Melissa Data Quality vs SAS Data Management comparison

Melissa and SAS are both solutions in the Data Quality category. Melissa is ranked #8 with an average rating of 9.0, while SAS is ranked #11 with an average rating of 8.0. Melissa holds a 3.2% mindshare in DQ, compared to SAS’s 3.0% mindshare. Additionally, 95% of Melissa users are willing to recommend the solution, compared to 87% of SAS users who would recommend it.

Melissa Data Quality

Read 40 Melissa Data Quality reviews

653 Views
372 Comparison Views

95% willing to recommend

SAS Data Management

Read 17 SAS Data Management reviews

1,524 Views
213 Comparison Views

87% 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: July 2025).

Buyer's Guide

Melissa Data Quality vs. SAS Data Management

July 2025

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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

8th

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

11th

Average Rating

8.4

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (30th), Data Governance (25th)

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 SAS Data Management is 3.0%, up from 2.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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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Vivek Trivedi

Head finance transformation at Ahli United Bank Ksc

Has robust data governance capabilities and efficient financial alerting features

The platform's robust data governance capabilities ensure strong compliance with regulatory standards. Our dedicated data governance team is responsible for setting policies and monitoring data access and usage. Additionally, we have a Data Protection Officer (DPO) and an independent InfoSec team that work together to enforce these policies. This multi-layered approach ensures that data governance is effectively managed and compliant with all necessary regulations. It is integrated with SQL and Power BI servers for executive management dashboard reporting. This integration supports comprehensive data analysis and reporting needs, which was a key factor in our decision. The value of using vCenter lies in its comprehensive data infrastructure management. It significantly enhances our reporting, day-to-day decision-making, and analytics capabilities. Integrating SaaS with vCenter adds value by streamlining key functions and improving overall organizational efficiency. The solution can be considered expensive, particularly due to infrastructure, maintenance, and ownership costs. The long-term savings and efficiencies gained from using vCenter can justify the investment for large organizations. However, the high costs might not be justifiable for smaller organizations with less data and fewer resources. Conducting a thorough cost-benefit analysis based on the organization's specific needs and scale is recommended. I rate it an eight.

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Quotes from Members

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

Pros

"By validating and parsing the addresses our customers submit to us, we have reduced the number of addressing errors encountered during our processing."

"Gives us the ability to offer an additional resource that other companies do not."

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

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

"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."

"The customers' addresses are now complete, correct and follow one consistent format."

"Ability to keep our data set clean and usable for our community searches."

"We are able to send out client mailings with the most accurate addresses possible."

More Melissa Data Quality pros

"The technical support is excellent."

"The solution is very stable. We haven't faced any issues with glitches or bugs. We haven't had any crashes."

"Its robustness is valuable. It is a full-fledged suite. We have a data warehouse model, and there are also a lot of data quality management tools. The repository and all other tools are there. So, it is a full package in terms of reporting tools."

"The tool is reliable, quick, and powerful."

"If you compare it to SQL, the memory and development times are very quick."

"The platform's robust data governance capabilities ensure strong compliance with regulatory standards."

"I find the solution to be very stable."

"In terms of which features I have found most valuable, I would say the importing and exporting features. Additionally, the data sorting, categorizing and summarizing features, especially how it can summarize based on categories. These are the key features."

More SAS Data Management pros

Cons

"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."

"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."

"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."

"I wish there was a way to do a "test run" and see what a particular format will give you."

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

"Needs to provide more phone numbers, even cell numbers (scrubbed numbers)."

"It would be helpful if a list of the codes and explanations could be included."

"Did not work as advertized. Needs better results in address parsing, as described on the website."

More Melissa Data Quality cons

"The solution could use better documentation."

"We implemented it a while ago, and we are trying to improve the data delivery performance. We are looking into how to get faster and automated reporting. We would need better designs and workflows."

"There is significant room for improvement, especially with regard to using a hybrid approach that involves both CAS and persistent storage."

"One problem is accessing the data using a solution other than SAS. The SAS data, which we create in the SAS, cannot be accessed by other tools. We can't open those data in other applications. So we need to have that application in place."

"The solution is quite expensive and hard to install/configure."

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

"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."

"Integration and cloud migration are areas where we see potential for improvement."

More SAS Data Management cons

Pricing and Cost Advice

"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."

"Be sure to determine how the data is priced (record-based versus credit-based or some hybrid of data and services)."

"I think it's worth the value for me to run it."

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

"It's affordable."

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

"This vendor has no equal in pricing for equivalent functionality."

"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

"The solution is expensive."

"The tool is a bit expensive."

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

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

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Top Industries

By visitors reading reviews

Insurance Company

14%

Manufacturing Company

11%

Financial Services Firm

Computer Software Company

Financial Services Firm

28%

Computer Software Company

Insurance Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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?

There is significant room for improvement, especially with regard to using a hybrid approach that involves both CAS and persistent storage.

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What is your primary use case for SAS Data Management?

Some clients and I are using SAS Data Management for data cleansing, data warehousing, and IoT-related activities such as streaming data.

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Comparisons

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Compared 19% of the time

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Compared 17% of the time

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

Compared 17% of the time

Anchor Software Data Quality vs Melissa Data Quality

Compared 17% of the time

Precisely Trillium vs Melissa Data Quality

Compared 11% of the time

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

Compared 19% of the time

Palantir Foundry vs SAS Data Management

Compared 13% of the time

Collibra Governance vs SAS Data Management

Compared 13% of the time

Protegrity’s Data Protection Platform vs SAS Data Management

Compared 9% of the time

Informatica PowerCenter vs SAS Data Management

Compared 8% of the time

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Product Reports

Buyer's Guide

Melissa Data Quality

August 2025

Download Melissa Data Quality product report

Buyer's Guide

SAS Data Management

July 2025

Download SAS Data Management product report

Also Known As

No data available

SAS Data Management Platform, Data Management Platform, DataFlux

Overview

Data Quality Components for SSIS

This suite of data transformations for Microsoft SQL Server Integration Services (SSIS) delivers the full spectrum of data quality including data profiling, data verification, data enrichment and data matching. With an intuitive interface and drag/drop capabilities, this powerful toolkit makes it easy to unify data into a single version of the truth for Master Data Management (MDM) success.

Melissa

Every decision, every business move, every successful customer interaction - they all come down to high-quality, well-integrated data. If you don't have it, you don't win. SAS Data Management is an industry-leading solution built on a data quality platform that helps you improve, integrate and govern your data.

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

July 2025

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