No more typing reviews! Try our Samantha, our new voice AI agent.

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

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 15, 2026

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 Intelligent Dat...
Ranking in Data Quality
1st
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
214
Ranking in other categories
Data Integration (1st), Business Process Management (BPM) (8th), Business-to-Business Middleware (2nd), API Management (5th), Cloud Data Integration (2nd), Data Governance (3rd), Test Data Management (3rd), Cloud Master Data Management (MDM) (1st), Data Management Platforms (DMP) (2nd), Data Masking (2nd), Metadata Management (2nd), Integration Platform as a Service (iPaaS) (4th), Test Data Management Services (3rd), Product Information Management (PIM) (1st), Data Observability (1st), AI Data Analysis (1st)
Melissa Data Quality
Ranking in Data Quality
8th
Average Rating
8.4
Reviews Sentiment
7.6
Number of Reviews
40
Ranking in other categories
Data Scrubbing Software (4th)
 

Mindshare comparison

As of May 2026, in the Data Quality category, the mindshare of Informatica Intelligent Data Management Cloud (IDMC) is 9.6%, down from 19.3% compared to the previous year. The mindshare of Melissa Data Quality is 4.3%, up from 2.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Quality Mindshare Distribution
ProductMindshare (%)
Informatica Intelligent Data Management Cloud (IDMC)9.6%
Melissa Data Quality4.3%
Other86.1%
Data Quality
 

Featured Reviews

Divya-Raj - PeerSpot reviewer
Sr. Consultant cum Assistant Manager & Offshore Lead at Deloitte
Handles large data volumes effectively and offers competitive pricing
There is a lot of improvement required, as we still face some cache issues most of the time, which is a challenge that we expect to see resolved in the future. Additionally, there is some limitation when we are working with a tool, especially regarding In and Out parameters, and I feel that this aspect should be improved going ahead. We face issues with the API side, as Cloud Application Integration cannot handle large volumes; according to the API page, there is a limitation of 500 records or 500 MB. The AI integrated into the Informatica Intelligent Cloud Services solution is called Application Integration, where we still face challenges when dealing with huge volumes, as previously explained.
GM
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.

Quotes from Members

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

Pros

"The interface is really good."
"The serverless capability and the packaging application of the solution are valuable."
"The overall quality of the addresses associated with other master data entities (Customer, Supplier, physical locations, etc.) has improved significantly."
"The valuable feature is that we can automate the integration part...It is a stable solution."
"Its data cleansing capabilities are very valuable. The match and merge and the audit trail functionalities are very good."
"Informatica provides a comprehensive solution for on-the-fly or real-time data masking."
"This flexibility is what I really love about Informatica."
"The dictionary, the search, and the ratings are without a doubt the most beneficial components of this solution."
"Ability to keep our data set clean and usable for our community searches."
"Decreases chances of incorrect shipping addresses and, thus, returned packages."
"By validating and parsing the addresses our customers submit to us, we have reduced the number of addressing errors encountered during our processing."
"​We are able to more accurately identify valid, and better formatted, data which improves the data we store in our database.​"
"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."
"Contact Verify is very simple to use and performs very fast."
"​Allows us to identify cell phones before dialing, and giving us data about callers."
"We use their GeoPoints to get the most precise, rooftop level geocoding."
 

Cons

"IEDC can improve the comparison of lineages."
"There may be some types of limitations with the performance."
"Informatica MDM has limitations with connectivity."
"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."
"The GUI needs to be a little more user-friendly and appealing."
"One thing to consider is that while Informatica Intelligent Cloud Services already integrates with AI platforms, we need to use these tools rigorously and check all these aspects, including machine learning and analytics. I feel that more work needs to be done on this aspect."
"It could be improved by including a buffer that saves data when there is a connectivity issue."
"If I want to scan the metadata from the data lineage or the Python code, such areas can get tedious in the tool."
"Pricing model."
"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."
"The SSIS component setup seems a little klunky."
"Tech support at Melissa Data was very quick to wash their hands of an issue and say it's IT policies on my side that are causing the issue. There was no offer to try and find a work-around. Just an overwhelming attitude of "it’s not our problem.""
"There are some companies out there using Google or other sources to check/confirm if addresses are residential. If Melissa is not doing this, that could be an improvement."
"An area for improvement is where an end customer's address is not found in the Melissa Data database, even though it is a valid address."
"An area for improvement is where an end customer's address is not found in the Melissa Data database, even though it is a valid address."
"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."
 

Pricing and Cost Advice

"Informatica MDM's pricing is not cheap but comparable to other vendors."
"The pricing is quite flexible."
"Informatica Axon is expensive."
"The pricing is high compared to other tools on the market."
"The price of Informatica Cloud Data Integration could be reduced."
"Informatica MDM is very expensive. Apart from licensing fees, they have broken down their products into multiple products, and they charge for each and every product. If the data is huge, they charge for the data. At times, we have to use third party services for data cleaning, and they charge for that as well."
"The product is not very pocket-friendly for small and medium-sized businesses, and it is understandable because of the kind of features the tool gives."
"You pay for this solution based on IPUs, Informatica Processing Units. This depends on how much data you process and how much memory you consume from the cloud provider, and you pay as you go."
"Pricing is very reasonable, no licensing required."
"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."
"It's 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."
"This vendor has no equal in pricing for equivalent functionality."
"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."
"They were willing to work with our preferred vendors, though it involved extra steps to get the license."
"Understand how may transactions you will be processing so that you can get the right tier pricing."
report
Use our free recommendation engine to learn which Data Quality solutions are best for your needs.
893,221 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
13%
Manufacturing Company
10%
Retailer
7%
Computer Software Company
7%
Insurance Company
11%
Construction Company
10%
Healthcare Company
8%
Comms Service Provider
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business51
Midsize Enterprise27
Large Enterprise153
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise3
Large Enterprise14
 

Questions from the Community

How does Azure Data Factory compare with Informatica Cloud Data Integration?
Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...
Which Informatica product would you choose - PowerCenter or Cloud Data Integration?
Complex transformations can easily be achieved using PowerCenter, which has all the features and tools to establish a real data governance strategy. Additionally, PowerCenter is able to manage huge...
What are the biggest benefits of using Informatica Cloud Data Integration?
When it comes to cloud data integration, this solution can provide you with multiple benefits, including: Overhead reduction by integrating data on any cloud in various ways Effective integration ...
Ask a question
Earn 20 points
 

Also Known As

ActiveVOS, Active Endpoints, Address Verification, Persistent Data Masking
No data available
 

Overview

 

Sample Customers

The Travel Company, Carbonite
Boeing Co., FedEx, Ford Motor Co, Hewlett Packard, Meade-Johnson, Microsoft, Panasonic, Proctor & Gamble, SAAB Cars USA, Sony, Walt Disney, Weight Watchers, and Intel.
Find out what your peers are saying about Informatica Intelligent Data Management Cloud (IDMC) vs. Melissa Data Quality and other solutions. Updated: April 2026.
893,221 professionals have used our research since 2012.