IBM Infosphere Information Analyzer vs Melissa Data Quality comparison

IBM and Melissa are both solutions in the Data Quality category. IBM is ranked #7 with an average rating of 10.0, while Melissa is ranked #8 with an average rating of 9.0. IBM holds a 2.5% mindshare in DQ, compared to Melissa’s 2.9% mindshare. Additionally, 66% of IBM users are willing to recommend the solution, compared to 95% of Melissa users who would recommend it.

IBM Infosphere Information ...

Read 6 IBM Infosphere Information Analyzer reviews

418 Views
393 Comparison Views

66% willing to recommend

Melissa Data Quality

Read 40 Melissa Data Quality reviews

750 Views
405 Comparison Views

95% willing to recommend

IBM Infosphere Information ...

Melissa Data Quality

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Executive SummaryUpdated on Jan 6, 2025

IBM Infosphere Information Analyzer and Melissa Data Quality are competing in the data quality management sector. Melissa Data Quality appears to have the upper hand due to its user-centric features and flexible integration.

Features: IBM Infosphere offers advanced data profiling, comprehensive analysis tools, and deep analytical capabilities. Melissa provides user-friendly data validation, cost-effective cleansing, and seamless integration with SSIS.

Room for Improvement: IBM Infosphere could benefit from an easier deployment process, more responsive support, and better cost-effectiveness. Melissa could enhance its analytical depth, offer more complex profiling features, and improve its integration with non-Microsoft environments.

Ease of Deployment and Customer Service: IBM Infosphere requires significant IT involvement for complex deployments, whereas Melissa enables a streamlined setup with limited technical resources. Melissa also offers more responsive customer service.

Pricing and ROI: IBM Infosphere has higher initial costs with potentially longer ROI timelines. Melissa offers competitive pricing and faster ROI, aided by quick deployment and user-friendly features.

To learn more, read our detailed IBM Infosphere Information Analyzer vs. Melissa Data Quality Report (Updated: September 2025).

Buyer's Guide

IBM Infosphere Information Analyzer vs. Melissa Data Quality

September 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

IBM Infosphere Information ...

Ranking in Data Quality

7th

Average Rating

7.6

Reviews Sentiment

5.8

Number of Reviews

Ranking in other categories

No ranking in other categories

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 (5th)

Mindshare comparison

As of October 2025, in the Data Quality category, the mindshare of IBM Infosphere Information Analyzer is 2.5%, up from 2.2% compared to the previous year. The mindshare of Melissa Data Quality is 2.9%, up from 2.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Market Share Distribution
Product	Market Share (%)
IBM Infosphere Information Analyzer	2.5%
Melissa Data Quality	2.9%
Other	94.6%

Data Quality

Featured Reviews

Tirthankar Roy Chowdhury

Teamlead at Tata consultancy services

Accessible from anywhere and easy to learn even for non-technical users, but needs more connectors and faster issue resolution from technical support

What could be improved or added to IBM Infosphere Information Analyzer is more connectors. This solution comes in a package with IBM InfoSphere DataStage and is missing a lot of connectors to various, new data sources, so IBM needs to work on that area. Compared with competitors such as Informatica and Alation which acquired other small companies to work on the connectors, IBM has not done any testing and has tried to develop the connectors in-house, but that's taking a lot of time. As a result, my company is unable to connect to a lot of data sources, particularly modern data sources.

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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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Quotes from Members

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

Pros

"You can also schedule and run data quality on the critical data elements on the databases."

"What's most useful in IBM Infosphere Information Analyzer is you can access it from anywhere. It's also pretty easy to learn, so even non-technical business people use it and found the solution easy to learn."

"We only use the one feature for the NAICS code. This allows our product users to know what industry a business is in."

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

"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."

"Enables us to send out bulk mailings when we need to verify NCOA."

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

"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."

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

"Standardizing allows me to more effectively check for duplicate/existing records. Verifying increases the value of the data."

More Melissa Data Quality pros

Cons

"What could be improved or added to IBM Infosphere Information Analyzer is more connectors. This solution comes in a package with IBM InfoSphere DataStage and is missing a lot of connectors to various, new data sources, so IBM needs to work on that area. Compared with competitors such as Informatica and Alation which acquired other small companies to work on the connectors, IBM has not done any testing and has tried to develop the connectors in-house, but that's taking a lot of time. As a result, my company is unable to connect to a lot of data sources, particularly modern data sources."

"The solution is outdated and is not on cloud."

"Many issues, sometimes I have to completely log out and start over."

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

"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 SSIS component setup seems a little klunky."

"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."

"More countries should be supported by Melissa."

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

"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

Pricing and Cost Advice

"For the licensing cost of IBM Infosphere Information Analyzer, I have no information on the exact cost, but as it's bundled with other IBM products such as IBM InfoSphere Information Governance Catalog and IBM InfoSphere DataStage, the bundle is expensive when compared to competitor pricing."

"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."

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

"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."

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

"Buy a lot more credits than you think you’re going to need."

"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."

"Melissa pricing is competitive."

"Pricing is very reasonable."

More Melissa Data Quality pricing and cost advice

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Top Industries

By visitors reading reviews

No data available

Insurance Company

14%

Manufacturing Company

14%

Healthcare Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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

Comparisons

Informatica Intelligent Data Management Cloud (IDMC) vs IBM Infosphere Information Analyzer

Compared 36% of the time

SAP Data Services vs IBM Infosphere Information Analyzer

Compared 33% of the time

Ataccama ONE Platform vs IBM Infosphere Information Analyzer

Compared 31% of the time

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Experian Data Quality vs Melissa Data Quality

Compared 17% of the time

Coheris Spad Data Quality vs Melissa Data Quality

Compared 16% of the time

Anchor Software Data Quality vs Melissa Data Quality

Compared 16% of the time

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

Compared 13% of the time

Eprentise Data Quality vs Melissa Data Quality

Compared 10% of the time

More Melissa Data Quality Competitors

Product Reports

Buyer's Guide

Data Quality

October 2025

IBM Infosphere Information Analyzer report

Download IBM Infosphere Information Analyzer product report

Buyer's Guide

Melissa Data Quality

September 2025

Download Melissa Data Quality product report

Also Known As

Information Analyzer

No data available

Overview

IBM Information Analyzer provides data quality assessment, data quality monitoring and data rule design and analysis capabilities. This software helps you derive more meaning from your enterprise data, reduces the risk of proliferating incorrect information, facilitates the delivery of trusted content, and helps to lower data integration costs.

IBM

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

Sample Customers

Integra LifeSciences, Longjiang Bank Corp., Webster University, Swedish Armed Forces, Edith Cowan University, Premier, PRISA Digital, TIAA CREF

Boeing Co., FedEx, Ford Motor Co, Hewlett Packard, Meade-Johnson, Microsoft, Panasonic, Proctor & Gamble, SAAB Cars USA, Sony, Walt Disney, Weight Watchers, and Intel.

Buyer's Guide

IBM Infosphere Information Analyzer vs. Melissa Data Quality

September 2025

Free Report: IBM Infosphere Information Analyzer vs. Melissa Data Quality

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