indeed a state-of-the-art tool that will help you understand, improve, protect, and govern the quality of your enterprise data in a single integrated and collaborative environment."

"With Oracle Data Quality, the most valuable feature is entity matching."

"However, I think Oracle Data Quality is definitely worth giving a try."

More Oracle Enterprise Data Quality (EDQ) pros

"For the warehouse and BI environment, SAS is very quick, very intuitive, very, very powerful, and very valuable."

"There is a huge ROI."

"Their software pricing offers the best value for money on the market."

"DataFlux in my opinion is an excellent high end solution for enterprise wide data management."

"Its robustness is valuable. It is a full-fledged suite. We have a data warehouse model, and there are also a lot of data quality management tools. The repository and all other tools are there. So, it is a full package in terms of reporting tools."

"The ROI can be very high in terms of improving the reliability and accuracy of data used in forecasting and marketing models (ex: customer data, sales data)."

"The tool is reliable, quick, and powerful."

"I find the solution to be very stable."

More SAS Data Management pros

Cons

"Mobile support and mobile app can be implemented, since business users generally prefers to work with their laptops and mobile phones."

"If the length of time required for deployment was reduced then it would be very helpful."

"Oracle is currently not that intuitive. We need to use programmers to write code for a lot of the procedures."

"Integration performance and availability of out of the box integration to more products (ERP Tools, Data Cleansing Software etc.)."

"Oracle Data Quality should integrate with data warehousing solutions such as Azure and CWS Office. For example, having the ability to integrate with tools, such as Azure Synapse and SQL data warehousing would be a great benefit."

"The initial setup was complicated, there is a lot of configuration that needs to be done."

"Though the solution offers multidimensional validation, it needs a bit more improvement in the accuracy aspect because smaller products can offer better accuracy in terms of validation compared to Oracle Data Quality."

"Oracle is currently not that intuitive. We need to use programmers to write code for a lot of the procedures. We need to have them write CL SQL code and write a CL script."

More Oracle Enterprise Data Quality (EDQ) cons

"We find we often have to go back and re-train users when there are changes made to the solution because the changes are not intuitive."

"The pricing of the solution needs to be improved. They need to work to make it more affordable."

"The solution is quite expensive and hard to install/configure."

"Complex, as usual with SAS"

"The stability could be a bit better. It's lacking in this area right now."

"Native SAP integration or more efficient SAP is needed."

"Integration and cloud migration are areas where we see potential for improvement."

"The solution could use better documentation."

More SAS Data Management cons

Pricing and Cost Advice

"The vendor needs to revisit their pricing strategy."

"The price of this solution is comparable to other similar solutions."

"The licensing model for vCenter is a perpetual license with yearly payments."

"The tool is a bit expensive."

"The solution is expensive."

"While it is even free for personal use on the cloud, it can be expensive for desktop installations and enterprise use."

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Top Industries

By visitors reading reviews

Manufacturing Company

14%

Financial Services Firm

Retailer

University

Financial Services Firm

20%

Healthcare Company

Government

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Midsize Enterprise	2
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	7
Midsize Enterprise	1
Large Enterprise	8

Questions from the Community

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What is your experience regarding pricing and costs for SAS Data Management?

From my experience, SAS Data Management is an expensive tool.

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What needs improvement with SAS Data Management?

There is significant room for improvement, especially with regard to using a hybrid approach that involves both CAS and persistent storage.

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What is your primary use case for SAS Data Management?

The main idea for using SAS Data Management was to bring all data inside SAS. I brought the finance area and the sales area in the company that I have worked, along with some market planning data, ...

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Product Reports

Buyer's Guide

Data Quality

May 2026

Oracle Enterprise Data Quality (EDQ) report

Download Oracle Enterprise Data Quality (EDQ) product report

Buyer's Guide

SAS Data Management

April 2026

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Also Known As

Datanomic

SAS Data Management Platform, Data Management Platform, DataFlux

Overview

Oracle Enterprise Data Quality is a comprehensive tool for improving data integrity through address verification, profiling, cleansing, and synchronization.

Oracle Enterprise Data Quality empowers organizations to manage their data by ensuring integrity and consistency. It provides efficient address verification, data profiling, cleansing, and synchronization. With capabilities like entity matching, deduplication, extraction, transformation, and validation, it supports diverse data types to enhance data quality processes. While it is seamless in data matching and third-party app integration, the platform benefits organizations by supporting Master Data Management for consolidated data protection. However, improvements in documentation, ERP and warehouse integration, cloud and mobile support, and reduced deployment time could enhance the user experience. Pricing strategy and installation challenges, especially involving coding, need attention for broader accessibility.

What are the main features of Oracle Enterprise Data Quality?

Address Verification: Ensures accuracy in address data.
Data Profiling: Analyzes data characteristics for better insights.
Cleansing: Refines data by removing inaccuracies.
Data Synchronization: Aligns data across platforms effortlessly.
Entity Matching: Identifies and matches related entities.
Deduplication: Eliminates redundant data entries efficiently.
Transformation and Validation: Converts data to required formats, verifying its accuracy.

What benefits and ROI can users expect from Oracle Enterprise Data Quality?

Data Integrity: Maintains consistent data accuracy.
Reduced Redundancy: Minimizes duplicate data across systems.
Enhanced Data Governance: Strengthens data management strategies.
Improved Accuracy: Boosts the precision of data validation.
Efficient Data Processing: Accelerates data handling routines.

Industries like education find Oracle Enterprise Data Quality invaluable for systems such as university fundraising, where tracking donor contributions accurately is crucial. Used in data governance, it manages quality during ETVL processes ensuring high precision for data warehouses and Data Lakehouses.

Oracle

SAS Data Management provides data integration, governance, and robust reporting tools. It connects to diverse data sources, ensuring quality management and enabling data analysis for technical and non-technical users.

SAS Data Management features flexible data flow creation, scheduling, and ETL control. It enhances data integration and metadata management with tools that support data standardization. Users benefit from its importing and exporting capabilities, connecting to multiple sources. It facilitates improved data quality management and offers a flexible language for diverse needs. Data visualization capabilities further support decision-making across industries, automating reports and data warehouses.

What are the key features of SAS Data Management?

User-friendly interface: Intuitive navigation facilitates ease of use.
Easy data flow creation: Streamlines processes for seamless data management.
Scheduling and ETL control: Automates tasks for increased efficiency.
Data integration: Connects datasets from multiple origins.
Metadata management: Ensures consistency and quality of data.
Data governance: Provides tools for managing data compliance.
Robust reporting: Delivers comprehensive insights through detailed reports.

What benefits and ROI should be considered?

Data standardization: Enhances data predictability and usability.
Data quality management: Improves accuracy and integrity.
Flexible language: Adapts to specific user requirements.
Import/export capabilities: Facilitates seamless data transitions.
Support for diverse users: Offers tools suitable for varied skill levels.
Automation features: Saves time by reducing manual processes.

SAS Data Management helps industries like finance integrate diverse data sources for analytics and reporting. It is used for tasks such as financial reporting, credit risk analysis, and data cleansing. Through user-driven automation, it aids in aligning data warehouses and generating insightful visual outputs, making it ideal for analyzing structured data from sources like Excel and CSV files.

SAS

Sample Customers

Roka Bioscience, Statistics Centre _ Abu Dhabi , Raymond James Financial inc., CaixaBank, Industrial Bank of Korea, Posco, NHS Business Services Authority, RWE Power, LIFE Financial Group,

Data Management, 1-800-FLOWERS.COM, Absa, Aegon, Allianz Global Corporate & SpecialtyAusgrid, Bank of Queensland, Bell, BMC Software, Canada Post, Ceska pojistovna, Chantecler, Chubb Group of Insurance Companies, Credit Guarantee Corporation, Cr_dito y Cauci‹n, Delaware State Police, Deutsche Lufthansa, Directorate of Economics and Statistics, DSM, Enerjisa, ERGO Insurance Group, Florida Department of Corrections, Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare, Livzon Pharmaceutical Group, Los Angeles County, Miami Herald Media Company, Netherlands Enterprise Agency, New Zealand Ministry of Health, Nippon Paper, North Carolina Office of Information Technology Services, Orlando Magic, OTP Group, PITT OHIO, Plano Independent School District, RWE Poland, Spanish Air Force, Stockholm County Council, Telus, The Travel Corporation, Transitions Optical, Triad Analytic Solutions, UNIQA, US Census Bureau, US Department of Housing and Urban Development, USDA National Agricultural Statistics Service, West Midlands Police, XS Inc., Zenith Insurance

Buyer's Guide

Oracle Enterprise Data Quality (EDQ) vs. SAS Data Management

April 2026

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