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 7.8, while Melissa is ranked #8 with an average rating of 9.0. Informatica holds a 7.6% mindshare in DQ, compared to Melissa’s 3.3% mindshare. Additionally, 78% of Informatica users are willing to recommend the solution, compared to 95% of Melissa users who would recommend it.

Informatica Cloud Data Quality

Read 15 Informatica Cloud Data Quality reviews

977 Views
967 Comparison Views

78% willing to recommend

Melissa Data Quality

Read 40 Melissa Data Quality reviews

656 Views
400 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: July 2025).

Buyer's Guide

Informatica Cloud Data Quality vs. Melissa Data Quality

July 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

6.8

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 (3rd)

Mindshare comparison

As of July 2025, in the Data Quality category, the mindshare of Informatica Cloud Data Quality is 7.6%, down from 9.9% compared to the previous year. The mindshare of Melissa Data Quality is 3.3%, up from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality

Featured Reviews

TaherDungrawala

Data & Analytics Practice at Tech Mahindra Limited

Has good reusability and CDI features

I use Collibra Data Quality. I switched to Informatica because Collibra cannot integrate. Colibra Cloud Data Quality is a very basic tool. There is no integration capability. If you have problem records, there is no fix to remediate them using the same tool. With Informatica, you can integrate with CDI and then create a remediation plan.

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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

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

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

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

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

"An advantage is its seamless integration with other Informatica capabilities, making data quality a deeply embedded part of the solution."

"The out-of-the-box features and standard profiling options provide quick visibility of the data within minutes, which facilitates discussions with the business."

"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 user-friendliness and performance of Informatica is quite impressive."

More Informatica Cloud Data Quality pros

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

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

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

"The high value in this tool is its relatively low cost, ease of use, tight integration with SSIS, superior performance (compared to competitors), and attribute-level advanced survivor-ship logic."

"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."

"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."

"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."

"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

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 integration with older technology and cloud quality needs improvement."

"The high price of the product is an area of concern where improvements are required."

"If given the opportunity, I would like to address these concerns, particularly with regard to enhancing the end-user experience."

"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."

"I used to use this tool more but recently we have been using another tool that we feel is better because it handles spatial data. Informatica Cloud Data Quality could improve by adding the ability to handle spatial data."

"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."

"Support response time could be better."

More Informatica Cloud Data Quality cons

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

"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."

"The custom software solution we still use in-house makes Excel a lot slower than usual."

"It would be helpful if a list of the codes and explanations could be included."

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

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

"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."

"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."

More Melissa Data Quality cons

Pricing and Cost Advice

"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."

"Informatica Cloud Data Quality is a costly solution."

"Understand how may transactions you will be processing so that you can get the right tier pricing."

"The only complaint that I have towards it is they sell licenses based on a range of usage, and I feel those ranges are too large."

"It's affordable."

"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."

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

"It is affordable."

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

More Melissa Data Quality pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Manufacturing Company

12%

Computer Software Company

10%

Healthcare Company

Insurance Company

15%

Financial Services Firm

11%

Computer Software Company

11%

Manufacturing Company

11%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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?

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, especially since we followed...

See all answers

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

The main use case we had with Informatica Cloud Data Quality was real-time data quality monitoring. When customer information was entered into a tool in real time, we were able to run data quality ...

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Comparisons

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IBM Infosphere Information Analyzer vs Informatica Cloud Data Quality

Compared 12% of the time

SAP Data Quality Management vs Informatica Cloud Data Quality

Compared 9% of the time

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Experian Data Quality vs Melissa Data Quality

Compared 38% of the time

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

Compared 37% of the time

Precisely Trillium vs Melissa Data Quality

Compared 25% of the time

More Melissa Data Quality Competitors

Product Reports

Buyer's Guide

Informatica Cloud Data Quality

July 2025

Download Informatica Cloud Data Quality product report

Buyer's Guide

Melissa Data Quality

June 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

July 2025

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

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