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

Melissa Data Quality vs Precisely Trillium comparison

 

Comparison Buyer's Guide

Executive Summary

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
40
Ranking in other categories
Data Scrubbing Software (4th)
Precisely Trillium
Ranking in Data Quality
14th
Average Rating
8.0
Reviews Sentiment
3.1
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of May 2026, in the Data Quality category, the mindshare of Melissa Data Quality is 4.3%, up from 2.8% compared to the previous year. The mindshare of Precisely Trillium is 1.6%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Quality Mindshare Distribution
ProductMindshare (%)
Melissa Data Quality4.3%
Precisely Trillium1.6%
Other94.1%
Data Quality
 

Featured Reviews

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.
reviewer2775918 - PeerSpot reviewer
Managing Partner at a tech vendor with 501-1,000 employees
Offers solid data quality functions and continues to evolve with opportunities in AI integration
Precisely Trillium's best features include being a very comprehensive solution in terms of data quality. There are different types of data quality tools, and what Infogix had before being bought by Precisely focused more on reconciliation, whereas Precisely Trillium actually focused on correcting errors within the data set itself. This is a different perspective on data quality. Some of the functions and features that Infogix did in their data quality solution are different than the functions and features that Precisely Trillium did. The data profiling feature of Precisely Trillium helps improve data integrity for certain clients by emphasizing the principle of bad data in, bad data out. It's important to have a data quality tool to ensure that your data is good because any calculations and business decisions that are made on this data are only as good as the data itself. It's extremely important to have good quality data, and Precisely Trillium serves to ensure the data quality.

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."
"It saves a huge amount of time. Before using this service, we used a vendor that manually ran our lists through this NCOA list, which might have taken one to three business days to return the file. This was a huge bottleneck in our process, and the data returned was not always accurate. After switching to Melissa Data’s SmartMover, the process has been reduced to between ten minutes and three hours, depending on the amount of records sent."
"Address parsing. Our other software does not have this functionality."
"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."
"Provides simplicity, ease of use, combined with overall accuracy of data."
"Since we switched to Melissa Data web services, we do not need to maintain those servers and/or software, and we get the most up-to-date addresses from USPS."
"We like having the ability to write our own utilities/software to process our records and store the final output the way we want."
"Customer service was excellent, and their technical team provides top support for people wanting to use the technology."
"Precisely Trillium's best features include being a very comprehensive solution in terms of data quality."
 

Cons

"Pricing model."
"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."
"Many issues, sometimes I have to completely log out and start over."
"Need to POC point of entry validation."
"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."
"Address validation and parsing in a few countries have room for improvement."
"MatchUp is a more complex product and I recommend a test area before upgrading to production. Performance can change from version to version."
"Needs to validate more addresses accurately."
"There's always room for improvement with Precisely Trillium. The hottest thing right now is AI, and leveraging artificial intelligence to enhance your data quality is always a plus."
 

Pricing and Cost Advice

"Cloud version is very cheap. On-premise version is expensive."
"​It is affordable."
"Melissa pricing is competitive."
"Be sure to determine how the data is priced (record-based versus credit-based or some hybrid of data and services)."
"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."
"I think it's worth the value for me to run it."
"Generally, the cost is ROI positive, depending on your shipping volume."
Information not available
report
Use our free recommendation engine to learn which Data Quality solutions are best for your needs.
893,244 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Insurance Company
11%
Construction Company
10%
Healthcare Company
8%
Comms Service Provider
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise3
Large Enterprise14
No data available
 

Questions from the Community

Ask a question
Earn 20 points
What is your experience regarding pricing and costs for Precisely Trillium?
I don't remember all the details about the pricing for Precisely Trillium as it was a while ago, but it was competitive. It was a premium product on a larger, higher end pricing tier. Even the Info...
What needs improvement with Precisely Trillium?
There's always room for improvement with Precisely Trillium. The hottest thing right now is AI, and leveraging artificial intelligence to enhance your data quality is always a plus. That's probably...
What is your primary use case for Precisely Trillium?
I'm more familiar with Precisely Trillium because Precisely acquired Data360, which they marketed for data governance. Precisely Trillium is a product that competes against Collibra and Alation. Al...
 

Also Known As

No data available
Syncsort Trillium, Trillium, TSS, Syncsort Trillium Software System
 

Overview

 

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.
Toyota Material Handling Australia, Westpac Pacific Banking, Symphony Health, Wimbledon, OCBC Bank
Find out what your peers are saying about Informatica, Qlik, SAP and others in Data Quality. Updated: May 2026.
893,244 professionals have used our research since 2012.