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Experian Data Quality vs Melissa Data Quality comparison

Experian and Melissa are both solutions in the Data Quality category. Experian is ranked #17, while Melissa is ranked #8 with an average rating of 9.0. Experian holds a 2.7% mindshare in DQ, compared to Melissa’s 2.9% mindshare. Additionally, 87% of Experian users are willing to recommend the solution, compared to 95% of Melissa users who would recommend it.
Experian Data Quality
Read 7 Experian Data Quality reviews
  • 180 Views
  • 153 Comparison Views
87% willing to recommend
Melissa Data Quality
Read 40 Melissa Data Quality reviews
  • 530 Views
  • 292 Comparison Views
95% willing to recommend
 

Comparison Buyer's Guide

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Executive Summary
We performed a comparison between Experian Data Quality and Melissa Data Quality based on real PeerSpot user reviews.

Find out in this report how the two Data Quality solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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To learn more, read our detailed Experian Data Quality vs. Melissa Data Quality Report (Updated: October 2024).
Buyer's Guide
Experian Data Quality vs. Melissa Data Quality
October 2024
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Categories and Ranking

Experian Data Quality
Ranking in Data Quality
17th
Ranking in Data Scrubbing Software
2nd
Average Rating
8.2
Number of Reviews
7
Ranking in other categories
No ranking in other categories
Melissa Data Quality
Ranking in Data Quality
8th
Ranking in Data Scrubbing Software
4th
Average Rating
8.4
Number of Reviews
40
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Data Quality category, the mindshare of Experian Data Quality is 2.7%, down from 2.8% compared to the previous year. The mindshare of Melissa Data Quality is 2.9%, down from 3.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Quality
 

Featured Reviews

it_user187320 - PeerSpot reviewer
Sep 6, 2016
Fast in taking unstructured data, processing it and spitting out all the different data types. The team moved to SSIS/SSRS, I suspect it didn’t fit in with the goal of creating a data warehouse.
The manual calculations and formulae. They were a bit complex. The formulae were a bit abstract. Not easy to understand. Not intuitive. I sat beside an SSIS guru and he took one look at them and said “Good luck Geoff”. I coded them all and after I left, I got a call from a techy there asking me what they were all about! He hadn’t a clue how to unravel them, even with documentation. Also, they managed to accidentally delete them all. No idea how they did that. After a few panic-filled phone calls, they dropped the whole thing. It was a mess there. Glad I left.
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GM
Feb 21, 2024
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:
 

Pricing and Cost Advice

Information not available
"Buy a lot more credits than you think you’re going to need."
"It's affordable."
"Generally, the cost is ROI positive, depending on your shipping volume."
"I think it's worth the value for me to run it."
"Cloud version is very cheap. On-premise version is expensive."
"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."
"​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.​"
"Be sure to determine how the data is priced (record-based versus credit-based or some hybrid of data and services)."
More Melissa Data Quality pricing and cost advice
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Energy/Utilities Company
14%
Computer Software Company
13%
Manufacturing Company
8%
Manufacturing Company
13%
Financial Services Firm
11%
Insurance Company
10%
Computer Software Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Comparisons

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Kroll-Software FuzzyDupes vs Experian Data Quality
Compared 92% of the time
Talend Data Quality vs Experian Data Quality Logo
Talend Data Quality vs Experian Data Quality
Compared 4% of the time
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Informatica Intelligent Data Management Cloud (IDMC) vs Melissa Data Quality Logo
Informatica Intelligent Data Management Cloud (IDMC) vs Melissa Data Quality
Compared 67% of the time
More Melissa Data Quality Competitors
 

Product Reports

Buyer's Guide
Data Quality
October 2024
Experian Data Quality reportDownload Experian Data Quality product report
Buyer's Guide
Melissa Data Quality
October 2024
Melissa Data Quality reportDownload Melissa Data Quality product report
 

Also Known As

QAS-Experian Data Quality, Experian Pandora, Intelligent Search Technology Data Quality
No data available
 

Learn More

Experian
Melissa
 

Overview

Experian Data Quality (previously QAS) contact data management tools help you capture correct address, email, and phone data as it comes in, Our data management platform, Experian Pandora, is an end to end platform that supports all your Data Management needs - Data Quality, Data Migration, Data Governance, Data Preparation and Single Customer View.

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.

 

Sample Customers

Overstock.com, Cabela, Drugstore.com, Saks Fifth Avenue, Midmark, Umpqua Bank, Colorado Department of Labor & Employment, Fresno Pacific University, University of North Texas, ALDO
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
Experian Data Quality vs. Melissa Data Quality
October 2024
Free Report: Experian Data Quality vs. Melissa Data Quality
Find out what your peers are saying about Experian Data Quality vs. Melissa Data Quality and other solutions. Updated: October 2024.
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We monitor all Data Quality reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.