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Melissa Data Quality vs Syniti Data Quality comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jan 6, 2025

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)
Syniti Data Quality
Ranking in Data Quality
6th
Average Rating
8.6
Reviews Sentiment
7.1
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of August 2025, in the Data Quality category, the mindshare of Melissa Data Quality is 3.2%, up from 2.4% compared to the previous year. The mindshare of Syniti Data Quality is 9.7%, up from 8.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Quality
 

Featured Reviews

GM
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.
RA
Offers predefined rules and easy to migrate data with minimum customisation of rules
The customization of the data needs improvement. We need to build basic SQL queries rather than being able to do it within the tool. We need to be able to analyze the SQL queries and then rerun them based on customer usage. We should be able to tune the existing process by using simple SQL queries based on the customer's requirements. In future releases, I would like to see more features around Preload and postload reports. From the end-user point of view, it is not very feasible to read. I need to know how the data has been migrated. I need to know whether the complete data has been migrated, only the required data has been migrated, and how it was migrated. So the postload reports will give validation between the source data and the target source. It would give exact picture of the data migration.

Quotes from Members

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

Pros

"Decreases chances of incorrect shipping addresses and, thus, returned packages."
"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."
"Extremely easy to install and setup."
"Services for all manner of data-driven organizations, no matter their size or budget."
"Provides quality accurate data that our downstream solutions depend on."
"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."
"Ability to validate addresses, make corrections to address."
"Provides simplicity, ease of use, combined with overall accuracy of data."
"With Syniti Data Quality, you can integrate SAP and directly fix errors from Syniti Data Quality instead of logging into SAP and then fixing them."
"The major benefits of Syniti Data Quality stem from the productivity and flexibility it offers to users."
"The customer service and support is good."
"Syniti has built-in 80% of the solution, and we only need to customize 20 to 25% of the features. It is easy to run and pre-load reports."
 

Cons

"Needs more/better search tools are needed. Also, state and local tax data would be nice."
"MatchUp is a more complex product and I recommend a test area before upgrading to production. Performance can change from version to version."
"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."
"Address validation and parsing in a few countries have room for improvement."
"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."
"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.""
"Needs better email append coverage (but every vendor struggles with this)."
"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 customization of the data needs improvement. We need to build basic SQL queries rather than being able to do it within the tool. We need to be able to analyze the SQL queries and then rerun them based on customer usage."
"The loading mechanisms and administration processes, particularly in setting up connections and deploying the system, need improvement."
"It would be good if Syniti Data Quality could integrate more AI in the future."
"In Syniti Data Quality, data extraction is an area with certain shortcomings where improvements are required."
 

Pricing and Cost Advice

"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."
"Trial subscriptions (via cloud) are very cheap and easy to use. It’s a great way to test Listware to see if you want to go deeper with integration."
"Understand how may transactions you will be processing so that you can get the right tier pricing."
"Pricing is very reasonable."
"​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."
"I think it's worth the value for me to run it."
"​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."
"The solution is expensive."
"I would rate the pricing a six out of ten, where one is cheap, and ten is expensive."
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Top Industries

By visitors reading reviews
Insurance Company
14%
Manufacturing Company
11%
Financial Services Firm
8%
Computer Software Company
8%
Manufacturing Company
18%
Computer Software Company
10%
Financial Services Firm
9%
Retailer
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

Ask a question
Earn 20 points
What do you like most about Syniti Data Quality?
The customer service and support is good.
What needs improvement with Syniti Data Quality?
The loading mechanisms and administration processes, particularly in setting up connections and deploying the system, need improvement.
What is your primary use case for Syniti Data Quality?
Syniti Data Quality's use case is similar to what we do in SAP Information Steward. You can create a data quality report and your business Steward, which is more into cleansing the data. They will ...
 

Also Known As

No data available
Syniti DQ
 

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.
Kraft Foods, Puget Sound Energy
Find out what your peers are saying about Melissa Data Quality vs. Syniti Data Quality and other solutions. Updated: July 2025.
865,484 professionals have used our research since 2012.