Melissa Data Quality vs SAS Data Management comparison

Why PeerSpot?

Melissa and SAS are both solutions in the Data Quality category. Melissa is ranked #11, while SAS is ranked #8 with an average rating of 10.0. Melissa holds a 3.9% mindshare in DQ, compared to SAS’s 3.3% mindshare. Additionally, 95% of Melissa users are willing to recommend the solution, compared to 88% of SAS users who would recommend it.

Melissa Data Quality

Read 40 Melissa Data Quality reviews

3,334 Views
2,300 Comparison Views

95% willing to recommend

SAS Data Management

Read 19 SAS Data Management reviews

5,709 Views
1,427 Comparison Views

88% willing to recommend

Melissa Data Quality

SAS Data Management

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 6, 2025

SAS Data Management and Melissa Data Quality compete in the data management and quality sector. Melissa Data Quality is often regarded as superior due to its feature robustness, despite SAS's strong support and pricing.

Features: SAS Data Management provides robust data integration, powerful analytics tools, and strong governance capabilities. Melissa Data Quality focuses on data verification, enrichment, and cleansing, offering advanced data cleansing features.

Room for Improvement: SAS Data Management could enhance user interface intuitiveness, expand cloud compatibility, and streamline complex customization options. Melissa Data Quality could benefit from a wider range of integration options, enhanced real-time processing, and more comprehensive support for international data standards.

Ease of Deployment and Customer Service: SAS Data Management offers comprehensive deployment options with responsive customer support, ensuring a seamless setup process. Melissa Data Quality emphasizes streamlined deployment and efficient customer service, providing ease and quick access to support.

Pricing and ROI: SAS Data Management involves higher initial costs but offers significant ROI through its broad functionalities. Melissa Data Quality generally has a lower entry cost with quick ROI due to its focused data quality improvements, making it cost-effective for many businesses.

To learn more, read our detailed Melissa Data Quality vs. SAS Data Management Report (Updated: September 2026).

Buyer's Guide

Melissa Data Quality vs. SAS Data Management

September 2026

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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

11th

Average Rating

8.4

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Data Scrubbing Software (4th)

SAS Data Management

Ranking in Data Quality

8th

Average Rating

8.6

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Integration (27th), Data Governance (22nd)

Mindshare comparison

As of September 2026, in the Data Quality category, the mindshare of Melissa Data Quality is 3.9%, up from 2.8% compared to the previous year. The mindshare of SAS Data Management is 3.3%, up from 2.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Mindshare Distribution
Product	Mindshare (%)
SAS Data Management	3.3%
Melissa Data Quality	3.9%
Other	92.8%

Data Quality

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.

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Namanjbaraiya Baru

Biostatistician at Lambda Therapeutic Research Ltd.

Data management has ensured compliant clinical trial datasets and supports reliable analysis

SAS Data Management's best feature is first, data reliability because SAS Data Management is a very trusted platform. The other valuable feature is data cleaning and the compliance that SAS Data Management provides. I can connect SAS Data Management with other SAS applications, as I am using SAS Viya, SAS 9.4, and SAS Enterprise Guide. The data query functionality of SAS Data Management is also very useful. Since I am using SAS Data Management, and SAS Data Management is well trusted by all regulatory authorities, the audit trails and security checks are very good. It is very reliable, very time-saving, and the chances of error are minimal.

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

"NCOA processing is now quick and easy. No waiting for the list to come back, no calling, and no worrying if there are enough credits available."

"We mainly communicate with our customers via email, so we primarily use it to find a phone number so we can contact them more efficiently. This allows us to talk to them and resolve their issues much more quickly."

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

"We like having the ability to write our own utilities/software to process our records and store the final output the way we want."

"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."

"Melissa offer a high quality product with great service."

"We now have less errors on catalog address labels."

"We use their GeoPoints to get the most precise, rooftop level geocoding."

More Melissa Data Quality pros

"The interface of the solution is great, you don't need to be an experienced analytics person to work with the product, and even new users can do all sorts of modeling on the data."

"The monitoring capabilities are very good."

"Their software pricing offers the best value for money on the market."

"The technical support is excellent."

"For the warehouse and BI environment, SAS is very quick, very intuitive, very, very powerful, and very valuable."

"There is a huge ROI."

"Previously our business users would have to write complex SQL or issue requests to the technical team in order to get the information needed, and they find it much easier using the Dataflux product to access the information stored in the database for the type of analyses they have to do on a day to day basis."

"I find the solution to be very stable."

More SAS Data Management pros

Cons

"MatchUp seems to be single threaded, and limits the amount of data that can be processed automatically."

"Needs better email append coverage (but every vendor struggles with this)."

"More countries should be supported by Melissa."

"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. There were a couple of updates to Windows this year, the program kept crashing. It happened on two different occasions over a period of a few months. Once we told them what the problem was - even though their tech support is great to work with - it literally took probably about two months to fix the issue where we could actually use the program the way we needed to use it."

"It could always be cheaper."

"One thing I would want to have, when you're doing a property search, you can do it either on the FIPS in the APN number or the address itself. For some entries, I'll have the APN number, and some I'll have the address. Apparently it cannot process something when both the FIPS-APN and the address are on there. I have to sort, once with one and once with the other, which is a little bit of a pain."

"The tool needs to provide resizable forms/windows like all other SSIS windows. Vendor claims its an SSIS limitation however all SSIS components are resizable so that isn't true. This is just an annoyance but needless."

"The billing structure does not seem very accurate. We’ve had issues with miscounted batch records processed"

More Melissa Data Quality cons

"Native SAP integration or more efficient SAP is needed."

"Customer Service: When DataFlux was a standalone company, the customer service deserved an A+ rating. Now that it is a wholly owned subsidiary of SAS, and just one piece of a giant software conglomerate, I would give them a C+."

"The support for SAS in Brazil is not the best one, but the support in Sweden is really good, as they visit the company and work to solve the issues."

"Costly. Not great for non-ETL use cases."

"The pricing of the solution needs to be improved. They need to work to make it more affordable."

"The solution could use better documentation."

"For the amount of features that we use, the licensing is expensive."

"We find we often have to go back and re-train users when there are changes made to the solution because the changes are not intuitive."

More SAS Data Management cons

Pricing and Cost Advice

"Depends on situation. We prefer to have data onsite, but some might prefer web access."

"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."

"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."

"Pricing is very reasonable, no licensing required."

"It's affordable."

"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."

"Generally, the cost is ROI positive, depending on your shipping volume."

More Melissa Data Quality pricing and cost advice

"The tool is a bit expensive."

"The licensing model for vCenter is a perpetual license with yearly payments."

"The solution is expensive."

"While it is even free for personal use on the cloud, it can be expensive for desktop installations and enterprise use."

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Top Industries

By visitors reading reviews

Construction Company

18%

Outsourcing Company

15%

Comms Service Provider

10%

Insurance Company

Outsourcing Company

13%

Financial Services Firm

12%

Comms Service Provider

11%

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	2
Large Enterprise	8

Questions from the Community

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What is your experience regarding pricing and costs for SAS Data Management?

From my experience, SAS Data Management is an expensive tool.

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What needs improvement with SAS Data Management?

SAS Data Management can be improved in terms of the learning curve.

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What is your primary use case for SAS Data Management?

My main use case for using SAS Data Management is data cleaning for my clinical trial data because my data is very large, and I need clean, reliable, and regulatory compliance data. My data comes f...

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Comparisons

Qlik Talend Cloud vs Melissa Data Quality

Compared 14% of the time

Experian Data Quality vs Melissa Data Quality

Compared 12% of the time

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

Compared 6% of the time

BCC Data Quality vs Melissa Data Quality

Compared 5% of the time

Oracle Enterprise Data Quality (EDQ) vs Melissa Data Quality

Compared 3% of the time

More Melissa Data Quality Competitors

Informatica PowerCenter vs SAS Data Management

Compared 8% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs SAS Data Management

Compared 7% of the time

WhereScape RED vs SAS Data Management

Compared 6% of the time

Collibra Platform vs SAS Data Management

Compared 5% of the time

Qlik Talend Cloud vs SAS Data Management

Compared 4% of the time

More SAS Data Management Competitors

Product Reports

Buyer's Guide

Melissa Data Quality

September 2026

Download Melissa Data Quality product report

Buyer's Guide

SAS Data Management

August 2026

Download SAS Data Management product report

Also Known As

No data available

SAS Data Management Platform, Data Management Platform, DataFlux

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

SAS Data Management provides data integration, governance, and robust reporting tools. It connects to diverse data sources, ensuring quality management and enabling data analysis for technical and non-technical users.

SAS Data Management features flexible data flow creation, scheduling, and ETL control. It enhances data integration and metadata management with tools that support data standardization. Users benefit from its importing and exporting capabilities, connecting to multiple sources. It facilitates improved data quality management and offers a flexible language for diverse needs. Data visualization capabilities further support decision-making across industries, automating reports and data warehouses.

What are the key features of SAS Data Management?

User-friendly interface: Intuitive navigation facilitates ease of use.
Easy data flow creation: Streamlines processes for seamless data management.
Scheduling and ETL control: Automates tasks for increased efficiency.
Data integration: Connects datasets from multiple origins.
Metadata management: Ensures consistency and quality of data.
Data governance: Provides tools for managing data compliance.
Robust reporting: Delivers comprehensive insights through detailed reports.

What benefits and ROI should be considered?

Data standardization: Enhances data predictability and usability.
Data quality management: Improves accuracy and integrity.
Flexible language: Adapts to specific user requirements.
Import/export capabilities: Facilitates seamless data transitions.
Support for diverse users: Offers tools suitable for varied skill levels.
Automation features: Saves time by reducing manual processes.

SAS Data Management helps industries like finance integrate diverse data sources for analytics and reporting. It is used for tasks such as financial reporting, credit risk analysis, and data cleansing. Through user-driven automation, it aids in aligning data warehouses and generating insightful visual outputs, making it ideal for analyzing structured data from sources like Excel and CSV files.

SAS

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.

Data Management, 1-800-FLOWERS.COM, Absa, Aegon, Allianz Global Corporate & SpecialtyAusgrid, Bank of Queensland, Bell, BMC Software, Canada Post, Ceska pojistovna, Chantecler, Chubb Group of Insurance Companies, Credit Guarantee Corporation, Cr_dito y Cauci‹n, Delaware State Police, Deutsche Lufthansa, Directorate of Economics and Statistics, DSM, Enerjisa, ERGO Insurance Group, Florida Department of Corrections, Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare, Livzon Pharmaceutical Group, Los Angeles County, Miami Herald Media Company, Netherlands Enterprise Agency, New Zealand Ministry of Health, Nippon Paper, North Carolina Office of Information Technology Services, Orlando Magic, OTP Group, PITT OHIO, Plano Independent School District, RWE Poland, Spanish Air Force, Stockholm County Council, Telus, The Travel Corporation, Transitions Optical, Triad Analytic Solutions, UNIQA, US Census Bureau, US Department of Housing and Urban Development, USDA National Agricultural Statistics Service, West Midlands Police, XS Inc., Zenith Insurance

Buyer's Guide

Melissa Data Quality vs. SAS Data Management

September 2026

Free Report: Melissa Data Quality vs. SAS Data Management

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