Melissa Data Quality vs Oracle Data Quality comparison

Melissa and Oracle are both solutions in the Data Quality category. Melissa is ranked #8 with an average rating of 9.0, while Oracle is ranked #14. Melissa holds a 3.1% mindshare in DQ, compared to Oracle’s 1.5% 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

403 Views
134 Comparison Views

95% willing to recommend

Oracle Data Quality

Read 8 Oracle Data Quality reviews

333 Views
75 Comparison Views

88% willing to recommend

Melissa Data Quality

Oracle Data Quality

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Executive SummaryUpdated on Jan 6, 2025

Oracle Data Quality and Melissa Data Quality compete in the data management space. Melissa Data Quality seems to have an upper hand due to its superior capabilities and more appealing cost structure despite Oracle's flexible pricing.

Features: Oracle Data Quality offers advanced data profiling, integrated data cleansing, and entity matching which supports large data volumes efficiently. Melissa Data Quality provides global address verification, comprehensive identity management, and effective data standardization options that enhance service quality.

Room for Improvement: Oracle Data Quality could improve by simplifying its deployment process, enhancing user interface intuitiveness, and offering better integration with third-party solutions. Melissa Data Quality might enhance its feature set by expanding its data cleansing capabilities, offering more robust entity matching algorithms, and improving reporting functions for data quality insights.

Ease of Deployment and Customer Service: Melissa Data Quality employs a cloud-based deployment model for hassle-free implementation and maintenance, paired with quick-resolve customer support. Conversely, Oracle Data Quality offers on-premises as well as cloud deployment but requires more technical expertise during initial setup and use despite its extensive support services.

Pricing and ROI: Oracle Data Quality features a scalable pricing model aimed at larger enterprises, enabling tailored investments. Melissa Data Quality, with competitive pricing, yields better ROI by accommodating essential features that foster consistent data accuracy and management cost-effectiveness.

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

Buyer's Guide

Melissa Data Quality vs. Oracle Data Quality

June 2025

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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

Melissa Data Quality

Ranking in Data Quality

8th

Average Rating

8.4

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Data Scrubbing Software (3rd)

Oracle Data Quality

Ranking in Data Quality

14th

Average Rating

8.4

Reviews Sentiment

7.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2025, in the Data Quality category, the mindshare of Melissa Data Quality is 3.1%, up from 2.7% compared to the previous year. The mindshare of Oracle Data Quality is 1.5%, down from 1.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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.

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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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Quotes from Members

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

Pros

"We ran a standard name, address, and zip code, internal dedupe between the different files we had purchased, and we were able to quickly notify our vendor that they had tens of thousands of duplications that they were not even aware of."

"Through more accurate data, our marketing department has been able to increase delivery and conversion rates through email direct marketing initiatives."

"It cuts down significantly on time in trying to match names to addresses. I can do in a few hours what would otherwise take days to accomplish."

"Be confident that the scalability and load are not going to be an issue with the services. "

"We are able to more accurately identify valid, and better formatted, data which improves the data we store in our database."

"We have only been using this for about two months, but it has sped up our processing significantly. It makes data mining easy and fast. We don't have to spend an entire month gathering correct information on leads. All we need is a list of home addresses, and in minutes we have names and phone numbers to increase our chance of these leads becoming customers."

"Enables us to send out bulk mailings when we need to verify NCOA."

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

More Melissa Data Quality pros

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

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

"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."

Cons

"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."

"Did not work as advertized. Needs better results in address parsing, as described on the website."

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

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

"If I had multiple Excel files open and ran Listware it would crash Excel, charge the credits, and not save the results."

"There are some hitches in setup, especially with the new encoding, but otherwise it’s relatively simple."

"It really hasn't given us a phone number for the owner of the property, and that's one thing I'd really like to be getting. Either a phone number or email."

"More countries should be supported by Melissa."

More Melissa Data Quality 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."

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

"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."

"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

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

"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."

"The price for address validation is similar in all software. However, the price for geocoding decides the actual pricing. If you get their most accurate geocoding (called GeoPoints), then it will add about $10k+ per million requests."

"Buy a lot more credits than you think you’re going to need."

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

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

"Pricing is very reasonable."

"NCOA address verification was a requirement from USPS to send out the mailers. This was the only option that charged per address which was extremely helpful since we are a small non-profit school."

More Melissa Data Quality pricing and cost advice

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

"The vendor needs to revisit their pricing strategy."

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Top Industries

By visitors reading reviews

Insurance Company

15%

Manufacturing Company

12%

Financial Services Firm

11%

Computer Software Company

10%

Financial Services Firm

20%

Manufacturing Company

13%

Computer Software Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Comparisons

Experian Data Quality vs Melissa Data Quality

Compared 34% of the time

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

Compared 29% of the time

Precisely Trillium vs Melissa Data Quality

Compared 20% of the time

Talend Data Quality vs Melissa Data Quality

Compared 17% of the time

More Melissa Data Quality Competitors

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

Compared 100% of the time

More Oracle Data Quality Competitors

Product Reports

Buyer's Guide

Melissa Data Quality

June 2025

Download Melissa Data Quality product report

Buyer's Guide

Data Quality

June 2025

Download Oracle Data Quality product report

Also Known As

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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 delivers a complete, best-of-breed approach to party and product data, resulting in trustworthy master data that integrates with applications to improve business insight.

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 Data Quality

June 2025

Free Report: Melissa Data Quality vs. Oracle Data Quality

Find out what your peers are saying about Melissa Data Quality vs. Oracle Data Quality and other solutions. Updated: June 2025.

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