Melissa Data Quality vs SQL Power Data Quality comparison

Melissa and SQL Power are both solutions in the Data Scrubbing Software category. Melissa is ranked #4, while SQL Power is ranked #9. Melissa holds a 9.2% mindshare in DSS, compared to SQL Power’s 3.1% mindshare. Additionally, 95% of Melissa users are willing to recommend the solution, compared to 100% of SQL Power users who would recommend it.

Melissa Data Quality

Read 40 Melissa Data Quality reviews

2,316 Views
1,436 Comparison Views

95% willing to recommend

SQL Power Data Quality

Read 4 SQL Power Data Quality reviews

517 Views
481 Comparison Views

100% willing to recommend

Melissa Data Quality

SQL Power Data Quality

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between Melissa Data Quality and SQL Power Data Quality based on real PeerSpot user reviews.

Find out in this report how the two Data Scrubbing Software solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Melissa Data Quality vs. SQL Power Data Quality Report (Updated: April 2026).

Buyer's Guide

Melissa Data Quality vs. SQL Power Data Quality

April 2026

Download the complete report

Helped 893,221 peers since 2012

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

Ranking in other categories

Data Quality (8th)

SQL Power Data Quality

Ranking in Data Scrubbing Software

9th

Average Rating

8.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2026, in the Data Scrubbing Software category, the mindshare of Melissa Data Quality is 9.2%, up from 7.3% compared to the previous year. The mindshare of SQL Power Data Quality is 3.1%, up from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Scrubbing Software Mindshare Distribution
Product	Mindshare (%)
Melissa Data Quality	9.2%
SQL Power Data Quality	3.1%
Other	87.7%

Data Scrubbing Software

Featured Reviews

GaryM

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.

Read full review

reviewer2091297

Fraud Strategist(Actimize/SAS) at a financial services firm with 10,001+ employees

Is easy to deploy and is stable and scalable

I like the load balancing feature The normalization factor should be improved so that it is better scaled. It should be more user-friendly. It should be easier to export reports. I've been using SQL Power Data Quality for 10 years. SQL Power Data Quality is stable. It is a scalable solution.…

Read full review

Quotes from Members

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

Pros

"It gives me an assessed value of the property in question; my partner and I are property investors, and it's good to get an assessed value to cull out properties that we're not interested in."

"Address parsing. Our other software does not have this functionality."

"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."

"Getting the most up to date address for our members, we like to keep in touch with membership a few times a year so we want to maintain up to date addresses to be sure they receive any information that we mail to them."

"Decreases chances of incorrect shipping addresses and, thus, returned packages."

"Be confident that the scalability and load are not going to be an issue with the services. "

"Address verification ensures our customers get their packages, and we aren’t charged for incomplete address information."

More Melissa Data Quality pros

"The solution is able to integrate with many systems and other products."

"SQL Power Data Quality is truly a proven product that makes the work of cleaning data in both source and target data easy yet efficient."

"It is a scalable solution. We have over 1,000 SQL Power Data Quality users in our organization."

"The solution is very easy to use and it's quite flexible."

"The Nomo, select form, and so on are the most valuable features."

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."

"It will mix up family members at times, so we will change addresses at times that shouldn’t be changed."

"If I had multiple Excel files open and ran Listware it would crash Excel, charge the credits, and not save the results."

"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."

"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."

"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."

"There are some hitches in setup, especially with the new encoding, but otherwise it’s relatively simple."

"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."

More Melissa Data Quality cons

"Integrating SQL Power with the system is challenging especially in places where no tutorials found and new versions continue to be released with varying requirements."

"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."

"The normalization factor should be improved so that it is better scaled. It should be more user-friendly."

"Downtime issues should be improved."

Pricing and Cost Advice

"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)."

"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."

"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."

"Pricing is very reasonable."

"It is affordable."

"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."

More Melissa Data Quality pricing and cost advice

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which Data Scrubbing Software solutions are best for your needs.

See recommendations

893,221 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Insurance Company

11%

Construction Company

10%

Healthcare Company

Comms Service Provider

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	3
Large Enterprise	14

No data available

Comparisons

Qlik Talend Cloud vs Melissa Data Quality

Compared 16% of the time

Experian Data Quality vs Melissa Data Quality

Compared 14% of the time

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

Compared 7% of the time

BCC Data Quality vs Melissa Data Quality

Compared 5% of the time

Anchor Software Data Quality vs Melissa Data Quality

Compared 4% of the time

More Melissa Data Quality Competitors

Ataccama ONE Platform vs SQL Power Data Quality

Compared 27% of the time

BCC Data Quality vs SQL Power Data Quality

Compared 22% of the time

Omikron Data Quality vs SQL Power Data Quality

Compared 19% of the time

Clavis Technology Data Quality vs SQL Power Data Quality

Compared 11% of the time

More SQL Power Data Quality Competitors

Product Reports

Buyer's Guide

Melissa Data Quality

April 2026

Download Melissa Data Quality product report

Buyer's Guide

Data Scrubbing Software

April 2026

Download SQL Power Data Quality product report

Overview

Melissa Data Quality delivers robust features for address validation and data standardization with seamless SSIS integration, making it a cost-effective choice for managing large datasets on-premises or in the cloud.

Emphasizing efficiency, Melissa Data Quality supports organizations in refining data accuracy through features like address validation, parsing, and cleansing. Its integration with SSIS simplifies setup and enhances operational ease, while solutions like Personator provide comprehensive contact detail acquisition. The system's match process ensures accurate deduplication, catering to extensive datasets with flexibility from on-premises to cloud deployments. Despite its strengths, there could be improvements in handling unknown addresses, phone number standardization, and international support, alongside refining processing speed and streamlining license management.

What features does Melissa Data Quality offer?

Address Validation: Ensures accurate address data
Data Parsing: Breaks data into structured components
Data Cleansing: Removes inaccuracies and standardizes
SSIS Integration: Simplifies installation and operation
Personator: Extracts names, phone numbers, and emails

What benefits can be expected from Melissa Data Quality?

Cost Efficiency: Provides value for business data needs
Accuracy: Enhances precision through deduplication
Flexibility: Adapts from on-premises to cloud solutions
Contact Verification: Streamlines onboarding processes

Organizations employ Melissa Data Quality for accurate address validation, customer data accuracy, and geocoding. It's instrumental in duplicate identification, data cleansing, and maintaining address accuracy via USPS NCOA. During customer onboarding, it verifies details while integrating seamlessly with existing data systems, using Listware and Personator for precise address entry, geocoding, and status updates, helping classify businesses by industry.

Melissa

SQL Power Data Quality enhances data management by providing an efficient platform designed for robust data quality processes. It's a comprehensive tool essential for data-driven organizations seeking accuracy and integrity in their data solutions.

Organizations leveraging SQL Power Data Quality benefit from advanced data profiling and cleansing capabilities, equipped with intuitive features that enable users to address complex data quality challenges effectively. It supports a seamless integration and automation process, allowing users to concentrate their efforts on strategic planning and decision-making by reducing manual data handling.

What are the key features of SQL Power Data Quality?

Data Profiling: Identify anomalies and patterns effortlessly for improved data accuracy.
Data Cleansing: Automatically correct and standardize data entries to ensure consistency.
Real-time Monitoring: Detect and address data quality issues promptly as they arise.
Automation Capabilities: Streamline repetitive data tasks to enhance productivity and efficiency.
Scalability: Easily adapt to expanding data sets and growing organizational data needs.

What benefits should be considered when reviewing SQL Power Data Quality?

Cost Efficiency: Reduces operational costs by automating data quality processes.
Improved Decision-Making: Enhances the reliability of business analytics through cleaner data.
Time Savings: Minimizes time spent on manual data corrections.
Data Integrity: Ensures high-quality, accurate datasets that support strategic goals.
Enhanced Compliance: Facilitates adherence to regulatory standards with comprehensive auditing features.

Industries like finance, healthcare, and telecommunications effectively integrate SQL Power Data Quality to meet stringent data standards. Finance institutions utilize its capabilities for auditing and risk management, while healthcare benefits from improved patient record accuracy. In telecommunications, the focus is on optimizing customer data for better service delivery. SQL Power Data Quality serves crucial industry-specific needs by offering versatile solutions tailored to unique challenges.

SQL Power

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

Buyer's Guide

Melissa Data Quality vs. SQL Power Data Quality

April 2026

Free Report: Melissa Data Quality vs. SQL Power Data Quality

Find out what your peers are saying about Melissa Data Quality vs. SQL Power Data Quality and other solutions. Updated: April 2026.

DOWNLOAD NOW

893,221 professionals have used our research since 2012.

See our Melissa Data Quality vs. SQL Power Data Quality report.

See our list of best Data Scrubbing Software vendors.

We monitor all Data Scrubbing Software reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.