Melissa Data Quality vs Oracle Enterprise Data Quality (EDQ) comparison

Melissa and Oracle are both solutions in the Data Quality category. Melissa is ranked #5 with an average rating of 9.0, while Oracle is ranked #16. Melissa holds a 3.4% mindshare in DQ, compared to Oracle’s 2.8% mindshare. Additionally, 95% of Melissa users are willing to recommend the solution, compared to 88% of Oracle users who would recommend it.

Melissa Data Quality

Read 40 Melissa Data Quality reviews

1,022 Views
623 Comparison Views

95% willing to recommend

Oracle Enterprise Data Qual...

Read 8 Oracle Enterprise Data Quality (EDQ) reviews

543 Views
538 Comparison Views

88% willing to recommend

Melissa Data Quality

Oracle Enterprise Data Qual...

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 11, 2025

Oracle Enterprise Data Quality and Melissa Data Quality compete in data management. Melissa Data Quality is often seen as having an advantage due to its advanced features and perceived value.

Features: Oracle EDQ includes data profiling, data cleaning, and integration capabilities, which enhance its utility across diverse datasets. Melissa Data Quality provides real-time verification, data enrichment, and seamless multi-platform integration, offering significant feature richness compared to competitors.

Room for Improvement: Oracle EDQ could benefit from more flexible pricing models, improvements in user interface design, and enhanced real-time processing capabilities. Melissa Data Quality can improve by expanding its customer support availability, offering more detailed documentation, and providing greater customization options for advanced users.

Ease of Deployment and Customer Service: Oracle EDQ supports both on-premises and cloud deployments, providing established support, but could enhance its real-time functionality. Melissa Data Quality offers strong cloud deployment flexibility and responsive support, advantageous for diverse operational needs.

Pricing and ROI: Oracle EDQ has higher setup costs but promises substantial ROI through its comprehensive offerings. Melissa Data Quality features a cost-effective setup and compelling ROI due to its rich feature set and competitive pricing.

To learn more, read our detailed Melissa Data Quality vs. Oracle Enterprise Data Quality (EDQ) Report (Updated: December 2025).

Buyer's Guide

Melissa Data Quality vs. Oracle Enterprise Data Quality (EDQ)

December 2025

Download the complete report

Helped 881,082 peers since 2012

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

Melissa Data Quality

Ranking in Data Quality

5th

Average Rating

8.4

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Data Scrubbing Software (5th)

Oracle Enterprise Data Qual...

Ranking in Data Quality

16th

Average Rating

8.4

Reviews Sentiment

7.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Data Quality category, the mindshare of Melissa Data Quality is 3.4%, up from 2.5% compared to the previous year. The mindshare of Oracle Enterprise Data Quality (EDQ) is 2.8%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Market Share Distribution
Product	Market Share (%)
Melissa Data Quality	3.4%
Oracle Enterprise Data Quality (EDQ)	2.8%
Other	93.8%

Data Quality

Featured Reviews

GaryM

Data Architect at World Vision

SSIS MatchUp Component is Amazing

- Scalability is a limitation as it is single threaded. You can bypass this limitation by partitioning your data (say by alphabetic ranges) into multiple dataflows but even within a single dataflow the tool starts to really bog down if you are doing survivorship on a lot of columns. It's just very old technology written that's starting to show its age since it's been fundamentally the same for many years. To stay relavent they will need to replace it with either ADF or SSIS-IR compliant version. - Licensing could be greatly simplified. As soon as a license expires (which is specific to each server) the product stops functioning without prior notice and requires a new license by contacting the vendor. And updating the license is overly complicated. - The tool needs to provide resizable forms/windows like all other SSIS windows. Vendor claims its an SSIS limitation but that isn't true since pretty much all SSIS components are resizable except theirs! This is just an annoyance but needless impact on productivity when developing new data flows. - The tool needs to provide for incremental matching using the MatchUp for SSIS tool (they provide this for other solutions such as standalone tool and MatchUp web service). We had to code our own incremental logic to work around this. - Tool needs ability to sort mapped columns in the GUI when using advanced survivorship (only allowed when not using column-level survivorship). - It should provide an option for a procedural language (such as C# or VB) for survivor-ship expressions rather than relying on SSIS expression language. - It should provide a more sophisticated ability to concatenate groups of data fields into common blocks of data for advanced survivor-ship prioritization (we do most of this in SQL prior to feeding the data to the tool). - It should provide the ability to only do survivor-ship with no matching (matching is currently required when running data through the tool). - Tool should provide a component similar to BDD to enable the ability to split into multiple thread matches based on data partitions for matching and survivor-ship rather than requiring custom coding a parallel capable solution. We broke down customer data by first letter of last name into ranges of last names so we could run parallel data flows. - Documentation needs to be provided that is specific to MatchUp for SSIS. Most of their wiki pages were written for the web service API MatchUp Object rather than the SSIS component. - They need to update their wiki site documentation as much of it is not kept current. Its also very very basic offering very little in terms of guidelines. For example, the tool is single-threaded so getting great performance requires running multiple parallel data flows or BDD in a data flow which you can figure out on your own but many SSIS practitioners aren't familiar with those techniques. - The tool can hang or crash on rare occasions for unknown reason. Restarting the package resolves the problem. I suspect they have something to do with running on VM (vendor doesn't recommend running on VM) but have no evidence to support it. When it crashes it creates dump file with just vague message saying the executable stopped running.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"We use a Melissa API to access the data, so it easy to use, accurate, and fast."

"We are able to send out client mailings with the most accurate addresses possible."

"Decreases chances of incorrect shipping addresses and, thus, returned packages."

"By using Melissa Data, we are able to scrub and verify, then better validate the end customer's address to ensure a more consistent delivery of products."

"Allows us to delete and correct incorrect data to make the searching of our applicant tracking system more consistent and relevant."

"Allows us to identify cell phones before dialing, and giving us data about callers."

"Extremely easy to install and setup."

"We mainly communicate with our customers via email, so we primarily use it to find a phone number so we can contact them more efficiently. This allows us to talk to them and resolve their issues much more quickly."

More Melissa Data Quality pros

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

"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."

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

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

Cons

"Address validation and parsing in a few countries have room for improvement."

"The SSIS component setup seems a little klunky."

"One of the problems that we ran into this year was we probably spent over 40 hours finding and trying to drill down to where specific bugs were in the program, which was a tremendous waste of time for us. There were a couple of updates to Windows this year, the program kept crashing. It happened on two different occasions over a period of a few months. Once we told them what the problem was - even though their tech support is great to work with - it literally took probably about two months to fix the issue where we could actually use the program the way we needed to use it."

"MatchUp seems to be single threaded, and limits the amount of data that can be processed automatically."

"It could always be cheaper."

"More countries should be supported by Melissa."

"Many issues, sometimes I have to completely log out and start over."

"Speed of delivery/ease of use. They advertise a 24-hour, next business day turn time on data annotation, but I’ve found it is usually closer to 72 hours. This is still excellent, just make sure you add in the appropriate fluff to your delivery timelines."

More Melissa Data Quality cons

"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."

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

"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 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."

Pricing and Cost Advice

"You should have a good idea of the size of your data and the amount of cleansing you will be doing, so you will purchase the appropriate size bundle."

"Pricing is very reasonable."

"Be sure to determine how the data is priced (record-based versus credit-based or some hybrid of data and services)."

"Generally, the cost is ROI positive, depending on your shipping volume."

"This vendor has no equal in pricing for equivalent functionality."

"We are concerned that our own pricing is going up every year for Melissa Data products, but we highly recommend the services for people who are routinely sending out mailings."

"Fully understand your volume, both monthly and annually. Speak with a Melissa account manager, they will put together an effective solution to meet your needs."

"Depends on situation. We prefer to have data onsite, but some might prefer web access."

More Melissa Data Quality pricing and cost advice

"The vendor needs to revisit their pricing strategy."

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

See which vendors are best for you

Use our free recommendation engine to learn which Data Quality solutions are best for your needs.

See recommendations

881,082 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Insurance Company

15%

Manufacturing Company

Educational Organization

Computer Software Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	3
Large Enterprise	14

By reviewers
Company Size	Count
Midsize Enterprise	2
Large Enterprise	7

Comparisons

Qlik Talend Cloud vs Melissa Data Quality

Compared 24% of the time

Experian Data Quality vs Melissa Data Quality

Compared 14% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Melissa Data Quality

Compared 10% of the time

Coheris Spad Data Quality vs Melissa Data Quality

Compared 7% of the time

SAP Data Quality Management vs Melissa Data Quality

Compared 3% of the time

More Melissa Data Quality Competitors

Informatica Intelligent Data Management Cloud (IDMC) vs Oracle Enterprise Data Quality (EDQ)

Compared 25% of the time

Trillium Quality vs Oracle Enterprise Data Quality (EDQ)

Compared 16% of the time

SAP Information Steward vs Oracle Enterprise Data Quality (EDQ)

Compared 11% of the time

SAP Data Services vs Oracle Enterprise Data Quality (EDQ)

Compared 9% of the time

More Oracle Enterprise Data Quality (EDQ) Competitors

Product Reports

Buyer's Guide

Melissa Data Quality

January 2026

Download Melissa Data Quality product report

Buyer's Guide

Data Quality

January 2026

Oracle Enterprise Data Quality (EDQ) report

Download Oracle Enterprise Data Quality (EDQ) product report

Also Known As

No data available

Datanomic

Overview

Data Quality Components for SSIS

This suite of data transformations for Microsoft SQL Server Integration Services (SSIS) delivers the full spectrum of data quality including data profiling, data verification, data enrichment and data matching. With an intuitive interface and drag/drop capabilities, this powerful toolkit makes it easy to unify data into a single version of the truth for Master Data Management (MDM) success.

Melissa

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

Sample Customers

Boeing Co., FedEx, Ford Motor Co, Hewlett Packard, Meade-Johnson, Microsoft, Panasonic, Proctor & Gamble, SAAB Cars USA, Sony, Walt Disney, Weight Watchers, and Intel.

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,

Buyer's Guide

Melissa Data Quality vs. Oracle Enterprise Data Quality (EDQ)

December 2025

Free Report: Melissa Data Quality vs. Oracle Enterprise Data Quality (EDQ)

Find out what your peers are saying about Melissa Data Quality vs. Oracle Enterprise Data Quality (EDQ) and other solutions. Updated: December 2025.

DOWNLOAD NOW

881,082 professionals have used our research since 2012.

See our Melissa Data Quality vs. Oracle Enterprise Data Quality (EDQ) report.

See our list of best Data Quality vendors.

We monitor all Data Quality reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.