Melissa Data Quality vs SAP Information Steward comparison

Why PeerSpot?

Melissa and SAP are both solutions in the Data Quality category. Melissa is ranked #11, while SAP is ranked #19. Melissa holds a 3.9% mindshare in DQ, compared to SAP’s 2.8% mindshare. Additionally, 95% of Melissa users are willing to recommend the solution, compared to 100% of SAP 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

SAP Information Steward

Read 9 SAP Information Steward reviews

2,190 Views
1,467 Comparison Views

100% willing to recommend

Melissa Data Quality

SAP Information Steward

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 6, 2025

Melissa Data Quality and SAP Information Steward are data management solutions. Melissa Data Quality is favorable in pricing and support, while SAP Information Steward is preferred for its comprehensive feature set.

Features: Melissa Data Quality provides effective data verification, address management, and contact correction tools. SAP Information Steward offers data profiling, metadata management, and integration with SAP products.

Room for Improvement: Melissa Data Quality could enhance integration flexibility, expand cloud capabilities, and improve user interfaces. SAP Information Steward might simplify its deployment, enhance support for non-SAP systems, and refine its metadata management tools.

Ease of Deployment and Customer Service: Melissa Data Quality offers easy deployment and attentive customer service. SAP Information Steward has a complex deployment due to broad integration but offers extensive support resources.

Pricing and ROI: Melissa Data Quality is cost-effective with a quick ROI for basic functions. SAP Information Steward demands a higher investment but offers substantial ROI through its extensive functionality and integration.

To learn more, read our detailed Melissa Data Quality vs. SAP Information Steward Report (Updated: September 2026).

Buyer's Guide

Melissa Data Quality vs. SAP Information Steward

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)

SAP Information Steward

Ranking in Data Quality

19th

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Metadata Management (12th)

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 SAP Information Steward is 2.8%, up from 2.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Mindshare Distribution
Product	Mindshare (%)
Melissa Data Quality	3.9%
SAP Information Steward	2.8%
Other	93.3%

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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FranciscoSantos

Director at Pixel Studio PTY

Provides accurate data that is validated against a personalized reference tool

For most SAP customers, Information Steward is enough because it is able to build quality data rules to detect issues in the source systems like SAP HANA, Business Warehouse, or other systems. A business user can first organize their data into several data domains. For example, procurement, human resources, and logistics setup. The domains can build data quality dimensions where you can describe the kind of rule that you are going to use. The user then can immediately see if something is wrong with their data using traffic lights. Another great feature of SAP Information Steward is the accuracy that the content is followed by validating against the reference tool. With the solution, you are creating data quality dimensions. Within these dimensions, you are creating business data quality rules that are looking for specific fields. From these rules, you can create a scorecard. The scorecard will highlight the percentage of good data and ensure the user can feel confident that the data is accurate within predetermined limits. SAP tables have field names that are very cryptic, making them hard to understand the meaning of the fields. Metapedia helps describe these fields in business terms.

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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

"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."

"Extremely easy to install and setup."

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

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

"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."

"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."

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

"SSIS integration."

More Melissa Data Quality pros

"Setup is straightforward."

"The solution is very fast, very stable, and very easy to use and straightforward."

"The ability to analyze the data even before we start the transformation of it, and generating the user-friendly interface, giving analytical reports, and helping create the transformation rules before we proceed with the data migration part was the most helpful part of the solution for our company."

"The Data Cleansing and the scorecard dashboard are very valuable. Additionally, the financial aspect of SAP Information Steward is very good. When a rule is incorrect then it will show how much is it costing the business. These features are very valuable."

"Initial setup was straightforward."

"Data insight is the most valuable feature."

"The scorecard will highlight the percentage of good data and ensure the user can feel confident that the data is accurate within predetermined limits."

"Ability to collect information, monitor user access and to plan storage capacity."

More SAP Information Steward pros

Cons

"The custom software solution we still use in-house makes Excel a lot slower than usual."

"More countries should be supported by Melissa."

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

"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."

"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."

"Need to POC point of entry validation."

"Address validation and parsing in a few countries have room for improvement."

"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.""

More Melissa Data Quality cons

"Granularity could be reduced from an application level to the object level."

"In some cases they have given extraneous or erroneous information, which is completely useless."

"The solution could improve by incorporating other applications, such as Power BI to show more visualization. More interaction with other solutions would be a good benefit."

"Needs to be more powerful on rules."

"The user experience of metapedia could be improved."

"Performance could be improved."

"SAP Information Steward is an expensive solution compared to others."

"The support team is not very responsive."

More SAP Information Steward cons

Pricing and Cost Advice

"Pricing is very reasonable."

"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."

"Cloud version is very cheap. On-premise version is expensive."

"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."

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

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

"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."

"It is affordable."

More Melissa Data Quality pricing and cost advice

"A bit pricey, and better tools are available for a lower price."

"I do not know if there were additional costs beyond the standard licensing fees."

"SAP Information Steward is an expensive solution compared to others."

"Smaller-sized organizations may not be able to invest in SAP Information Steward because of the cost."

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Top Industries

By visitors reading reviews

Construction Company

18%

Outsourcing Company

15%

Comms Service Provider

10%

Insurance Company

Manufacturing Company

16%

Government

11%

Comms Service Provider

Outsourcing 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	1
Large Enterprise	7

Comparisons

Qlik Talend Cloud vs Melissa Data Quality

Compared 13% 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

Ataccama ONE Platform vs Melissa Data Quality

Compared 4% of the time

More Melissa Data Quality Competitors

Informatica Intelligent Data Management Cloud (IDMC) vs SAP Information Steward

Compared 12% of the time

SAP PowerDesigner vs SAP Information Steward

Compared 12% of the time

SAP Data Quality Management vs SAP Information Steward

Compared 9% of the time

Oracle Enterprise Data Quality (EDQ) vs SAP Information Steward

Compared 8% of the time

Syniti Information Management vs SAP Information Steward

Compared 7% of the time

More SAP Information Steward Competitors

Product Reports

Buyer's Guide

Melissa Data Quality

September 2026

Download Melissa Data Quality product report

Buyer's Guide

SAP Information Steward

August 2026

Download SAP Information Steward product report

Also Known As

No data available

Information Steward, SAP Data Insight

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

SAP Information Steward offers data quality insights, metadata management, and data validation scorecards, ensuring accurate data validation through scorecards and dashboards, making it user-friendly and efficient for businesses seeking clarity and effective data profiling.

SAP Information Steward provides a comprehensive approach to managing data quality and governance. It is designed to simplify deployment and streamline data profiling and cleansing with ease. Businesses leverage its capabilities to create data quality rules and detect issues in source systems, enhancing business clarity and accurate data validation. Centralizing cloud data and offering business-friendly metadata descriptions with Metapedia, it supports better metadata management and data profiling. However, it requires improvements in data export capabilities, integration for data manipulation, data filtering features, and enhanced support responsiveness.

What are the key features of SAP Information Steward?

Data Quality Insight: Provides tools for insightful data quality assessments.
Metadata Management: Allows centralized management of metadata through Metapedia.
Scorecard for Data Validation: Offers intuitive scorecards and dashboards for validating data accuracy.
Data Profiling and Cleansing: Facilitates effective profiling and cleansing processes.
Cloud Data Centralization: Centralizes cloud data for effective management.

What benefits and ROI should users look for?

Ease of Deployment: Simplifies the integration into existing systems ensuring swift usage.
User-friendly Interface: Enhances usability for efficient operations.
Accurate Data Validation: Improves the accuracy of business data.
Comprehensive Metadata Descriptions: Offers detailed business-friendly descriptions.
Integrated Data Profiling: Enhances the quality and accuracy of data management.

Industries implement SAP Information Steward widely, notably in global enterprises for S/4HANA business processes and historical reporting, prioritizing data profiling, data quality assessments, and business rules for managing customer information and transformations. Its Metapedia component supports data governance initiatives within these businesses.

SAP

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.

American Water, Graphic Packaging International, OSRAM Licht AG, Maxim Integrated

Buyer's Guide

Melissa Data Quality vs. SAP Information Steward

September 2026

Free Report: Melissa Data Quality vs. SAP Information Steward

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