Melissa Data Quality vs SAS Data Management comparison

Melissa and SAS are both solutions in the Data Quality category. Melissa is ranked #8, while SAS is ranked #7 with an average rating of 9.0. Melissa holds a 4.3% mindshare in DQ, compared to SAS’s 3.4% 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

2,316 Views
1,575 Comparison Views

95% willing to recommend

SAS Data Management

Read 18 SAS Data Management reviews

3,799 Views
874 Comparison Views

88% willing to recommend

Melissa Data Quality

SAS Data Management

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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: April 2026).

Buyer's Guide

Melissa Data Quality vs. SAS Data Management

April 2026

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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 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

7th

Average Rating

8.6

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Integration (28th), Data Governance (23rd)

Mindshare comparison

As of May 2026, in the Data Quality category, the mindshare of Melissa Data Quality is 4.3%, up from 2.8% compared to the previous year. The mindshare of SAS Data Management is 3.4%, up from 2.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Mindshare Distribution
Product	Mindshare (%)
SAS Data Management	3.4%
Melissa Data Quality	4.3%
Other	92.3%

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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Fernanda Knopki

Data Scientist & Scrum Master at Volvo Group

Has supported centralized data access and improved governance through unified administration

The best features I appreciate about SAS Data Management tool are that it's easy to create the flows and schedule data, and the tables are not too big, making it easy to control the ETL process, including user access which is also easy to manage in SAS. The data integration tools with SAS Data Management were quite good because they allowed me to do all lineage, to manage and see all the variables, find the libraries where the data is, and understand the data in just one tool. Offering another development environment combining code and predefined objects, where I could just drag and drop to achieve what I needed, although some objects were not too good, but some were effective. The metadata management feature of SAS Data Management helps a lot; creating your data marts or data lake with good naming conventions, library conventions, and so on is very important because it allows easy queries to find the whole structure, though I think metadata governance also depends on first definitions, not only on the tool. SAS Data Management is very good for data governance; for me, it was the best tool I worked with in data management, although I don't have too much experience with others, and it truly helped a lot.

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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

"Allows us to delete and correct incorrect data to make the searching of our applicant tracking system more consistent and relevant."

"We are able to more accurately identify valid, and better formatted, data which improves the data we store in our database."

"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."

"I believe the Melissa Data products are very good."

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

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

"SSIS integration."

"Allows us to identify cell phones before dialing, and giving us data about callers."

More Melissa Data Quality pros

"This is an established product with powerful data analysis and varied options for user entry points."

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

"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."

"DataFlux in my opinion is an excellent high end solution for enterprise wide data management."

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

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

"The ROI can be very high in terms of improving the reliability and accuracy of data used in forecasting and marketing models (ex: customer data, sales data)."

"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."

More SAS Data Management pros

Cons

"The billing structure does not seem very accurate."

"The use case I'm familiar with is for a merchant who was contrasting this technology with data from Dun and Bradstreet: so my recommendation is that Melissa Data purchase Dun and Bradstreet to combine their data breadth."

"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 SSIS component setup seems a little klunky, but otherwise the product does what I expect."

"Needs more and better search tools. Also, state and local tax data would be nice."

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

"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."

"Needs to validate more addresses accurately."

More Melissa Data Quality cons

"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."

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

"With SAS Data Management, you have to purchase an external driver, configure all of the tables for all of the data that you will extract from Salesforce. It's not a straightforward process."

"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."

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

"The stability could be a bit better. It's lacking in this area right now."

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

More SAS Data Management cons

Pricing and Cost Advice

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

"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."

"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."

"It's affordable."

"It is affordable."

"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."

"They were willing to work with our preferred vendors, though it involved extra steps to get the license."

"Pricing is very reasonable, no licensing required."

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."

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

"The solution is expensive."

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Top Industries

By visitors reading reviews

Insurance Company

11%

Construction Company

10%

Healthcare Company

Comms Service Provider

Financial Services Firm

20%

Healthcare Company

Government

Manufacturing 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	7
Midsize Enterprise	1
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?

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

See all answers

What is your primary use case for SAS Data Management?

The main idea for using SAS Data Management was to bring all data inside SAS. I brought the finance area and the sales area in the company that I have worked, along with some market planning data, ...

See all answers

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

ibi Data Quality vs Melissa Data Quality

Compared 3% of the time

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Informatica PowerCenter vs SAS Data Management

Compared 10% of the time

WhereScape RED vs SAS Data Management

Compared 9% of the time

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

Compared 8% of the time

Collibra Platform vs SAS Data Management

Compared 5% of the time

More SAS Data Management Competitors

Product Reports

Buyer's Guide

Melissa Data Quality

April 2026

Download Melissa Data Quality product report

Buyer's Guide

SAS Data Management

April 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

April 2026

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