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Melissa Data Quality vs SQL Power Data Quality comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Melissa Data Quality
Ranking in Data Scrubbing Software
4th
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
40
Ranking in other categories
Data Quality (8th)
SQL Power Data Quality
Ranking in Data Scrubbing Software
6th
Average Rating
8.8
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the Data Scrubbing Software category, the mindshare of Melissa Data Quality is 4.8%, up from 4.7% compared to the previous year. The mindshare of SQL Power Data Quality is 1.0%, 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

"There have been tangible benefits in combating fraudulent transactions. The information from Melissa Data is fed straight into our fraud system. This creates efficiency but also removes the need for manual address checks."
"Through more accurate data, our marketing department has been able to increase delivery and conversion rates through email direct marketing initiatives."
"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."
"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 use their GeoPoints to get the most precise, rooftop level geocoding."
"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."
"Helps our organization provide accurate address information to our customers for direct mailing (household) and other campaigns they want to do."
"Provides quality accurate data that our downstream solutions depend on."
"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

"We would appreciate it if there was a larger database so that we could find information more often. For example, we can search for 10 people and only find the information for three of them, if we are lucky."
"More countries should be supported by Melissa."
"MatchUp is a more complex product and I recommend a test area before upgrading to production. Performance can change from version to version."
"​If I had multiple Excel files open and ran Listware it would crash Excel, charge the credits, and not save the results."
"Address validation and parsing in a few countries have room for improvement."
"I wish there was a way to do a "test run" and see what a particular format will give you."
"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.""
"We have noticed that some of the emails and addresses return with confusing or incorrect codes, but for the most part, it is accurate.​"
"Downtime issues should be improved."
"The normalization factor should be improved so that it is better scaled. It should be more user-friendly."
"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."
 

Pricing and Cost Advice

"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."
"Understand how may transactions you will be processing so that you can get the right tier pricing."
"Be sure to determine how the data is priced (record-based versus credit-based or some hybrid of data and services)."
"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."
"Buy a lot more credits than you think you’re going to need."
"It's affordable."
"Depends on situation. We prefer to have data onsite, but some might prefer web access."
"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."
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Top Industries

By visitors reading reviews
Manufacturing Company
14%
Financial Services Firm
12%
Insurance Company
11%
Computer Software Company
10%
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.
 

Learn More

 

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: December 2024.
824,067 professionals have used our research since 2012.