Melissa Data Quality vs Qlik Talend Cloud comparison

Melissa and Qlik are both solutions in the Data Quality category. Melissa is ranked #5 with an average rating of 9.0, while Qlik is ranked #3 with an average rating of 8.0. Melissa holds a 3.4% mindshare in DQ, compared to Qlik’s 6.6% mindshare. Additionally, 95% of Melissa users are willing to recommend the solution, compared to 88% of Qlik users who would recommend it.

Melissa Data Quality

Read 40 Melissa Data Quality reviews

1,022 Views
623 Comparison Views

95% willing to recommend

Qlik Talend Cloud

Read 53 Qlik Talend Cloud reviews

6,400 Views
1,344 Comparison Views

88% willing to recommend

Melissa Data Quality

Qlik Talend Cloud

Comparison Buyer's Guide

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Executive SummaryUpdated on Nov 18, 2025

Qlik Talend Cloud and Melissa Data Quality compete in the data management category, each offering distinct features that cater to specific business needs. Qlik Talend Cloud holds an edge in terms of pricing and support, while Melissa Data Quality is preferred for its powerful features despite higher costs.

Features: Qlik Talend Cloud offers integration capabilities, data transformation tools, and a robust set of components for handling various workflows. In contrast, Melissa Data Quality provides advanced data cleansing, address verification, and geocoding features, focusing on data integrity and accuracy.

Room for Improvement: Qlik Talend Cloud could benefit from enhanced data accuracy tools, better address verification features, and improved cleansing options. Melissa Data Quality might improve its cost structure, provide more competitive pricing, and offer enhanced integration capabilities to broaden its appeal.

Ease of Deployment and Customer Service: Qlik Talend Cloud supports multiple environments, allowing easy integration into existing systems, with reliable customer service. Melissa Data Quality ensures straightforward deployment with specialized support for complex data issues, benefiting those needing in-depth assistance.

Pricing and ROI: Qlik Talend Cloud is more budget-friendly, appealing to businesses focused on ROI by offering effective data manipulation at a lower cost. Melissa Data Quality requires a higher initial investment but provides substantial ROI through precise data enrichment features, justifying its premium price with long-term value.

To learn more, read our detailed Melissa Data Quality vs. Qlik Talend Cloud Report (Updated: December 2025).

Buyer's Guide

Melissa Data Quality vs. Qlik Talend Cloud

December 2025

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Helped 879,425 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

5th

Ranking in Data Scrubbing Software

5th

Average Rating

8.4

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Qlik Talend Cloud

Ranking in Data Quality

3rd

Ranking in Data Scrubbing Software

1st

Average Rating

8.2

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Integration (9th), Master Data Management (MDM) Software (3rd), Cloud Data Integration (7th), Data Governance (8th), Cloud Master Data Management (MDM) (4th), Streaming Analytics (9th), Integration Platform as a Service (iPaaS) (9th)

Mindshare comparison

As of January 2026, in the Data Quality category, the mindshare of Melissa Data Quality is 3.4%, up from 2.5% compared to the previous year. The mindshare of Qlik Talend Cloud is 6.6%, down from 11.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Market Share Distribution
Product	Market Share (%)
Qlik Talend Cloud	6.6%
Melissa Data Quality	3.4%
Other	90.0%

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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Herivao Jus Winny

IT Consultant at a tech services company with 201-500 employees

Has automated recurring data flows and improved accuracy in reporting

The best features of Talend Data Integration are its rich set of components that let you connect to almost any data design intuitive and its strong automation and scheduling capabilities. The TMap component is especially valuable because it allows flexible transformation, joins, and filtering in a single place. I also rely a lot on context variables to manage different environments like Dev, Test, and production, without changing the code. The error handling and logging tools are very helpful for monitoring and troubleshooting, which makes the workflow more reliable. Talend Data Integration has helped our company by automating and standardizing data processes. Before, many of these tasks were done manually, which took more time and often led to errors. With Talend Data Integration, we built automated pipelines that extract, clean, and load data consistently. This not only saves hours of manual effort, but also improves the accuracy and reliability of data. As a result, business teams had faster access to trustworthy information for reporting and decision making, which directly improved efficiency and productivity. Talend Data Integration has had a measurable impact on our organization. By automating daily data loading processes, we reduced manual effort by around three or four hours per day, which saved roughly 60 to 80 hours per month. We also improved data accuracy. Error rates dropped by more than 70% because validation rules were built into the jobs. In addition, reporting teams now receive fresh data at least 50% faster, which means they can make decisions earlier and with more confidence. Overall, Talend Data Integration has increased both efficiency and reliability in our data workflows.

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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."

"Provides simplicity, ease of use, combined with overall accuracy of data."

"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."

"Address verification ensures our customers get their packages, and we aren’t charged for incomplete address information."

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

"We use a Melissa API to access the data, so it easy to use, accurate, and fast."

"The high value in this tool is its relatively low cost, ease of use, tight integration with SSIS, superior performance (compared to competitors), and attribute-level advanced survivor-ship logic."

"Our customer database is now significantly more accurate and reliable."

More Melissa Data Quality pros

"Talend Data Integration has had a measurable impact on our organization by automating daily data loading processes, reducing manual effort by around three or four hours per day, improving data accuracy with error rates dropping by more than 70%, and enabling reporting teams to receive fresh data at least 50% faster for earlier and more confident decision-making."

"The file fetch process is impeccable."

"I like the way that you can use the context variables, and how you can work those context variables to give you values and settings for every development environment, such as PROD, TEST, and DEV."

"We can develop our own code if we do not see the functionality we need."

"The solution is customizable."

"Talend Studio has the ability to connect to almost anything to integrate data from files, databases, web services, etc."

"The process of upgrading the software is quite easy."

"They're very competitive in terms of performance, which is a good selling point. It has very rich features. It provides a very rich feature set in the application."

More Qlik Talend Cloud pros

Cons

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

"MatchUp is a more complex product and I recommend a test area before upgrading to production. Performance can change from version to version."

"The billing structure does not seem very accurate. We’ve had issues with miscounted batch records processed"

"We have noticed that some of the emails and addresses return with confusing or incorrect codes, but for the most part, it is accurate."

"More countries should be supported by Melissa."

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

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

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

More Melissa Data Quality cons

"I think they should drive toward AI and machine learning. They could include a machine-learning algorithm for the deduplication."

"The product must enhance the data quality."

"I wonder if, at the same price, the API component could be added, which would be beneficial."

"When we upgraded to Version 6.4.1, we tried using a GIT repository instead of a SVN repository. After a few incidents where things disappeared and changes were not saved, we decided to go back to a SVN repository."

"Once you get past the basic tools, it gets pretty complicated."

"The product's setup process could be simpler."

"The documentation from version to version could be more accurate."

"I would say that some of the support elements need improvement."

More Qlik Talend Cloud cons

Pricing and Cost Advice

"The only complaint that I have towards it is they sell licenses based on a range of usage, and I feel those ranges are too large."

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

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

"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."

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

"The price for address validation is similar in all software. However, the price for geocoding decides the actual pricing. If you get their most accurate geocoding (called GeoPoints), then it will add about $10k+ per million requests."

"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."

"Pricing is very reasonable."

More Melissa Data Quality pricing and cost advice

"The price of the Talend Data Management Platform is reasonable. The other competing solutions are priced high. Gartner Magic Quadrant identified other solutions, such as Informatica, that are far more expensive."

"I have been using the open-source version."

"The product pricing is considered very good, especially compared to other data integration tools in the market."

"The price is on a per-user basis. It's a little more expensive than other tools. There aren't any additional costs beyond the standard licensing fee."

"The tool is cheap."

"Moreover, the pricing structure stands out as highly competitive compared to other offerings in the market, making it a cost-effective choice for users."

"We did not purchase a separate license for DQ. It is part of our data platform suite, and I believe it is well-priced."

"The solution's pricing is very reasonable and half the cost of Informatica."

More Qlik Talend Cloud pricing and cost advice

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Top Industries

By visitors reading reviews

Insurance Company

15%

Manufacturing Company

12%

Computer Software Company

Healthcare Company

Financial Services Firm

13%

Computer Software Company

11%

Comms Service Provider

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	20
Midsize Enterprise	11
Large Enterprise	18

Questions from the Community

Ask a question

Earn 20 points

What do you like most about Talend Data Quality?

The most valuable feature lies in the capability to assign data quality issues to different stakeholders, facilitating the tracking and resolution of defective work.

See all answers

What needs improvement with Talend Data Quality?

I don't use the automated rule management feature in Talend Data Quality that much, so I cannot provide much feedback. I may not know what Talend Data Quality can improve for data quality. I'm not ...

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What is your primary use case for Talend Data Quality?

It is for consistency, mainly; data consistency and data quality are our main use cases for the product. Data consistency is the primary purpose we use it for, as we have written rules in Talend Da...

See all answers

Comparisons

Experian Data Quality vs Melissa Data Quality

Compared 15% of the time

Coheris Spad Data Quality vs Melissa Data Quality

Compared 12% of the time

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

Compared 11% of the time

Anchor Software Data Quality vs Melissa Data Quality

Compared 11% of the time

Eprentise Data Quality vs Melissa Data Quality

Compared 7% of the time

More Melissa Data Quality Competitors

Informatica Intelligent Data Management Cloud (IDMC) vs Qlik Talend Cloud

Compared 10% of the time

Collibra Platform vs Qlik Talend Cloud

Compared 4% of the time

SSIS vs Qlik Talend Cloud

Compared 3% of the time

SAP Data Services vs Qlik Talend Cloud

Compared 3% of the time

Microsoft Purview Data Governance vs Qlik Talend Cloud

Compared 3% of the time

More Qlik Talend Cloud Competitors

Product Reports

Buyer's Guide

Melissa Data Quality

December 2025

Download Melissa Data Quality product report

Buyer's Guide

Qlik Talend Cloud

December 2025

Download Qlik Talend Cloud product report

Also Known As

No data available

Talend Data Quality, Talend Data Management Platform, Talend MDM Platform, Talend Data Streams, Talend Data Integration, Talend Data Integrity and Data Governance

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

Qlik Talend Cloud provides robust data integration tools tailored for efficient management of large volumes, offering real-time data access, Java integration, and custom code capabilities for developers.

Qlik Talend Cloud is known for its extensive connectivity options, enabling seamless integration across different platforms, such as S3, Redshift, Oracle, and SQL Server. The central repository facilitates consistent metadata access throughout organizations, enhancing collaboration. Despite its strengths in advanced monitoring, automation, and user-friendly drag-and-drop interfaces, users face challenges with installation stability, technical support, documentation inconsistencies, and complexities in learning. Performance concerns also include multitasking limitations and excessive memory usage. The platform's licensing costs can be prohibitive for smaller companies, while demands for improved data governance and intuitive code management continue. Its applications in healthcare data parsing, ETL task automation, and diverse data platform integration demonstrate its utility, although there's a constant demand for better scalability and efficient transformations.

What are the key features?

Seamless Real-Time Data Access: Ensures that users can access the latest information across systems without delays for better decision-making.
Java Integration: Facilitates smooth incorporation of Java components into workflows, broadening development options.
Custom Code Development: Offers flexibility to develop tailored solutions to meet specific integration scenarios.
Vast Connectors: Enhances connectivity between multiple platforms, supporting comprehensive data integration strategies.
Central Repository: Allows for uniform metadata access across departments, promoting cohesive data management practices.
Automation and Monitoring: Streamlines workflows with advanced automation and monitoring, reducing manual intervention and errors.

What benefits and ROI should users evaluate?

Improved Data Quality: Tools for data matching and error handling ensure high-quality, reliable data for analytics and reporting.
Efficient Workflow Streamlining: Drag-and-drop interfaces and automation features simplify complex workflows, enhancing operational efficiency.
Enhanced Business Insights: By integrating diverse data sources, businesses can derive deeper insights and ensure compliance through better data management.
Cost Management: Although some users find licensing expensive, comprehensive feature sets aimed at improving data processes can justify costs.
Community Support: Users benefit from active community forums that provide assistance, although technical support also necessitates enhancements.

In specialized industries like healthcare, users leverage Qlik Talend Cloud for data integration and transformation, aiding in compliance and analytics. Compatibility with cloud and on-premises systems ensures adaptability to complex data tasks, facilitating business application development. Organizations focus on enhanced data ingestion and quality checks for comprehensive solutions.

Qlik

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.

Aliaxis, Electrocomponents, M¾NCHENER VEREIN, The Sunset Group

Buyer's Guide

Melissa Data Quality vs. Qlik Talend Cloud

December 2025

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