Informatica Cloud Data Quality vs Melissa Data Quality comparison

Informatica and Melissa are both solutions in the Data Quality category. Informatica is ranked #3 with an average rating of 7.9, while Melissa is ranked #8 with an average rating of 9.0. Informatica holds a 4.4% mindshare in DQ, compared to Melissa’s 2.9% mindshare. Additionally, 75% of Informatica users are willing to recommend the solution, compared to 95% of Melissa users who would recommend it.

Informatica Cloud Data Quality

Read 17 Informatica Cloud Data Quality reviews

967 Views
967 Comparison Views

75% willing to recommend

Melissa Data Quality

Read 40 Melissa Data Quality reviews

769 Views
423 Comparison Views

95% willing to recommend

Informatica Cloud Data Quality

Melissa Data Quality

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 6, 2025

Melissa Data Quality and Informatica Cloud Data Quality are competing products in the data cleansing and enhancement category. Informatica Cloud Data Quality has the upper hand with its advanced features and comprehensive integration capabilities, which justify its higher price for many users.

Features: Melissa Data Quality is known for data verification, enrichment, and ease of use, ideal for businesses prioritizing data accuracy. It offers a straightforward installation and configuration with components like Contact Verify and MatchUp. Informatica Cloud Data Quality is favored for its advanced integration and automation features, Master Data Management, and a user-friendly interface, making it suitable for enterprises needing scalable solutions.

Room for Improvement: Melissa Data Quality could benefit from enhanced support for more complex data needs, increased flexibility in integration, and advanced rule creation capabilities. Informatica Cloud Data Quality may improve by simplifying its deployment complexity, offering more cost-effective pricing options, and enhancing user training resources for its comprehensive features.

Ease of Deployment and Customer Service: Melissa Data Quality provides a straightforward deployment process with strong customer support, making it convenient for smaller teams. On the other hand, Informatica Cloud Data Quality, while having a complex deployment, is backed by comprehensive customer service, suiting organizations with intricate data requirements.

Pricing and ROI: Melissa Data Quality offers a cost-effective setup, allowing for quick ROI, appealing to businesses looking to minimize initial expenses. Informatica Cloud Data Quality involves a higher upfront cost, viewed as a valuable long-term investment due to its extensive capabilities and scalable solutions.

To learn more, read our detailed Informatica Cloud Data Quality vs. Melissa Data Quality Report (Updated: September 2025).

Buyer's Guide

Informatica Cloud Data Quality vs. Melissa Data Quality

September 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

Informatica Cloud Data Quality

Ranking in Data Quality

3rd

Average Rating

8.0

Reviews Sentiment

5.9

Number of Reviews

Ranking in other categories

No ranking in other categories

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 (4th)

Mindshare comparison

As of September 2025, in the Data Quality category, the mindshare of Informatica Cloud Data Quality is 4.4%, down from 9.1% compared to the previous year. The mindshare of Melissa Data Quality is 2.9%, up from 2.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Market Share Distribution
Product	Market Share (%)
Informatica Cloud Data Quality	4.4%
Melissa Data Quality	2.9%
Other	92.7%

Data Quality

Featured Reviews

Mehmet SukruKaya

Senior Data Analytics Manager at Esan

Improved data quality significantly enhances project efficiency and effectiveness

I am not utilizing the data quality rules management part yet. We had a bad experience before Informatica Cloud Data Quality. We started a data analytics project that took more than three months of wasted time because we couldn't use the data to create the optimization model. If we have an application like Informatica Cloud Data Quality or a data quality platform, we can finish the project in less than three months. This is the on-prem data that we ingested. We don't use any cloud data that we have. For now, we are just using the on-prem data, and we don't have any integration from the data outside of the on-prem. I would recommend it to other users. On a scale of 1-10, I rate this solution a 9.

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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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Quotes from Members

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

Pros

"One of the most valuable features of Informatica Cloud Data Quality is Master Data Management. You can write code to build your logic rules to check the quality."

"Initial setup was fairly easy."

"Informatica Cloud Data Quality was most valuable for our real-time data quality monitoring needs."

"I would definitely recommend Informatica Data Quality to others."

"The profiling features are much better than the on-premise version."

"Stability-wise, I rate the solution a ten out of ten."

"The reusability factors are nice. The Cloud Data Quality is much better. A major flaw in the previous version was integration with the catalog, which is now seamless."

"The most beneficial feature of Informatica Cloud Data Quality is it's cloud-based."

More Informatica Cloud Data Quality pros

"We use their GeoPoints to get the most precise, rooftop level geocoding."

"Standardizing allows me to more effectively check for duplicate/existing records. Verifying increases the value of the data."

"Our customer database is now significantly more accurate and reliable."

"This tool works better for us than using a batch processing system that we do not have enough control over as each record is being processed."

"Address verification ensures our customers get their packages, and we aren’t charged for incomplete address information."

"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."

"By validating and parsing the addresses our customers submit to us, we have reduced the number of addressing errors encountered during our processing."

"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."

More Melissa Data Quality pros

Cons

"Informatica Cloud Data Quality has significant room for improvement. It is not stable, and there are several issues, especially on the cloud side, unlike the on-premise version which was very stable."

"The downside of Informatica Cloud Data Quality is that it requires a developer to write rules and maintain the systems. This creates a significant cost and time burden."

"I definitely will not recommend Informatica Cloud Data Quality because it's very hard to manage the licensing model and the price is very high."

"Informatica Cloud Data Quality could improve by adding more algorithms for matching and mastering. We currently only have five or six. Additionally, the parallelism in data is better in other solutions, such as IBM."

"You cannot import the data discovery rules you create in the solution to the Cloud Data Governance and Catalog (CDGC)."

"Support response time could be better."

"I remember when I used it, there was some limitation in one of the data quality dimensions. I was not able to perform certain tasks on the cloud version, even though I could do them on the data center version."

"If a new solution has the same features and less investment, it would be worth considering."

More Informatica Cloud Data Quality cons

"We are no longer using Melissa Data to clean up our address information as there are free tools that we can use to do the same thing."

"I wish there was a way to do a "test run" and see what a particular format will give you."

"To continually update the database with NAICS codes on businesses."

"The tool needs to provide resizable forms/windows like all other SSIS windows. Vendor claims its an SSIS limitation however all SSIS components are resizable so that isn't true. This is just an annoyance but needless."

"It would be nice if it also had a user interface, as it did in years past."

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

"One thing I would want to have, when you're doing a property search, you can do it either on the FIPS in the APN number or the address itself. For some entries, I'll have the APN number, and some I'll have the address. Apparently it cannot process something when both the FIPS-APN and the address are on there. I have to sort, once with one and once with the other, which is a little bit of a pain."

"Needs more/better search tools are needed. Also, state and local tax data would be nice."

More Melissa Data Quality cons

Pricing and Cost Advice

"Informatica Cloud Data Quality is a costly solution."

"We pay for a yearly subscription."

"The licensing costs attached to the solution are pretty high, but then, with the cloud model, the prices depend on what it provides for the value of money, which I feel was very high."

"It is affordable."

"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."

"Cloud version is very cheap. On-premise version is expensive."

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

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

"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."

"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."

"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."

More Melissa Data Quality pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

11%

Financial Services Firm

11%

Manufacturing Company

11%

Healthcare Company

Manufacturing Company

13%

Insurance Company

13%

Computer Software Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	2
Large Enterprise	9

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

Questions from the Community

What do you like most about Informatica Cloud Data Quality?

The profiling features are much better than the on-premise version.

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What needs improvement with Informatica Cloud Data Quality?

I think it would be very useful to have the same features on the data center version available on the cloud version. I remember when I used it, there was some limitation in one of the data quality ...

See all answers

What is your primary use case for Informatica Cloud Data Quality?

I worked on a project where we had to use these tools to execute the data quality requirements from assessment perspectives to scorecards and similar tasks. I haven't used any other tools, so I can...

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Product Reports

Buyer's Guide

Informatica Cloud Data Quality

September 2025

Download Informatica Cloud Data Quality product report

Buyer's Guide

Melissa Data Quality

August 2025

Download Melissa Data Quality product report

Also Known As

Cloud Data Quality Radar

No data available

Overview

Informatica Cloud Data Quality Radar is a cloud application that quickly identifies, fixes, and monitors data quality problems in your business applications—wherever they are, in the cloud or on-premise.

This easy-to-use, browser-based tool empowers line-of-business managers to take ownership of the data quality process so business can maximize the return on trusted data. Informatica Cloud Data Quality Radar enables you to quickly assess the strengths and weaknesses in your data, to track improvements in the data over time, and to calculate data quality scores for objects and field entities with the flexibility to filter and drill down on specific records for better detection of problems.

Informatica

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

Sample Customers

Freddie Mac, Rabobank

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

Buyer's Guide

Informatica Cloud Data Quality vs. Melissa Data Quality

September 2025

Free Report: Informatica Cloud Data Quality vs. Melissa Data Quality

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