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 #6 with an average rating of 9.3. Oracle holds a 3.6% mindshare in DQ, compared to SAS’s 3.3% 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

1,368 Views
1,354 Comparison Views

88% willing to recommend

SAS Data Management

Read 19 SAS Data Management reviews

4,330 Views
1,039 Comparison Views

88% willing to recommend

Oracle Enterprise Data Qual...

SAS Data Management

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Executive SummaryUpdated on Jun 3, 2026

Oracle Enterprise Data Quality and SAS Data Management are competing in the data management domain. While Oracle is praised for competitive pricing and effective support, SAS is often chosen for its comprehensive features despite a higher cost.

Features: Oracle excels in data profiling, cleansing, and integration, enabling significant improvements in data quality. SAS is strong in data transformation and analytics, suitable for organizations needing advanced analytical capabilities. SAS also has a solid reputation for data governance and metadata management, supporting intricate data management tasks.

Room for Improvement: Oracle could enhance its advanced analytics capabilities and expand compatibility with more complex data scenarios. SAS, while robust, may benefit from simplifying its deployment process and reducing complexity for user interfaces. Both solutions could work on improving scalability and integration with newer technologies to stay competitive.

Ease of Deployment and Customer Service: Oracle is known for its straightforward deployment and solid customer support, offering ease for organizations integrating with existing systems. SAS, though more complex in deployment due to its extensive features, provides reliable customer service, catering to businesses seeking extensive customization.

Pricing and ROI: Oracle typically has a lower setup cost, appealing to budget-conscious organizations seeking a quicker ROI. In contrast, SAS, with a higher initial investment, is often viewed as delivering better long-term ROI due to its comprehensive features and scalability, attracting those prioritizing functionality over cost.

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

Buyer's Guide

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

June 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

6th

Average Rating

8.6

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Integration (26th), Data Governance (24th)

Mindshare comparison

As of June 2026, in the Data Quality category, the mindshare of Oracle Enterprise Data Quality (EDQ) is 3.6%, up from 1.5% compared to the previous year. The mindshare of SAS Data Management is 3.3%, up from 3.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Mindshare Distribution
Product	Mindshare (%)
SAS Data Management	3.3%
Oracle Enterprise Data Quality (EDQ)	3.6%
Other	93.1%

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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Namanjbaraiya Baru

Biostatistician at Lambda Therapeutic Research Ltd.

Data management has ensured compliant clinical trial datasets and supports reliable analysis

SAS Data Management's best feature is first, data reliability because SAS Data Management is a very trusted platform. The other valuable feature is data cleaning and the compliance that SAS Data Management provides. I can connect SAS Data Management with other SAS applications, as I am using SAS Viya, SAS 9.4, and SAS Enterprise Guide. The data query functionality of SAS Data Management is also very useful. Since I am using SAS Data Management, and SAS Data Management is well trusted by all regulatory authorities, the audit trails and security checks are very good. It is very reliable, very time-saving, and the chances of error are minimal.

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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

"Once it is set up, it is easy to use and maintain."

"Compared to the competition, Oracle Data Quality is a very flexible and advanced product."

"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."

"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 technical support is very good; we had a good experience with the support."

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

"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."

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

More Oracle Enterprise Data Quality (EDQ) pros

"The platform's robust data governance capabilities ensure strong compliance with regulatory standards."

"National Quality Knoledge Base Matching features QPI (Quality Performance Indicator) analysis approach"

"The solution is very stable. We haven't faced any issues with glitches or bugs. We haven't had any crashes."

"I think the product is very stable, I think it's a marvelous product, it is very widely used by many people and I never hear people complain."

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

"If you compare it to SQL, the memory and development times are very quick."

"I find the solution to be very stable."

"The monitoring capabilities are very good."

More SAS Data Management pros

Cons

"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."

"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."

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

"There are some challenges with respect to standardization, matching, segregation, and merging."

"We have experienced some system crashes i.e. when tying to run Data Profiling Processes for very large data sets."

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

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

More Oracle Enterprise Data Quality (EDQ) cons

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

"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."

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

"With SAS Data Management, you have to purchase an external driver, configure all of the tables for all of the data that you will extract from Salesforce. It's not a straightforward process."

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

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

"Costly. Not great for non-ETL use cases."

"We implemented it a while ago, and we are trying to improve the data delivery performance. We are looking into how to get faster and automated reporting. We would need better designs and workflows."

More SAS Data Management cons

Pricing and Cost Advice

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

"The vendor needs to revisit their pricing strategy."

"The solution is expensive."

"The tool is a bit expensive."

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

"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

Construction Company

15%

Manufacturing Company

12%

Comms Service Provider

10%

Financial Services Firm

18%

Government

Comms Service Provider

Construction 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	8
Midsize Enterprise	2
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?

SAS Data Management can be improved in terms of the learning curve.

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What is your primary use case for SAS Data Management?

My main use case for using SAS Data Management is data cleaning for my clinical trial data because my data is very large, and I need clean, reliable, and regulatory compliance data. My data comes f...

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Compared 4% of the time

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Compared 9% of the time

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Compared 8% of the time

WhereScape RED vs SAS Data Management

Compared 8% of the time

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Compared 5% of the time

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Product Reports

Buyer's Guide

Data Quality

June 2026

Oracle Enterprise Data Quality (EDQ) report

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Buyer's Guide

SAS Data Management

May 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

June 2026

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