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Melissa Data Quality vs SQL Power Data Quality 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 Scrubbing Software
4th
Average Rating
8.4
Reviews Sentiment
7.6
Number of Reviews
40
Ranking in other categories
Data Quality (8th)
SQL Power Data Quality
Ranking in Data Scrubbing Software
7th
Average Rating
8.8
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Data Scrubbing Software category, the mindshare of Melissa Data Quality is 6.8%, up from 4.5% compared to the previous year. The mindshare of SQL Power Data Quality is 0.7%, down from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Scrubbing Software
 

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.
Ayinde Hammed - PeerSpot reviewer
Helps you cleanse your data, validate and correct addresses, and identify and remove duplicates
I use SQL most of the time to clean up the data, transform it, and then push it into the database. We extract data from apps and from websites using a lot of applications. We do that using Python. We used Outlook before to pull data from, the source and then log into Snowflake.  The Nomo, select…

Quotes from Members

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

Pros

"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."
"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."
"Provides simplicity, ease of use, combined with overall accuracy of data."
"​We are able to more accurately identify valid, and better formatted, data which improves the data we store in our database.​"
"Enables us to send out bulk mailings when we need to verify NCOA."
"​Initial setup was fairly straightforward. The documentation was very good in terms of how to integrate and consume the service(s) that we use. It did not take an abundance of time to set up things on our side to use the service."
"By validating and parsing the addresses our customers submit to us, we have reduced the number of addressing errors encountered during our processing."
"​Ability to keep our data set clean and usable for our community searches.​"
"The Nomo, select form, and so on are the most valuable features."
"The solution is able to integrate with many systems and other products."
"It is a scalable solution. We have over 1,000 SQL Power Data Quality users in our organization."
 

Cons

"Pricing is based on tiers, with each tier capped at a specified number of records processed. Once you go over the cap at one tier, you are automatically bumped to the next tier. However, they seem to count failed batch processes so it’s good to keep track of the number of records sent. They’ll fix the count when notified, but their system fails to detect actual successful processes versus failed processes."
"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."
"Needs to provide more phone numbers, even cell numbers (scrubbed numbers)."
"Needs better email append coverage (but every vendor struggles with this)."
"It would be helpful if a list of the codes and explanations could be included."
"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."
"MatchUp seems to be single threaded, and limits the amount of data that can be processed automatically."
"The only area of improvement that we've come across within the solution was the portfolio roadmap creation. There's a bit of limitation there, but otherwise, the tool itself is very good."
"Downtime issues should be improved."
"The normalization factor should be improved so that it is better scaled. It should be more user-friendly."
 

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."
"Depends on situation. We prefer to have data onsite, but some might prefer web access."
"It's affordable."
"​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.​"
"Cloud version is very cheap. On-premise version is expensive."
"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."
"I think it's worth the value for me to run it."
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Top Industries

By visitors reading reviews
Insurance Company
15%
Manufacturing Company
12%
Financial Services Firm
11%
Computer Software Company
9%
No data available
 

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 SQL Power Data Quality?
The Nomo, select form, and so on are the most valuable features.
What advice do you have for others considering SQL Power Data Quality?
It is a very good solution and I recommend it. I rate the overall solution an eight out of ten.
 

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.
TimeWarner, Champion Technologies Tiscali, Oneil, Broadspire,youbet.com, Pepsi Co, Citco, John Lewes
Find out what your peers are saying about Melissa Data Quality vs. SQL Power Data Quality and other solutions. Updated: April 2025.
848,716 professionals have used our research since 2012.